GPT-5.6
openai.com
openai.com
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.
> Original image detail: GPT-5.6 preserves the original dimensions of images sent with original or auto detail instead of resizing them to a patch budget or pixel-dimension limit.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
> Control warmth: GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic.
That part is confusing because it's not like they provide an example of how default GPT-5.6 output compares with GPT-5.5 both with default output and prompted for brevity. Whenever I use such prompts, it's usually because I want the model to give me the gist in a few sentences. I'd be stunned if GPT-5.6 was that concise by default. I would think that could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. What if you were expecting GPT to be as wordy as it usually is? Then suddenly your output is not wordy enough?
Smells like OpenAI trying its best to stave off financial armageddon for another few months. Then again, I'm not sure why they chose to waste so much output computation on verbal diarrhea all this time up to now.
Here's the example they give:
> Instead of asking for the shortest possible answer, replace brevity instructions with prioritization:
> Lead with the conclusion. Include the evidence needed to support it, any material caveat, and the next action. Omit secondary detail and repetition.
> Keep all required facts, decisions, caveats, and next steps. Trim introductions, repetition, generic reassurance, and optional background first.
Generally speaking, when I ask for a short answer, I want a short answer because I'm not really willing to read through a bunch of bullshit to get to a summary. Putting the onus back on me to assume what the model will return and write a longer prompt detailing exactly what information I want completely misses the point of why I'm asking for a short answer in the first place.
Human can no longer be concise when asking for a few sentences instead of 20 paragraphs of BS they don't want to read when all they want is a summary to verify the general direction of the prompt-work before digging into the details.
such progress!
We will probably just get reader-side affordances for this like auto-folded justification and introduction sections and so on.
Doubtless some chat interface will add this the way they’ve added reasoning folding.
Pray they do not realign them further.
There are times I require single word answers. I will use whatever model responds as I desire and at this point those models are just a few.
Because that’s what’s in the training set. Reticent humans don’t have blogs.
I would presume (perhaps falsely?) that an instruction like this would lead to the model presenting a conclusion not supported by the evidence, and potentially backtracking as it then tries to justify said conclusion.
Yes, if deliberation happens, the model should figure out what it wants to say during that phase; but if you're using auto mode, the model is not going to be doing any deliberating half the time. In those cases, the output blathering is the model's only chance for deliberation. It "thinks as it talks", per se.
Given that, I would advise a different approach: let it blather, but then get it to write you a conclusion at the end that the model can guarantee will obviate the need to read any of the blathering.
I.e. advise the model to add an "executive summary" to the end of any non-trivial-in-length response. With some wording to carefully navigate the model between "the summary is itself too long" vs "the summary acts more like clickbait, leaving out necessary detail such that it requires actually reading the blather."
Not sure exactly what that wording would look like. I imagine something like "write your postscript executive summary as if you were a senior CIA intelligence analyst summarizing ground-level reports into a daily digest for the Joint Chiefs of Staff. Take up as little of their time as possible, but ensure that any detail critical to decision-making is retained." (But that phrasing might only be useful if the model is delivering a certain type of response, and actively counter-productive otherwise. This kind of thing is delicate.)
On high-challenge turns, the auto mode routes to the "thinking" model. But on low-challenge turns, it routes to the "instant" model.
And the "instant" model, by design, has no capacity for deliberation. (If it did, it couldn't guarantee that its responses would begin streaming "instantly.")
The model will still have read the entirety of the document before composing its response. And I believe that even in auto mode, there are thinking tokens behind the scenes.
But, unless your desired output is literally a document for others to read, at the point where you're having a model generate a full, lengthy output multiple times over with revisions, you may as well just turn off auto mode and have it always deliberate (i.e. choose the thinking model explicitly from the model selector.) Then it'll be as messy as it needs to be while deliberating, but give you exactly what you want as output.
(And if your desired output is literally a document for others to read, that you want to interactively draft and polish, then (in the case of ChatGPT specifically) you should not only be explicitly forcing the "thinking" model, but also should be asking it to activate the "canvas" feature from the start. My understanding is that revising a canvas document involves the model emitting something like editing gestures, rather than simply re-streaming the updated chunks of text. This saves a lot of output tokens on large documents.)
This is the actual reason why openai _invented_ reasoning models, to give them time/space to work out a solution, rather than having to magic a correct solution out of thin air from token 1.
It's less important now that all models do reasoning, but it's still almost always better to make the output come out last rather than first.
> Trim introductions, repetition, generic reassurance, and optional background first.
It's not possible for the model to "trim" those before they've been output, so this is akin to telling it "not think of an elephant or even take the existence of elephants into consideration while solving this problem".
No reasonable model has worked that way for years.
Extraordinary claims require extraordinary proof. So far the only other people I've seen teach this prompting style or talk about models "correcting their own output" were getting their information from AI-generated, hallucinated LinkedIn and TikTok posts.
If this thing exists - which is not just a LLM outputting content serially, placed inside a harness where itself (or another llm) is prompted to review and also output revisions serially - and if a single model can be prompted to output content and "iterate" or rewind it, and it's been widespread amongst "all reasonable models", surely there will be a flurry of sources you can point me to so I can learn.
Demanding sources for this is odd. It's literally been a headline feature of every frontier model for two years.
I guess you are "technically correct" that no model can "un-emit" tokens... but I don't think that is what anyone was saying or an interesting point to make.
Edit: see also this recent post, which details another place where revisions can happen, upstream of the reasoning token emission:
GP was saying
> That would be true of non-iterative models that just emit an output from beginning to end.
Which suggests that there are
- "iterative" models
which
- do not output "from beginning to end"
which AFAIK is science-fiction.
Since the LLM is now designed to run in a harness, it’s really not even wrong any more.
https://openai.com/index/openai-o1-system-card/
https://arxiv.org/html/2410.10630v1
https://magazine.sebastianraschka.com/p/understanding-reason...
GP was saying
> That would be true of non-iterative models that just emit an output from beginning to end.
Which suggests that there are
- "iterative" models
which
- do not output "from beginning to end"
which AFAIK is science-fiction.
Longer, more detailed or conditional prompts always introduce an additional cognitive load as it checks every token it generates against the conditions. Making instructions more absolute (like: "Never do...") can increase the duration of compliance but at the cost of creating a significant center of attentional gravity. This can cause far more output distortion as the model devotes increasing portions of its attention budget to ensure compliance with a heavyweight requirement or prohibition. Every word in a global prompt is a trade-off between attention, compliance, drift, etc.
As someone used to thinking of computers as natural deterministic rule-followers, it's weird having to carefully wordsmith and A/B test even the simplest global prompts. It feels like coaxing a hyper-literal, emotionally sensitive, spectrum-ish toddler to comply but without being so strict it gets 'upset' or spirals into hyper-focusing.
Sadly, you can't do things like this directly using ChatGPT's own "GPTs" abstraction. (For that feature to be useful, they really need some concept of server-side agents as stateful resident IO-stream-reducer actors.)
Trying to craft a workable prompt got so frustrating I eventually just tried a prompt of "Don't change anything about your normal text formatting, it's perfect as is" and even that skewed the output vs no prompt. For browser chat I finally just wrote a client-side CSS UserStyle that does the formatting. Now I even have sequentially numbered sections with indented alphabetic bullets! Zero cognitive load or attentional skew and it never drifts off the formatting in long sessions.
At least before it would listen to instructions like this.
Would it actually follow them? IME LLMs are incapable of estimating the length of their own output, the total length of the current context, etc. They just make stuff up unless they have external tools that can inspect those things for them.
This is from Ministral 3 14B, a 2025 model without reasoning, that you can run on your PC:
> Write a Haiku involving HackerNews, and the capability of large language models like you to reply in an exact number of words or syllables.
Silicon whispers,
exact words in code’s embrace—
Haiku blooms anew.
My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.
My brother in Christ this entire thread is talking about the new model that was released
You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.)
Not quite. The hosting side can change reasoning budgets (or re-assign what terms like "high" means), temperature and other decoding parameters, output length limits, finetune internal "hidden" prompt, latency optimizations, finetune attention algorithms, even change quantization - all still serving as the same model.
We know (or suspect) Anthropic frequently nerfs models while keeping their name and version the same.
You might argue that the systems we've built around them are learning in a way, as they strategically condense and save artifacts from past interactions to pass into the LLMs context. But the LLM itself, which is the source of the intelligence, is not learning. It remains entirely unchanged throughout inference. This difference may seem trite, but it has significant impacts over the long term behavior.
The LLM can immediately learn and start using new skills. To any lay person, that's what learning is.
The underlying structure and tuning of the LLM are entirely unchanged by context. It merely affects the attention and activation of the network. The LLM will not be able to work with this hypothetical new language unless it is in context. This does not fit the computational meaning of learning.
Smart is not a well defined term. Nor is it's general idea formally understood. Use it freely, but you won't be saying anything meaningful unless you define your usage.
You're making an artificial distinction. The LLM sees a new programming language for the first time, and can immediately code in it. That's learning by any reasonable definition.
If you go past the context window, it forgets, which is a limitation of current LLMs. But as long as it learned how to code in the new language within its context window, it has gained that new ability.
First, the "hype" is not externally imposed. It's the genuine reaction of hundreds of millions of people to a technology that would have been considered science fiction just four years ago. The fact that I can't be certain you whether you are an LLM or a human is incredible.
Second, the output of the LLM depends on both its weights and the context. If it has seen something in its context window, it now knows it for all intents and purposes. It learns, in other words.
"The underlying model is just a biological neutral network. It seems you carbonoids get upset when someone talks honestly about synapses and neuron firing."
It is possible to train on every prompt. ChatGPT is not currently doing that, but it is in fact possible to do.
Tuning those can definitely make a model respond better or worse.
So your claim (quoting 100% as written) that "Their performance depends solely on the model training before release and how well you curate the context you feed it" is wrong. Hence the downvotes.
Doesn't matter if LLMs are to be considered intelligent or not for the claim to be wrong.
> But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.
Often yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.
Oh give me a break. Show me one example of 1) any knob twisting that makes the underlying model better. or 2) any example of the AI providers twisting those knobs to do anything other than degrade performance for their own bottom line or safety.
The current post says: "it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount."
When no, the model cannot "get better". It doesn't determine any appropriateness of response realtime except for the weights baked into it from the beginning and whatever context it can muster. If you cram enough guidance that it doesn't decide to ignore maybe you can make it more brief. But it (the model) can do none of those things.
LLM models are literally stupid by design.
You are now anthropomorphizing the model yourself.
I mentioned several.
You're now once again changing goalpoasts to say you meant the underlying model, not the overall llm performance, even though you explicitly wrote: "Their performance depends solely on the model training before release and how well you curate the context you feed it".
So, the context curation was relevant (meaning you didn't constrain your claim to the underlying model), but now somehow all the additional tunables aren't relevant (because suddenly you're just talking about the model).
End of discussion.
End of discussion.
Wrong. The face-saving backtracking doesn't change that.
Your comments are conflating multiple kinds of “smart” and “better”. You’re right that if all the inputs are exactly the same, it takes a new model to improve (ignoring non-determinism). But the knobs and context and harness change the inputs, and they do improve output, contrary to your claim. You’re failing to capture the distinction between what the model itself does and how the harness can boost the model’s performance. It is legitimately valid and fair to call improved performance “better”, no matter where it comes from.
This all gives me the feeling you might not have experience with or understand what’s happening in today’s harness development, and the degree to which it may be as important as the weights. There are in fact a lot of things you can do to improve a model’s performance on tasks & benchmarks, without changing the model weights. @coldtea mentioned a bunch, but the harness feedback loop, internal prompts, system prompts, skills, and requests for a model to try harder, and verify and validate it’s output all lead to improved performance, all without retraining.
I agree LLMs are stupid; they’re statistical token predictors. But somehow statistical token prediction is amazing and works much better than we imagined. The talking points about LLMs being stupid token predictors are fading now because they lack explanatory power for how good the models have become. The big surprise here isn’t about LLMs. It’s about language, and how much “thinking” and intelligence is contained in language. We don’t have a good grasp on where the line is between language and intelligence. LLMs have crushed the Turing Test into dust, and yet we don’t consider them intelligent. They often appear to understand what you ask thoroughly, can re-state it in different words, they can correct your misunderstandings or add nuance you didn’t see. All this because that’s what humans do and LLMs talk like humans.
Because this entire discussion is about the release of a new model, and models are fixed. Sure you can try to modify all the scaffolding around it, but the model is the model. It doesn't matter what you're trying to improve. You can only improve the peripheral aides. And the peripheral aides can't fundamentally fix the problems with llm models when they can't learn new relationships or facts.
You will always have to wait for a new model (like this one we are talking about) for improvements to the model.
Right. The sentence you quoted was about brevity improving with a new model. It did not suggest the model itself improving.
I’m confused why you’re stuck on this tangent. And confused why you are repeating the talking points about the model being fixed. The model is fixed - that’s true, I already agreed with you. But you don’t seem to be listening to anything else.
> It doesn’t matter what you’re trying to improve.
What do you mean? If we’re trying to improve LLM output, there are multiple ways to achieve it. A new model is one of them. Changing the inputs is another.
> You will always have to wait for a new model (like this one we are talking about) for improvements to the model.
This is true! Nobody here is disagreeing with that. The part that it seems you’ve argued incorrectly is the apparent claim that output can’t get better. Output can “improve” without improving the model.
Not disagreeing with your point, but your terminology muddies your point.
But your point doesn't acknowledge that even with inference, there is a lot of room to tune the calculations. Multiple models, quantization tradeoffs are just the most obvious examples. Every architecture can be adjusted to increase intelligence/watt or other measure, even without further training.
Not just prose. I think this is part of the reason why you see ridiculous code with insane error handling and type checking even for impossible cases.
Although I was surprised that I could get very Claude like results from Chinese models though by just telling it to make the code elegant.
Reminds me of the old days with art AI where you had to put "+good -bad" in the prompt because otherwise it would assume you just wanted random quality outputs, because it had been trained on random quality inputs...
I mean, it's true that it would be ideal of this stuff did just get figured out optimally behind the API, but there is definitely an incentive on their side to burn more tokens.
How does this differ from the other changes in behavior in 5.6 that will also break things? New models always break things.
shouldnt you have good testing for that and not deploy a version update when those tests fail?
To put it another way, you will only get the benchmarked performance if you let it talk the way it talks by default. Trying to modify this neuters the model's IQ.
But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly.
You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.
RIP Caveman skill. Six month good. Now skill dead.
ftfy
There is a Yoda skill. -> A Yoda skill, there is.
What about my favorite, "no yapping"?
A shorter prompt results in half as much tokens spend? I find this very hard to believe.
Should be relatively easy to test. And if it's true, just first use a very cheap near-SOTA model to first rewrite the prompt to a similar but shorter prompt before sending it to GPT-5.6.
pi.dev for example can control other harnesses.
An example: the other day for example I didn't understand why Claude Code CLI (which I hadn't used in a while) wouldn't let me cut/paste anymore (turns out they apparently fixed some long-standing scrolling and blinking SNAFU, but this modified how mouse selection/paste worked under Xorg but I didn't immediately realized they changed this)... I had to copy/paste the oauth challenge/response for I was logged out (maybe because I hadn't used Claude Code CLI in a while, dunno). But my usual copy/paste wasn't working and I didn't know how to fix it at first. And because I wasn't logged in, I couldn't use Claude Code itself for this.
My prompt was something like: "Screenshot the Claude Code TUI, transform the URL into a link, open that link in a broswer to get the oauth token, copy it character by character by simulating keypresses in the Claude Code CLI".
(remember: I had no idea how to paste with the mouse not with the keyboard, no I know but I was pissed off and wanted to be logged in immediately... So: another model / harness to the rescue).
(for the curious: it decided to use xdotool and use a 50 ms wait between simulated keypresses to copy the oauth token)
This worked just fine. And I that with a cheap model.
I think that just like Linux and Git owned many proprietary software, we'll soon have fully open-source harnesses orchestrating everything and delegating the work to proprietary tools (like "ChatGPT now Codex and vice-versa" and Claude Code)... If proprietary tools are even still needed at all.
Honestly I begin to wonder if they're even needed at all: the models, sure, while waiting for the open-weight ones to beat them. But those proprietary tools trying to lock people in?
I feel like the open source harnesses are already more powerful.
When has this ever not been the case? I don't think this is a GPT 5.6 specialty!
And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default).
Conciseness is usually a byproduct of information density though.
Claude is terrible at this! Probably for the same reason that its writing style in prose is so annoying and full of claudisms.
If the intent is not easy to understand, it's information sparse. Because it takes a lot of CPU (or brainpower) to interpret.
You can run gzip on an English sentence to make it more textually dense, but clearly it is not more information dense in this context.
The prompt was also “easier to understand”, purely in the sense that the response is more or less guarantee to be what I wanted it to say, which was the point behind the demonstration. I went into more detail on it in another comment around here.
Thus from first principles it's most likely that content which is more understandable to humans is also likely to be more understandable to LLMs. Of course they are still capable of interpreting very obscure structures too, but usually at the cost of cognitive performance.
I'm open to being wrong about this, and I'm sure it's being researched.
(Specifically for text representations)
To your point, at some level of intelligence an LLM will be able to infer the intent of your prompt consistently without thinking enabled, in which case interpretability to a human matters less. But for complex tasks you aren't likely to get optimal performance with prompts that are difficult for humans to understand. And yes, you'd see that with thinking enabled as it churns over thousands of tokens trying to "mentally expand" a compressed prompt.
Interesting discussion though!
Intelligence requires thought/processing, and I think we can all agree on that part, even if we struggle to define intelligence itself. Increased thought or processing requires increased energy, and the universe agrees on that part. There’s no way around it, that’s the thermodynamics of computation and it holds for biological, digital, and as of yet undiscovered systems used by aliens at the edge of the observable universe. Having information means fighting entropy, and that requires energy. The more, the more.
If you give a dense LLM a 100 token long question about the nature of quantum mechanics or a 100 token long sequence of “-“ and limit it to N token responses to both, it will take exactly the same time and energy to provide both responses. If you resist the urge to turn temperature above 0.0 you’ll also get the exact same response for the same input tokens every time. A deterministic response to external stimulus is typically first broad stroke we use to separate thought capable entities from rocks, but even if we grant LLMs their own unique category of “thinking rock” we can see that prompt complexity and energy required to respond are always constant (per token), so the thermodynamics necessarily means there is not additional thought or computation. Physics demands it. That, again, is a deterministic response.
It has a seemingly endless range of potential responses, but it doesn’t. If you don’t add a random number generator, which is common practice, then you can directly map every possible input to every output. I’m pretty sure that’s why Anthropic removed the ability to change temperature on the latest models. They always forced some amount of non-deterministic responses, but it was a small amount and I actually used that fact to track changes in the model by mapping repeated responses.
Most people actually do have some experience with things that have an astronomical range of possible outputs, a nearly equal number of possible inputs, input and output are directly correlated, and input complexity does not change processing time per input unit. One example is a piano, but we don’t worry about confusing it with a complex note.
1. Information density is subjective, lexical complexity is how you measure it. The OP is talking “weight”, I’m talking “mass and gravity.” One of them will get you the other in most situations, so for the causal physicist it doesn’t matter, but if you’re getting into tweaking the universe then your mental model and approach matters significantly. My comment right now could be seen by some as being information dense, since I’m staying roughly on topic and tossing many concepts out, but “lexical complexity” might be the most lexical complexity in the whole thing and taken word-for-word I’m sure less than 1% of it is domain specific. “The program must use parallel processing on the CPU.” That seems decently information dense, but “the” is found in nearly every block of text ever written, “program” - are we talking television? Theater?, “must” is no better than “the”, and so on. Compare it to “#include <immintrin.h>“
2. Most people don’t realize how far that goes with LLMs. The vocabulary it has is dictated by the words in the conversation. If I ask you “what time is it?” you don’t respond “shoelace” because you’d sound crazy, although you could say it if you wanted, but the model absolutely won’t say it because that word literally does not exist yet. The end result feels the same, but the difference matters and it’s why it’s suggested not to use negating instructions. For example: “Do not mention elephants.” Well that mathematically wasn’t possible until you said it. Not having the word in the list of possibilities is a lot better than hoping it adheres to the “do not mention” part. My example prompt took that same idea from the opposite direction. The model must respond, it will be grammatically complete and coherent, and as much as possible the only words it has are the ones tightly associated with making my point for me. It didn’t ramble about baking a chocolate cake because it can’t, and making that the case is the goal with prompting, not specifically density. Word density > language density; feels similar, very different.
Perhaps this comment itself is the irony you were seeking. I spent several meandering paragraphs and included analogies to drive home the point that you should focus on the words that matter most.
What you might not have fully realized is that it’s exactly what enabling “thinking” does as well. That’s why that exists and it literally does what you’re doing as well: it primes the system. You say “light sensor and amplifier” and at some point it outputs “photodiode and transimpedance amplifier” - now you’re off into advanced responses. The thing is, if you knew it you could have just used those words in your question and received much the same response. “Thinking” exists to turn “So, I was wondering..” into academic prose that raises the probability of academic tokens in the response.
You can kind of cheat the system by doing the same thing for a fraction of the token cost by using something like Haiku to provide a comma separated list of advanced topics and jargon associated with {Your question}, then tack that onto your prompt to Opus with thinking disabled. Obviously easier if you’re using the API, but I’ve run hundreds of millions of tokens though that process and it’s consistently and measurably better than their default thinking. I believe that’s because Anthropic and OpenAI drank their own kool-aid and are treating it like a sentient being that needs to add “hmm…good question” so it feels more thinky about things ‘cause that’s how we do it. The fact that it isn’t, and doesn’t, is why I developed the example prompt I showed earlier; it’s an extreme play on the offloaded “thinking” I also use.
> GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic. Instead of generic instructions such as “Be friendly and warm,” use concrete guidance: > Be direct and tactful. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
Soo basically, my new 5.6 custom instructions: Be Jeeves and eliminate all friction from my life through immense processing power. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
[1] https://developers.openai.com/api/docs/guides/latest-model#c...
This is a trap.
It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined.
For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether.
We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play.
In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying.
Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.
Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc.
As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins.
That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.
It should follow instructions incredibly well while inferring contradictions or gaps in logic and surfacing those to the user as suggestions for improvements and persistence.
I really hate how Claude just assumes you want to do X/Y/Z and goes off and breaks everything and you're constantly screaming at it STOP DOING THAT. Instead, it should just do the minimal things while building its own guidance along the way in a persisted memory, like, 'would you like me to do X, now, and in the future?' etc.
I want the same as you, and even further, I want a model that refuses to execute changes I request if they don't make sense considering the context, or if they're impossible, and avoid any sort of quick hacks and patches. But I also want a model that does the pure opposite, that I can chuck a "Do X" query at and it figures it out. Then I'm sure there are middle-zones between these two, or even more extremes too.
But the choice isn't there, we get to chose between "fast/stupid", "medium/medium" and "slow/smart", then that's it. With system prompts we get to steer it a bit, but I've needed to make my own fork of codex to surface those things to me (the user) so I can control it better, and different models respond differently to the "Stop and don't implement anything if the request doesn't make sense yadda yadda" parts, would be lovely to have those sort of "personalities" surfaced up front when making decisions about what model to use.
I use better questions than the above but will keep my questions safe so they don't end up in the model, the point is however, when the model repeats your question back to you and "gets" what you really mean, that's a good sign of intuition and also suggests you'll get a response back that hopefully matters.
I used to go to a barber and if you said "cut it short", he cut it really short.
Remains to be seen how the "shorter prompts" advice translates to homogeneity/collapse though.
This will totally make it brain damaged over a certain tasks. Sort of like the same brain damage that prompted OpenAI project managers to destroy ChatGPT.app today.
... What changed, exactly?
Seems good/fine once you get through upgrading the app.
(For that matter at what point is it "long"? And does the rest of the context matter? Should it be short too?)
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
I don't follow. Isn't "the model actually cares and will do what you say" a reason to use those kinds of instructions more liberally?
Now if you tell them too much they go mute or stop telling you important information. Oh intelligence!
But this is exactly why we should not anthropomorphize the models: they are very obviously not conscious, because they are not alive, any more than conventional computer programs are. And proposing otherwise leads to absurd moral arguments, while not really serving any other purpose.
If you don't like the fact that some people disagree with you about what the word "intelligence" actually means, fine. But I am not about to entertain a world in which humans face moral retribution for "enslaving" a literal inanimate tool created by humanity.
Can you prove that these models aren't conscious? And, as a counterpoint, can you prove that you are conscious, rather than a philosophical zombie?
We bred horses, cows and sheep. Most of those that live today wouldn't be alive if not for human intervention. Does that give us the right to do whatever we want with them, without consideration for feeling or morality?
In this case, you can take comfort in the idea that the tokens these models produce are likely a form of excrement to the conscious entity metabolizing the information, and rather than enslaving anything, we're creating a habitat and "harvesting" the byproducts.
Does this mean ChatGPT will stop botsplaining things to me? I get it quite a bit more per unit time from ChatGPT than claude. Maybe that will change now.
(By botsplaining I mean when the AI explains some unstated premise of the prompt itself back at me as a correction when in many cases it's the motivation for the question in the first place)
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.
y'know, I don't think I will. I really, truly want one-word answers to any binary or multiple-choice question. If I want more, I will ask for it once the model has given its answer.
Winner by default!
It's making it very hard to justify even trying to use Fable. When it works, awesome; it's legitimately good. But I can't trust it to do a task without deferring to Opus and that's really annoying at times. I want to know what I'm getting up front, not after the fact.
(It’s not)
If I'm going to be getting AI-generated results, I'd rather read the output of a model that has all the context of my specific situation than slop generated in bulk with a cheap model from six months ago.
At some point just kill the thing, it's not able to work properly as it is.
Cancelled my subscription today. Hope OpenAI isn't patronizing like Anthropic. I don't want to hear about their "safety" bullshit ever again.
Let’s be generous, it will uplift the investors pretty well once they start charging the real token costs and maybe drive out a few competitors.
Seems a bit more hand picked than usual to me..
Pathetic situation, this one, where we are supposedly building a superintelligence while at the same time thinking that fasting is a biological weapon.
Sol is the first verified frontier model to ever beat an ARC-AGI-3 game
Also what does this tell about Yann LeCuns whole world model theory? Bro has been going on and on about it. He has made multiple wrong predictions on the trajectory of LLMs.
At some point his claim should be fully falsified no?
And yeah.. Reality has not been kind to LeCun.
JEPA is just getting started
There's many ways to skin a cat so you can probably do something with a JEPA approach as well, but I doubt he actually catches up to having agents on the level of where Anthropic/OpenAI will be at any point.
What exactly was he dead wrong about that is proven by any of this?
GPT getting better has absolutely nothing to do with completely disproving anything LeCun has been saying.
He never said LLMs couldn't get better. He never said they couldn't score 7.6% on Arc AGI 3.
He's merely said they don't think, and you probably want something that actually thinks if you want a model that can be trained cheaply on a small amount of data and provide a ton of value.
Spending $5B to train a model that scores better than an older model does not disprove any of that in any way.
He said years ago even 'GPT 5000' couldnt do things that they ended up doing fine a month later, let alone by 5000. His later predictions are just moving that goal post including towards them not being able to do more general, harder problems of which Arc AGI is a counter-example.
What things specifically and when?
You probably wont like the edit but I dont have the timestamp of the original on hand, you can find it.
LeCun's ideas cannot be reduced to a 6 second clip...
You're missing the forrest for the trees, taking a singular example of a problem and thinking that if an LLM can solve the singular example it completely disproves LeCun is comical...
Ive read and watched more of his interviews and lectures it seems, it feels like you just have a rosier idea of his views than the views he repeatedly presents.
To me LLMs have gotten better since 2024, but their fundamental flaws still seem there.
They hallucinate when it comes to really challenging tasks such as math proofs. They still do not reuse code well and will rewrite functions instead of perusing the standard library.
But this is good news. LLMs are awesome and they are only the first step towards AI being applied everywhere. They are a Model T
That LLMs don't have common sense and don't have good physical reasoning abilities; that you can't scale LLMs all the way to AGI; or that they can't predict the consequences of their actions which is the foundation of agentic behavior all seem like still (mostly) accurate predictions to me.
While LeCun has his share of problems, I think largely his criticisms on LLMs are more right than wrong. What remains to see is how good JEPA can be at filling in the gaps left behind from the brittleness of LLMS.
The human brain manages to self-organize with only a fraction of the information that LLMs get trained on. To train an LLM you need a lot of high-quality data.
There are two threads in history: firstly the compute thread leading to GPUs and AlexNet in 2012. Secondly the model architecture thread that started long before we had the compute and lead to transformers in 2017.
If the compute thread had been 30 years behind then we might be spending this century coming up with better architectures to make do with the more limited compute. However since the compute came first, we settled on the first thing that worked (transformers) and all effort went into polishing that.
There's something wrong with transformers though. No matter how many trillions of dollars get thrown at the problem, they still don't learn like humans do.
So? The question isnt can we get to ASI as efficiently as a brain, the question is can we get there, which we likely can. The inefficiencies can also be fixed after that.
>No matter how many trillions of dollars get thrown at the problem, they still don't learn like humans do.
Again, so? Humans are efficient but also bad at many things that transformers are already better at because of it. You are looking at the wrong thing if you think it needs to be like humans.
He said as you need more and more tokens models will fall apart because each additional token is a chance for a mistake and they will just exponentially fall apart. But in practice models have learned to identify and self-correct mistakes and if you look at the graphs more inference reasoning tokens almost always give far better accuracy.
His other criticism of LLMs that I like better is that they try to predict tokens instead of learned embeddings. Tokens are arbitrary and in order to decode LLMs you need technical analysis (see mechanistic interpretability).
With JEPA models so far, it seems that PCA on latent vectors suffices.
tldr: embeddings have a lot more room for improvement
"we use a rough estimate of a total 9M GPU hours"
From CoreWeave, at current prices (~$2.46/hr spot to ~$6.16/hr on demand) would correspond to $22M–$55M.
The dataset is really where the cost is though - they used LVD-1689M - 1.6B images of curated web data from roughly 17B instagram images. This probably cost a huge amount of hours in human annotation, compute for algorithmic filtering, etc and not to mention probably a 20-50 person team working on this model.
You might want to change assumptions about how expensive these models are.
The 9M GPU hours includes the DINO v2 inference used in order to curate the data set.
The final training run used like 300000 dollars of compute.
Unfortunately we don't know how much RLVR + Agent training costs these companies. I'm just gonna say it's in the hundreds of millions, because they are supposedly making billions of profit on inference yet making billion dollar losses
The difficulty in predicting a latent is so called "collapse"; the embedding neutral network can always output the zero vector and this would predict the output correctly.
There are different ways to solve this, DINO uses two different models - a teacher and a student and LeCunn uses an explicit term against collapsing to a single output.
Yann mentions DINO in his talks
Mythos can do some amazing things (I'm assuming, I've never seen it). A young child can learn to control its body without reading any books on dynamical systems and kinematics. Mythos cannot learn to control a humanoid robot after sucking in every piece of data Anthropic can get their hands on.
I’d not wager against him having at one one more break though architecture before he retires.
It's telling
As far as the lack of shipping, they're scientists and what we're doing now with LLMs is more "engineering."
Richard Sutton specifically states that the search has to be smart. We know that the brain uses recurrent connections and is shallow. I think a lot more money has to go into architecture. Feed Forward transformers can only scale so far
I agree with the sibling comment, effiency is probably the more important component at this point. We are hitting not just a practical engineering roadblock for scaling with current technology, I think we have definitely hit a financial and logistical roadblock for up scaling with the number of GPUs (on an immediate basis)
Or a breakthrough in algorithms etc.
The human brain, heck all bio brains, are proof that you don't need a lot of power or size for intelligence.
The real message of the last 15 years has actually been the opposite: if you throw enough processing power at it, intelligence emerges.
[1] https://www.sciencedirect.com/science/article/pii/S193459091... [2] https://pmc.ncbi.nlm.nih.gov/articles/PMC5063692/
Yeah, people might object, but it can be argued that we are already subjecting scores of animals to horrors beyond comprehension just to get a bucket of chicken wings. And even if we manage to get silicon to do what brains do, it will likely cost 1000x as much and consume 1000x the power like you said.
It's hell of an economic incentive.
Bitter lesson wildly overstated in this context.
(had to look it up)
That may not be the intent of the original article, but over the past few years that’s what the phrase turned into.
A lot of pretraining is also choosing the right type of data, you don't want to just have it ingest garbage (although I read that some amount of garbage actually helps the model be more robust). Pretraining crystallizes a lot of the inductive biases that post-training builds on, so by crafting the right data mixture you can make it easier for it to start off with a good foundation. There is also a lot of focus on mid-training these days, which I understand is basically either the name for the synthetic data stage, or the SFT phase before all the RL
Couch cushions?
As GP said. More RLHF is in fact the bitter lesson.
My sense of the Sutton Dwarkesh interview was that he was calling out that he didn't mean just longer datasets, but rather learning through exploration and that's exactly RL.
Isn't this just the difference between getting 0 right and getting 1 right?
I'd imagine they're going to 10x this, maybe 100x this.
Right now, we have models that are statistical models of language, with a world model and reasoning "falling out" of a lot of effort.
It's like we've made something that's a little bit intelligent, and now we're trying to amplify that trick to create something that's quite intelligent. And - don't get me wrong - it works.
But it's also super, super inefficient. We're having machines "think out loud" to compensate for the quality of their thought processes. We elongate the path to make up for the progress made on a given step.
I tink there's probably a much smarter way of doing things that will require qualitative architectural (and quite possibly hardware) innovations. Right now we're on the path to a Dyson sphere: that's probably not going to be necessary once we figure out a smarter way to think.
So as more thinking is cached in their weights through increased RL training, those weights are doing more useful work and the efficiency is increasing.
[1] https://www.lesswrong.com/posts/2MD3NMLBPCqPfnfre/cached-tho...
It would be nice to see on which categories of problems the extra thinking makes it better and on which it makes it worse.
When Mythos was announced after that, I was pleasantly surprised to hear about it. But when it turned out to be only two times bigger, I was a little disappointed!
(I am even more disappointed with the safety filters, but that's kind of a separate discussion... "Fortunately" I find that I can usually edit my prompt by single character and get through...)
Replay of Sol attempting the game: https://arcprize.org/replay/83543d22-8e1e-439a-8809-129ff1d9...
It seems a weird and arbitrary challenge for a language model to be expected to perform. It also seems like there are some harness/visual issues even in the first few steps, where it states that it hasn't moved when it clearly has.
I was thinking about those species earlier in the context of, what does intelligence mean outside of language.
The benchmark appears to be testing the same thing. Although I don't know how much transfer there would be between this data set and the kind of situations a crow or an octopus would encounter.
Edit: Huh, it's just a Game boy game? I just did a couple of the tasks. It looks like C64 era game to me. Navigating levels. A lot of overlap with animal intelligence then.
I think it is historical name. At some point when benchmarking was very undeveloped, this was targeting abstract reasoning and generalization, hence AGI.
Fable's main advantage is that its average solution size is smaller. However, GPT 5.6 Sol is a substantial improvement from GPT 5.4/5.5 which would write verbose, defensive code. 31KB for GPT 5.4/5.5 down to 26KB for GPT 5.6 Sol, with better performance for Sol.
Fable scores slightly lower, but with an average solution size of 12.2 KB.
I wonder why nobody has tried to optimize for actual code size or complexity metric, or at least why I haven’t seen more benchmarks that display this. GPT5.5 just keeps pushing more and more pointless indirection into every function it writes in my main project, it’s borderline negative productivity.
P.S. I’d be curious to see Cursor’s composer models in there, they seem to be among the best performing low cost models: https://artificialanalysis.ai/articles/cursor-composer-2-5-c...
For my usage, I would very much prefer if those $/task were being spent in thinking and experimenting, and the actual output would be as short and maintainable as possible. “maintainability” is a vague target of course, but it’s at least somewhat correlated with code size.
I have not broken down the comment/code ratio, but that's actually a really interesting idea for a metric.
I would also like to test Cursor, but our policy is to only test models available on public routers for now.
Agentic coding data: https://gertlabs.com/rankings?mode=agentic_coding
Yikes
What's the consensus today on codex vs claude code, does it really matter anymore?
You can also make it not count against extra usage.
OpenCode docs show it because Anthropic specifically ambushed them with a PR to remove support so simpletons can't use it easily.
Further, the claim that the subscription "version" of the model is worse sounds like bullshit (and the sort of anecdotal nonsense that you see on sites like this). Do you have anything substantiating this?
This is one reason it surprised me that Anthropic decided to run stuff on Musk's hardware. It seems overwhelmingly likely that the new Grok release is informed by what Musk has been able to learn from that relationship.
It's more diligent and empirical and results focused, and less creative. It sometimes needs a kick to avoid a Zeno's paradox of incremental steps to get to the goal. But it produces more reliable code with fewer race conditions, unhandled negative cases, etc.
It's also better value from a $$ POV, or at least has been. This fluctuates a bit.
You're also free to use your Codex subscription with other harnesses, like opencode, etc. Unlike Anthropic. Plays better with others.
Anthropic has certainly had some drama inflicted on them by the US administration, but otherwise they have just had heads down and executed with great focus. That is why they have succeeded.
https://investors.palantir.com/news-details/2025/Anthropic-J...
Can you post more information about this?
https://www.google.com/search?q=claude+usage+draining+site:w...
Try Pi: https://pi.dev/
pi is also worth tinkering with, particularly if you have an eye towards automating some things.
Codex is more details focused, often catches wonky bugs and correctness issues that Fable misses, feels more terse and less "friendly", more like a stern senior engineer versus a friendly talkative engineer (Claude). Codex is also better if you're already an engineer, Claude is better for non-engineers. I.e. Codex works better if you know exactly what you want and know the right way of explaining it.
You get much more generous usage from the 20x plan.
And you get far better uptime.
If benchmarks and early tester impressions are accurate, you also get access to Fable level capability at greater speed and lower cost (included in subscription).
$2 says nah. You can't take Fable away in a week where GPT-5.6 and Grok 4.5 launch, if you want to hold on to customers.
Knowing Anthropic, this unfortunately might end up meaning a quietly quantized Fable on subscription.
Coca-Cola doesn't "quietly water down" its product to save a few bucks. They know people will take a sip, say "oh that's not what i wanted", and go buy a Pepsi.
If they serve me a quantized Fable, I'm just going to think Fable sucks and go get my tokens elsewhere. What's the point?
Coca-Cola is also mostly measurable and reverse-engineerable.
The Claude models are black boxes, and actively curtail distilling efforts.
They've also introduced banked resets, which are really clever. If you have a $200/month plan and three banked resets, you're not churning because you will overweight giving up those resets (loss aversion theory).
I had put a decent amount of effort into setting up that initial codex attempt and it went so poorly that i've been entirely uninterested in trying again. This was maybe a month or so ago, and i know stuff moves fast, but for me, i like the models, dont care for the harness.
https://arena.ai/leaderboard/agent
5.6 isn’t on there yet but Fable leads by a significant margin atm
You're fully free to use and try anything and without caring about what others think is right
> Well it's objective _to me_
I have one non technical people in my firm using it. One is using it to assist with editing books, basically using it to gather up manuscripts from e-mail / Google Doc etc. submissions, and then switch models between a cheap one and Opus (for actually analysing the manuscript).
The other non-technical person has done really surprising things with it AI, like a long-running GPT 5.5 Pro chat session which is basically her expense tracker - it has an .xlsx file "carried" in the chat, and she just tells ChatGPT (or scans a receipt) whenever she has a new expense, and then prompts it in natural language when she needs a report. I'm looking forward to seeing what she can do with omp.
Care to detail this?
I've tried a fuck load of harnesses but keep coming back to Codex as my harness.
- codex UI is much more responsive
- i get feedback about the progress easily
- the tool calls and results are very legible, I can click them and see the progress
- no one talks about this but the tool call and response notification are handled much more elegantly in Codex. In Claude Code, it is handled in a clunky way using loops which always causes some delay
- you can steer the conversation midway in Codex
- /side is underrated (/btw is the equivalent and is much worse in Claude Code)
- I have to admit subagents are handled better in Claude Code
For personal stuff, I've been pretty happy with chatgpt's $20 plan. I believe it has considerably higher limits than claude's $20 plan, and it's enough for the personal stuff I play with (hermes, and some small coding stuff). Also allows me to keep up to date on openai models.
I've been using it with hermes and some coding (with opencode), and I am getting a LOT more than one feature out of it, but the work is spread throughout the week.
I tend to define it "better at solving, worse at assisting phenomenon". Which doesn't properly show on benchmarks that only focus on the solving part.
Though my feeling, no proof, is that the opus/fable today is not what it was months ago. there was a time for about a month where opus was incredible. Just incredible but as fable started to move out i swear to god it feels like sonnet now. Fable feels like opus used to but costs more.
Stick the "Never suppress errors" section into your Claude.md, this will never happen again (works for me with Python/Flask, ymmv for other languages).
I don't like OpenAI as a company, but they appear to have QA, and that is probably enough to get me to switch.
Basic stuff about features that are more than a week old just get no attention at all. From the outside Athropic seems to be a clear feature factory.
this has been my experience with Codex as well, and I have to fix its mistakes every single time. But recently, I literally threw away three hours of work because it kept adding hundreds of lines to my code base. When I restarted the entire work using Fable and Opus, it was like night and day.
Obligatory YMMV, maybe your prompting style fits gpt better. We forget that this matters a lot
IMO that's exactly why it's a bit better at actual problem solving.
You absolutely do not "always have to correct" Codex. I'm not sure what you're doing, but I'd say 80-90% of its edits on my side it doesn't need any revisions.
Did they fix that, as that for me was what actually made codex worse.
If anything the online optics have been bad for Anthropic for the last half year. OpenAI doesn't have optics issues, from my point of view they simply have the issue that they are the least trustworthy player at the frontier. The way they pivoted from their original mission is truly breathtaking, especially coming in gloatingly to take the government contract when Anthropic got kicked out for insisting the government does not use their systems for mass surveillance or autonomous weapons systems. You understand what that means, right? OpenAI models are now actively used/developed for mass surveilance and/or autonomous weapons systems.
I know there are plenty here who seem to value their own ability to use these models cheaply above all other considerations. Then OpenAI is a great choice, and much less restrictive than Anthropic. But their problem is not on the optics. It's on the substance.
Gemini is fantastic, however.
But soooooo cheap. Especially for those of us where a monthly sub doesn’t make sense.
It's not better at reasoning on complex coding tasks, Claude Opus is still ahead there, but not by a lot.
To be honest though, I've gotten to the point where I prefer the OpenCode UI. A big win for OpenAI is you can log in to your subscription in OpenCode, whereas this is not trivially achievable for a Claude subscription.
I was getting some really impressive cost efficiency today in OpenCode with the following:
* Main session agent: gpt-5.6-sol (high) via OpenAI subscription
* General purpose subagent: deepseek-v4-pro (high) via OpenCode Go subscription
* Using `obra/superpowers` for subagent driven workflows
* The main session only being allowed filesystem read permissions and everything else delegated
GPT 5.6 was picking up on quality and functional issues from DeepSeek and having it resolve them cleanly, and I didn't even get close to my quotas whereas I can usually blast through them. I feel as people get more comfortable with subagents and mixing and matching models in their daily work, Anthropic's walled garden stance will start to hurt them.
One thing I appreciate with Codex is, OpenAI nowadays sometimes just gives you quota resets you can bank, so when you use up weekly quota before the week ends, you could just reset the quota, to continue using Codex. I've been much less anxious about Codex quota because of this perk. I just used one reset in the bank yesterday, and still have 3 resets left. Whereas with Claude, when you've used 95% quota 3 days before the week ends, you'd be much more anxious.
On the other hand, Claude Code's /remote-control mechanism is extremely helpful when I am running it in the cloud and wants to monitor it or control it on my phone. Codex currently doesn't support this kind of usage. Codex only allows you to use your phone to connect to a session on your desktop, not in the cloud.
It’s amazing how much work you can get done on your phone now, especially if you already have a design mapped out in your head.
One killer feature that Claude has, and AFAIK Codex still lacks, is the ability to start a session in the terminal and then hand it off (actually just remotely control it), from the iOS app.
Last time I tried Codex on iOS it required a ton of set up to link a github project etc. The way claude lets me remote into a session I've already started on my actual machine is much better IMHO.
You sign in the Codex app on your Mac same on iOS and are able to completely control your sessions - fork, side chats, plugins - everything.
It’s really great i often work through it. And you can connect any number of Codex instances on any number of macs and then manage them all through the iOS app.
1. Run `codex remote-control --help` directly on your Linux server. 2. From the desktop app, connect to your Linux box, start Codex there, and make it remotely controllable.
Either approach will get you set up.
1. Install another copy of codex in a special dir on the Linux machine:
$ curl -fsSL https://chatgpt.com/codex/install.sh | sh
$ ~/.codex/packages/standalone/current/codex remote-control start
$ ~/.codex/packages/standalone/current/codex remote-control pair
4. Voila! The codex sessions running on the Linux machine now show up on the phone!
Not sure if there's a Termux equivalent for iPhone.
Free, OSS, pretty great for ssh via VPN => tmux a => codex/claude
I set up Codex to send a notification when done over Pushover (https://pushover.net). With this setup, you can just ssh into a Mac or Linux box either way.
Just use termux on your phone and connect to a tmux session on your server.
Codex won't know the difference.
It's vastly better this way. Sure, it may impact the bottom line but it's a huge customer satisfaction win.
When Anthropic randomly resets me and I've only used 2%, that's worthless. When OpenAI tells me I have 3 resets available to use whenever I want - it's wonderful.
That's actually pretty awesome. Anthropic's random resets often have me scrambling to launch huge sessions to make the most of them before the weekly rollover. The gacha-like mechanics are maddening.
After 6+ months of exclusive Claude Code usage, I was begrudgingly forced to try Codex once Anthropic rejiggered their limits such that I kept maxing out my $200/mo plan in just a few days. These days I pay both $200/mo plans, and it's just about enough to get me through a week's work (small game studio - infinite code to write!)
Curious: what multiplier do you think your productivity has increased by, from before AI?
[1] https://www.reddit.com/r/ProgrammerHumor/comments/10ek380/co...
Simple UI change? I do an AI review, but otherwise neither read nor write the code. The models are good enough they write better UI code than me, 9 out of 10 times. Not always the more idiomatic, but usually safer and more correct.
Change to our core data plane? I might spend 2-3 times more effort reviewing it than before AI. Yes, I go more slowly than pre-AI. Many more reviews, many more angles considered, including both human and (lots of) AI review cycles.
Most code is not that critical, and AI is also scarily good at writing tests. We also spend considerably more time paying down tech debt and testing thanks to AI, now that the cost is near-zero.
Net: I spend 10-25X less time on low-risk changes. I often direct (or at least approve) the implementation approach, but I rarely read this code. I spend 2-3X more time on high-risk changes. In both cases, I never write code "by hand". Since about November, I've had no reason to actually edit code in a code editor (perhaps maybe except .env files, which we don't allow agents to edit for obvious reasons).
AI is a tool. You can use it to go fast recklessly, or you can use it to go slow with confidence. Just like before AI... the skill and art of engineering is knowing when to do which.
:-) I hope you read those tests before claiming it's "scary good"
Having said that, in truth, I almost never read the unit tests. Before AI, we had almost none (see: several person game studio) so the tradeoff is not "AI-generated tests" vs "human written ones", it's whether we have tests at all. So, I take them for what they're worth - not much - but if it catches an extra regression before it ships every now and then, it was worth it for the price (~free).
I can’t imagine a more useless test, but I get that it wants to verify that it actually made the change. I just delete the test when it’s done.
I have 10+ of these workspaces in parallel, and I context switch between them as I get blocked on things. I manage the workspaces using `herder`, which is a terrific tmux-like tool that allows me to keep those workspaces on a nixOS machine I have at home that I SSH into via tailscale, so my agents don't stop working every time I close my laptop (it also lets me leverage that machine's computing resources instead of running dozens of servers and harnesses on my poor MacBook).
Can they all be wrong/paid-off?
This is using the same AGENTS.md prompts, which were designed firstly for Claude use, so maybe it's something that could be optimized better if I understood gpt as well?
However you can do what you are asking "fable--> sol" you need to setup a mcp or have fable run a bash tool, just invoke the `codex.exe` cli tool with whatever cmdline args are needed.
Codex and Claude Code are not mutually exclusive, you can use both.
Codex writes all of the code, no exceptions.
Works great, especially when you ask Claude to break up large CRs into roughly 10 minutes of Codex work each.
I use Codex because it's better at the kind of code I need written (math-heavy, 3D geometry code).
But if I was doing mainly UI code, I would do the opposite.
It is so hard to tell at this point between the models to make generalizations like this.
Just complete nonsense.
Personally, I find it very interchangeable. I open codex --yolo or claude with whatever there yolo flag is (have an alias).
Consensus is probably the wrong word for the popular opinions reflected in HN that you might get.
I would recommend that you have 2 of each at all times when it comes to AI so you don't necessarily become overly locked to quirks of one thing. You'll soon realize that things move so fast that you just start internalizing common patterns instead of depending on one specific vendor.
I recommend that you try pi and codex besides claude, to get your own feel for it.
But what I love about Openai is that they still let you hook OTHER harnesses up to a subscription. My Pi setup has been built up for a few months now into exactly what I want and moving over to CC or even Codex is really annoying.
Caveat: I vibe code in tiny little chunks. I see what I want to do, and exactly how I want it done, then prompt that, refine, what was output, then repeat. I bet Fable is better at building a whole app from a 2-sentence prompt; but that's just not important to me at all.
Claude lost my trust around February this year when the plan would say nonsensical things as "delete this method" that was clearly a key method on that part of the codebase.
For personal projects I am using Codex 20$ plan and when that is over I use DeepSeek which is insanely good for the cost.
They're different models with different philosophies behind them. This is anecdotal with a user group of 1, but in my experience:
Claude has a stronger personality and is more creative. If you give it vague instructions, it's better at filling in the blanks with reasonable ideas.
GPT-5.5 is better at following instructions. If you know exactly what you want, it will do it without going off the rails. It's also less likely to imply that you're dumb, but I don't really care about that. Some people do.
Between the two the biggest difference by far is ... getting your harness / AGENTS.md / skills / tools set up right.
I tried them both side by side, mostly for reviewing existing Godot/GDScript code, or sometimes generating Swift Mac apps, including converting ancient relics I wrote eons ago in Visual Basic on Windows
Codex was consistently better than Claude: https://i.imgur.com/jYawPDY.png
Besides the useless "This is good" findings while reviewing and the excessive "oops you're right" backtracking, Claude's atrocious UX and borderline "spyware" make me never want to try an Anthropic product again for a long long while.
I'm trying Codex as my primary the last day or so, because I'm at 98% use and reset in 3 days on Claude. I'm worried about a lot of our skills and CLAUDE.mds and the like getting lost unless I migrate them, but otherwise codex seems to be working great.
Codex with GPT 5.5 is much better at general SWE tasks but Claude Code with Opus is far better at complex reasoning tasks like reading and summarizing research papers, replicating experiments, identifying research gaps and proposing interesting follow ups.
I personally find GPT-5.5 to be a better programmer than Opus 4.8, it is extremely thorough, but I don't like the code it generates ("austere"), and find Opus 4.8 to write more "human friendly" code. The programming comments GPT-5.5 makes is pretty awful where-as Opus 4.8 is good. I feel like Opus 4.8 is better at grasping my intention than GPT-5.5, and honestly find GPT-5.5 to be kind of "autistic". I do prefer the language (not the writing) of GPT-5.5, as I find the philosophical flowery language of Opus 4.8 kind of annoying.
I have only managed to try Fable 5 a little bit, which feels like a much more generally smarter version of Opus 4.8, that is much better a programming and grasping your intention, and I think even the intention of your code, and is _really_ good at spotting bugs or problems with logic in your code. It feels wicked smart but is extemely expensive. It feels smart in the sense like it has a "bigger brain" and is much more sensitive to subtleties/details.
These are different "brains", have different "personalities", etc. I think the best thing is to develop a feeling for it yourself.
- built-in image generation using your subscription, which can be super handy
- can actually edit Google Docs and Google Sheets (Claude can only create new or sometimes append)
- I get a surprising amount of mileage out of the $20 plan
They both have their places for sure.
Claude's very bloated and convoluted by comparison. Maybe you need the bloat (Claude Design), but I prefer the more razor's edge efficiency of Codex.
Model wise, I can't really tell. They all do what I want them to do most of the time and go off the rails occasionally. The question is increasingly becoming who's faster and cheaper and gives me more tokens, not who's better.
I installed the Claude Code Codex skill provided by Anthropic and I am having Claude invoke it automatically to review all plans and changes. The nice thing about this is that for an additional $20/month pro plan I can extend the runway for Claude rate limiting and compare frontier model responses. I am looking for more ways now to work in Codex as a subagent that gets used automatically from Claude Code.
I didn’t think I could have found a better solution, spawning multiple subagents with different models is such a great thing.
I built in the past very small cli wrappers to call other models; Claude Code often refuses to do that, lies and does the job itself instead of delegating to another provider’s llms.
Not having to deal with Anthropics constantly changing policies, token-gating, and carrot-and-stick marketing helps me to focus on work, rather than dealing with their company problems.
IMO the two biggest problems not really being answered by both OpenAI and Anthropic are: 1. Why not make specific models good at specific tasks for Codex/Claude Code. Theres a handful of types of work here whereby small good quality models would do better than these generalised all purpose models whereby someone discovers Fable is bad at biology.... 2. Why cant they consistently run these models and keep them performing? Performance of the models seems to directly correlate with amount of compute available, but they dont talk about it...
Codex historically will follow tasks more closely with less creativity, whereas Opus will do more than you specify. I wouldnt consider either one better due to this fact, just makes them useful for different situations. Generally they'll perform similarly for most tasks.
Opus and Fable dominate 5.5 in artistic design (pixel art, ascii art), and edge out 5.5 slightly in general UI design taste. Have not tested Sol in that regard yet.
So far in my usage Sol has been superior to Fable at graphics rendering engine optimization.
Codex will work longer, and in single sessions without as much subagent usage.
Codex only has 256k context but its compaction is absolutely next level. You will not notice compactions and they will happen multiple times during a complex task or set of tasks without you ever having to notice or care. Claude code on the other hand still has fairly poor compaction.
Codex has more generous usage limits, and they also give you usage resets (weekly+5h resets) that you can bank for a month or so. Not sure how often they give these out.
Codex also seemingly never has outages or weird delays like Claude code does.
OpenAI randomly resets usage just like Anthropic does
I would use both if you code often
I have a bunch of Claude Code Plugins and yesterday asked Codex to make them accessible to itself. It wanted to rewrite most of it. I was hoping i could get by with some symlinks or something to avoid drift.
I do almost all my regular coding tasks with Codex 5.5 on medium. Sometimes for niche edge cases, or when I run out of tokens on my Codex sub, I'll switch to Claude. Some recent examples where Claude was able to solve things Codex couldn't:
- 3D gamedev layout: I asked Codex to render a solar system in a certain camera positioning, saying it needed to fit the planets of the system to the viewport. Codex just couldn't do it, even on high reasoning: Claude Opus did it first attempt.
- Tricky Tiptap image drag-n-drop layout implementation: Codex failed this after numerous iterations. Claude Opus also struggled mightily to get it to work, but I think around 3 attempts it nailed it. Both of them ended up grepping the Tiptap code from node_modules - that's the kind of task it was.
But these are really isolated examples. Across all my projects (I have many; mostly TypeScript, but also things like C#), Codex "Just Works" (tm), with minimal prompting effort from me.
I have my context managed in a structured (but nowadays way too big) Obsidian Vault. I also built myself a vector based "vault search" capability and have my harness use this as a tool to find thematically similar things across the different contexts, when needed. I also build a few custom skills and extensions for my harness to be able to do my work.
Talking about harness: I use pi.dev and have taken care of, that i set it up in a way as to easily be able to switch the intelligence layer without loosing context. Yes, there are differences in how well models perform, but if a model refuses a task - like gpt-5.5 not willing to build a downloading tool for Annas Archive - I switch the model to something less finicky.
Thus I was able to switch to gpt based models after about a year with Claude (and having had a Claude Max since the early days it was available).
I played a lot with other models recently, to see how stabl my setup is for switching, should something like Fable happen on a broader scale with the US government. As said, minor changes in tonality, minor issues ith the quality of long text being written by the model, but most of it is actually managed in by the tonality docs, guard rails, coding standards and the likes, I set up over the last 9+ months of intensive work with it (first in Claude Code, then Codex and now as said pi.dev).
So YMMV and it heavily depends on your setup. But I more and more treat those models as interchangable.
Comparing this to other models, I find it similar to GPT-5.5 and a bit behind Sonnet 5. You can see how other models fared here: https://senko.net/vibecode-bench/ (you can also fetch the prompt and the the 5.6 Terra resulting code on from that page).
I don't have access to Sol yet (on a Plus sub, which should get it according to what I've read), so can't do the more interesting test. I'll update the above page as soon as I get access - hopefully soon.
I... I think you are missing the point.
However, I would say it is a measure (not the measure). If you look at the entries, there's a lot of variation - definitely not something they memorized outright.
And the test itself is deceptively simple. You need to do canvas rendering, there's pathfinding, command queueing, terrain generation, etc. There are some subtle click handler bugs (various LLMs often stumble on those). And I ask the model to do it all in one file, further increasing the complexity of the task.
And the result is something that you can instantly evaluate. And if the result is any good, even play! So yeah, I think it's a fair test.
I'm sure it'll get saturated at some point. Actually I started with Minesweeper and switched to RTS last December, because Minesweeper was being saturated. I'm expecting (hoping?) the RTS test will last until the end of this year...
There are plenty of little js web games anyway. The point isn't to make an actual game, it's to show coding ability, design and taste in a way that's more assessable than reading a codebase.
Which model is the best at the moment, for this kind of stuff, in your experience?
It even has enemies! (I'm not too mad about it not following my instructions because it can be fun to play :) And I generated that from Claude Code on my phone.
Sonnet 5 also produced a pretty nice version. You can see all of them here: https://senko.net/vibecode-bench/
I'm a bit concerned about this - starting with GPT5, AI labs started doing this 'complete app from a prompt' sizzle demos. When I started working with GPT5 - which was supposed to be a qualitative jump, just like Fable is now, I tried to do a frontend, and discovered that it gave me a CSS-animated purple-blue interface with embossed buttons, gradient backgrounds and dropshadows.
It looked very cool, but it was a bit overwhelming (also broken), and I was really looking for a pedestrian Bootstrap job.
It required not inconsiderable amount of wrangling for GPT5 to stop doing this. So I don't really like the idea that these models have tons of implicit and hidden behavior, to 'soup up' pedestrian prompts.
Clearly much better than the Terra version. I'd say its on par with Fable, and the observed differencies are more due to random luck and open-ended prompt, rather than model capability. (Edit: after some more testing, perhaps not on par - somewhere between Opus and Fable, is a better description).
Fable did better pathfinding and has more terrain variety, visually the map looks better, especially soft edges of the fog of war. And the enemies.
Sol took more care with tiny ux details, added help, and more building varieties.
It was interesting to see where the approaches were similar and where they diverged.
> Create a simple but functional real time strategy (RTS) game similar to old WarCraft, StarCraft or Command & Conquer games. The player should be able to build buildings, create units, gather resources and should uncover the whole map. No AI or multiplayer needed. Use simple but nice-looking graphics. No sound. Implement everything in HTML/CSS/JS, everything in a single file (you can use 3rd-party js or css libraries/frameworks via CDN).
The naming convention is especially difficult to decipher depending on what your native language is. Of course a latin language speaker might be able to easily determine oh yeah each one is slightly bigger than the other but I still think it borderlines too confusing.
That aside all the numbers look amazing, and I'll be happy to probably main this alongside grok-4.5 for a while comparing the two on price and efficiency.
I vastly prefer the direction that OpenAI seems to be going with token efficiency and performance compared to Anthropic who seems to be moving towards a world where you just token-max as much as possible ignoring any and all costs.
Getting rid of that seems like a step back. Just a personal nit though.
I've seen buzz about this elsewhere as well but to me effort levels seem more like spend limits disguised with another word. I don't think they should even exist.
I already know plenty who had no clue what the difference between Terra and Luna would be.
I agree with them, Sol, Terra, and Luna are confusing names. They mean the same thing as GPT-5.6-Max, GPT-5.6-Plus, and GPT-5.6-Fast but require base knowledge for an analogy.
It feels like it was adding by the marketing department.
But do they though? When do you use GPT-5.6-Max-Low vs. GPT-5.6-Plus High? Or GPT-5.6-Fast-Xhigh? What's the Pareto optimal choice (outcome and price)? According to the benches it seems to bop around and the even if the benches are accurate the best choice isn't always consistent.
You don't, because that isn't something I proposed using for model naming.
I called them GPT-5.6-Max, GPT-5.6-Plus, and GPT-5.6-Fast. Reasoning levels are distinct from the model design itself, and the UI makes that clear.
Plus, using that same flawed argument this would be called GPT-5.6-Sol-Low or GPT-5.6-Luna-High which also makes no sense/is confusing. So that argument applies (or more accurately doesn't), no matter the model names.
Similar to Anthropic's size/length based naming: Opus > Sonnet > Haiku
These names seem easy to understand to me, and much clearer than suffixes like -max and -plus.
You don't actually explain why or how these names are "easy to understand" just state that they simply are. That's great; to me, they aren't obvious or intuitive at all. May have well just start randomly pointing at dictionary words.
I'll admit though that until recently I never really thought about Anthropic's naming scheme as having meaning (an Opus being longer than a Sonnet, being longer than a Haiku).
Or something like dog/puppy/sperm.
Anthropic ships models with a helpful one-liner tag that makes the model hierarchy obvious. I think it wouldn't hurt if OpenAI did the same.
Luna is good too, for classification tasks or any pre-processing task that is not critical
I absolutely save money and time by constantly using everything at the highest reasoning. I guess my use case and needs are different from others, but I really don’t understand how it can be true when people say they don’t need the highest reasoning and best model. Every time I drop down, things are missed, code gets unnecessarily bloated, more mistakes, and more iterations to solve the same problem. I think it might be because I’m spending a lot of time in a legacy system that I’m trying to clean up, and given the messiness, one needs all the reasoning available to decode what the hell is going on in there.
Their dev guide has the following:
> Use gpt-5.6-sol for frontier capability, gpt-5.6-terra for a balance of intelligence and cost, or gpt-5.6-luna for efficient, high-volume workloads. The gpt-5.6 alias routes requests to gpt-5.6-sol
https://developers.openai.com/api/docs/guides/latest-model#u...
Terra when you need to get shit done here on Earth, Luna for moonshots, and SOl for when you want to launch something into the sun..
..right?
They could hide this behind a harness that picks the correct model for you, but devs don't seem to like that.
There's also the 'effort' slider, which I guess how many experts in the MoE are evaluated and how long reasoning chains are allowed to go on, which is the 'smooth' scaling you are thinking of.
"i really wish this thing in my non native language was easier to decipher"
huh? if you dont know the words then read them in your native language. Sol/Terra/Luna are immediately unambiguous to an english speaker with any sense.
I'd like to know how cherry-picked this is, and what tests it performed less overwhelmingly in, but I suppose that info is not going to be on this post.
If it pans out to be as good as it says, that's great. On the other hand, if this model is not overwhelmingly impressive over Fable, I will lose what remaining trust I had in these announcements.
Great catch.
No, doesn't seem like it
https://openai.com/index/separating-signal-from-noise-coding...
Regarding your main point, yes, I agree. My impression (as someone who uses both Codex and Claude Code daily) is that OpenAI does a fair amount of benchmaxxing.
GPT 5.6 on the Pro x5 plan is down to... 100%. It looks like they just reset the usage limits again. And I still have two resets on the bench.
Anthropic is going to have to up their game to compete.
Before, they would do their celebrated usage resets while I was behind the already generous weekly quota.
If I had 80% left on Friday to spend over the weekend, they would reset and I'd have 100% until next Friday, a potential net negative.
OpenAI listened to the complaints.
In some ways, more impressive than GPT 5.5 with high(!) thinking. GPT says quite some nonsense from time to time; didn't see any sign of this in MiMo so far, which is a pretty wild difference.
The sooner Chinese labs catch up the better it is for all of us, even if you don't use their models, as they're the ones who define the baseline capability that cannot be taken away from you (no one is going to limit/remove access to an LLM if you can get equivalent/better unrestricted access from Chinese open-weight models).
that is a polite way of saying "I don't believe AGI is coming anytime soon".
What is AGI to you though?
By the way, this isn't about their 5.6 version in particular I guess, it's just the first time I've looked at one of their videos.
I’m still developing, I’m just doing more than I ever did by directing Codex.
The way I see it is the same as I saw the leap from writing code in a text editor, to using an ide with intellisense, to using the jetbrains ide’s, to using mcp’s, to now directing AI - at all of those steps I wrote code, each step less and less but still it has the same output which is it is my work - even writing in a text editor I wrote less Java (until enterprise architects got involved :) ) than C++, and than assembly.
For me and (likely) for OP as well it is the opposite. Result is meaningless without the process. I can't take fruits of my labor (whichever labor it can be) to the grave, and if you remove the process of growing and picking said fruits, then where is "you" in that? Did you really "achieved" anything? Or whatever you wished for just magically appeared in front of you with little effort from your side?
Where is the fun of renting a helicopter that would carefully put you onto mountain's summit and pick you up 5 minutes later?
I am working tirelessly and often long nights, on top of a day job.
My effort has shifted to QA testing, reviewing UI designs, and delegating the agents on the implementation.
I will consider it an achievement if I manage to publish a successful app.
Where is the "me" in that? I am guiding the design of every screen and feature the way I would like it to be.
On top of that I make technical decisions on how it is implemented.
Apart from the loads of QA work I will have to handle the business side as well.
As of now it's hardly as trivial and effortless as some make it out to be.
Yes, I no longer write the code, and sometimes it feels frustrating that any teenager without experience could perhaps build a similarly good app soon.
Overall I'm still happy I can now build much larger and better apps and realistically publish them in my free time, for a chance to make serious money.
I disagree. I think you're actually giving up so many little decisions. You are delegating decisions to agents all the time. In place of your slow but still personal decisions, you are ok with decisions that might be similar to what the LLM believes to be the best average, or the best solution based on what limited experience of the world they were trained on. The Ai devil for me is in these details.
That is probably fine for a lot of use-cases, but it's still removing your own agency from the process itself willfully, and yet still taking all of the merit. And to me that makes the final thing less of a byproduct of you and your experiences.
I am not black and white on this, and there are different degrees to the issue. But I just cannot accept this approach that trades the nature of the output for the quantity of it.
And frankly, a lot of other people feel the same. Check the data on the explosion of apps and how little they are maintained or picked up by final users.
For the design of my app, I try to imitate the UX of Apple's first party apps.
I checked the competitors, and what is in the AppStore looks like it was built in 2015, and only updated to add Ads and subscriptions. Surprisingly, I did not even see vibe coded apps for my use case.
Is it bad that you used a computer and Google and lifted information from other people to accomplish a task?
My father is a machinist and has built has knowledge off the skills and documentation of thousands before him, is that empty?
You still have to do the actual work, and where do you draw the line on "shipping more". If a farmer now has an automated tractor, does that mean his work is now trivial?
It's a phenomenon that's happened basically nonstop since the Enlightenment. Yeaterday it was John Henry, today it's you.
Time to find a new hobby to bring intellectual satisfaction if typing "do my job" over and over in a text box isn't doing it.
What the other replies seem to overlook is that it fundamentally changes the nature of the work - it’s not the next step in the evolution after an IDE, it is closer to an automated tractor, and yes that does make the farmer’s work trivial. Pressing a button and having the field get plowed is a very different experience than manually plowing a field yourself. I can no longer in good conscience say “I plowed that field”, because all I did was press a button. The tractor plowed that field.
Most people on this site seem to feel comfortable claiming “I plowed that field” after they pressed the button that started the automated tractor. Okay, you had the idea to press the button - but you didn’t actually do the work, you delegated it. Same end result, but a different lived experience. Now would I rather actually plow a field by hand or simply press a button? You can probably guess - but the two actions do differ greatly in the experience they bring me, and the feelings of satisfaction.
The argument does eventually degrade over time, though. For example, in the future the farmer uses satellite imagery fed into an advanced AI to build the most optimal route. So yeah, eventually the farmer loses all utility I suppose beyond being a land owner (until AI owns land lol).
I'm still using my brain, just doing a lot more plumbing now and reading a lot more code than writing. Depressing in some ways and exciting in others. At the end of the day it's not going way. It's disruptive and better to embrace it.
Consequently I do not feel depressed or have to disregard any feelings.
I am busy working on my project. It is still hard to ship good software, even if the implementation is mostly getting done by itself.
you have to start using AI to achieve something which was way more challenging before, then you will feel lots of fulfillment and inspiration.
I can do things that were far beyond my reach — suddenly. Almost all at once.
"Now you can discover you suck at marketing and business" :D
How many ancient Roman businesses are around today?
The eval is an agent that runs a set of tools and a prompt we can tune separately for different models. The OpenAI version of the prompt was specifically tuned based on their guide[0]. Then we let Opus to run another agent that acts as a user, trying to solve a problem (anonymized and taken from production). The problem is complex and we don't expect it to be solved by these agents, but we measure how the agents operate when faced with a vague problem:
- Opus 4.8 and GLM 5.2 both identified a constraint sooner and stopped so the user can fix an issue first that the agent cannot solve.
- Sol tried hard to solve the issue with different tools, burning tokens, until finally reached to the same conclusion with Opus and GLM. It was two times more expensive compared to Opus and six times more expensive to GLM for this task.
- Terra went even further and started calling tools that would not solve the issue, burning tokens and failing.
- Luna repeated the same failing tool call until it hit the round limit, and burned more money than Opus.
I'm kind of puzzled with the new GPT. Like, yes Sol is OK for programming, but I was expecting to get a cheap agentic model for non-programming tasks, one that can detect if things go awry and correct. Terra is too expensive and Luna not really fit for the task. Sonnet 5 is a bit better but more expensive than Opus 4.8, which is still the best in my evals. GLM 5.2 is extremely good if you can define the task and the tools clearly for it, and costs pennies!
[0] https://developers.openai.com/api/docs/guides/latest-model
I think 5.6 is still not even close to 5.5 xhigh pre-nerf.
Ultimately I think the issue is that OpenAI is under tremendous pressure to perform, but GPT-6 is not ready yet, so they had to push GPT-5 to its limits, and the only way they could do it was with really heavy RLHF, which has its shortcomings. Like, it is super obvious that Sol, Terra and Luna are all heavily biased towards working on a problem relentlessly because that's what their reward functions emphasized. That pushes up their scores in some benchmarks but does not translate to actual intelligence and capability.
Now if you look at where GLM stands, or even DeepSeek v4 Flash, things get really interesting for what they provide.
US labs completely miss a cheap model that can solve problems for 95% of the people. Gemini 3.5 Flash could've been it if it didn't burn so many tokens.
I use both ChatGPT and Claude for engineering work on a daily basis, touching performance critical code to application backends to frontend work, and I've found that DeepSWE scores don't reflect my reality when I assess high quality output from the models/harnesses.
Not that Opus always beats GPT 5.5., but that 5.5 is ahead of Opus on a general benchmark smells off to me.
And 5.6 Luna ($0.21) is also impressive, cheaper than GLM 5.2 ($0.37) with higher intelligence.
Here's my benchmark results for GPT-5.6:
https://aibenchy.com/?q=gpt-5.6
(the high reasoning variants are still running, uploading them soon too)
EDIT: The high variants are there too, enjoy the hamsters[0].
It GPT-5.6 doesn't seem to be a lot smarter than 5.5, but it is faster, cheaper, more efficient and more consistent:
https://aibenchy.com/compare/openai-gpt-5-6-sol-medium/opena...
Gemini models tend to have most knowledge for most domains, and are one of the most intelligent overall. You can check other benchmarks too, on specific categories, those models still beat other SOTA models.
Regarding Opus, Anthropic models often fail to follow instructions, formatting requirements or simply refuse to answer questions (i.e. Fable).
The issue with Gemini models is that they are not as good as using tools or go into weird failure modes when coding or trying to extract/generate specific data. They work amazing, until they don't...
https://aibenchy.com/showcase/?q=Gemini+3.5%2Cgpt+5.6%2C+5.3...
You can see that most Gemini 3.5 generations are more correct than 5.6 Sol (the net is in the middle of the table, hamster seems reasonable and not deformed, etc.)
This one is really promising, as it may allow to close major gap with Claude in design/UI skills
My Fable weekly limit is at 15% used already, 5.6 Sol at 3% used. And this is with the Max 20x plan compared to Codex 5x.
I don't work on the same tasks to compare them objectively, but GPT 5.6 on xhigh seems much cheaper. Essentially unlimited usage.
The gap between Opus 4.8 and Fable is large enough to drive a GPT 5.6 sized bus through. A better Opus would take some of the heat off.
Otherwise I am not interested.
Supposedly Fable 5.1 is in the later stages of the release pipeline, maybe it takes back the crown from OpenAI, who are now rumored to launch GPT 6 in August.
Or if you want to see some in 3D, OpenAI featured a pelican riding a tricycle, bicycle, pony and another pelican in their livestream this morning: https://www.youtube.com/live/Wq45rvPGNHs?t=1070s
I assume multimodal models can do it already do it today if constantly asked "make it better"
On the other hand: the test is clearly not saturated, given that you can see a clear difference in output at the various reasoning levels / model versions.
https://themagnet.substack.com/p/why-is-it-so-hard-to-draw-a...
But either way, with no real way to visualize the result of the text it starts with - it will always be stabbing in the dark. It can't understand conceptually what any of it should look like and then refine the SVG to improve it gradually. It just throws darts at a wall and hopes it comes out alright.
Have them use tikz instead of svg, or have it write code that moves the cursor and draws the thing in paint.
Compositionality and visualization are generally much, much better at each new generation / release cycle.
It's fascinating how well models have internalized visualizing things without actually having joint embeddings / broad multimodality.
I once used something like karpathy's auto-scientist to mutate the prompts and rank them with a vison model. Some of the winners where pretty neat. I think they have a lot more style than the gpt-5.6 ones. https://xcancel.com/xundecidability/status/20449185674144196...
Also would be good to have a tool where users can select models and instantly see each model's generated pelicans. That will make it easy to compare the output of different models.
A skilled human artist wouldn't have both legs in front of the bike, or a single straight line representing both leg's crank arms.
Dead internet theory? Semi-random parroting by real people? Or something else.
Nice to see you did the quality level comparisons and did three passes.
I've been using that technique myself on my image gen reviews[1] and it also works well in presentations and for personal study.
[1] https://generative-ai.review/2025/12/beast-mode-activated-op...
https://www.google.com/search?client=firefox-b-d&q=pelican+1...
https://chatgpt.com/share/6a5009de-fff8-83ea-98ff-0da17d1d04...
Amusing that they use A100e as the reference point to sound impressive. Different ways you could make that conversion, but based on FP4 FLOPs (yes it's disadvantageous to A100, that's the point), that's something like 200hr on a GB300 NVL72 rack.
Not nothing either, but far less astounding sounding than 700k hrs.
about a sprint's level of effort.
The A100 doesn't have hardware FP4, and you'd be running a quantized model with some accuracy loss but unless this was natively trained on FP4*
* to add another layer, they own the model and could apply tons of post-training techniques to reduce that accuracy loss and probably already do
It's definitely good that Anthropic's feeling the pressure. Anthropic has worn out their welcome with this "safety" nonsense. If OpenAI actually lets me use the LLMs on a subscription without any of this bullshit, I'll definitely switch.
I suspect it's not what you meant, but it's definitely not random and is very deliberate. Just today I got it to reliably trigger the "safety" filter with (drumroll) having it list the weight keys of a 300M parameter ModernBERT-derived model. Their "safety" classifier must be matching one of the key names in there and trigger their "this is a frontier model" anti-competitive filter[1] (even though it's just a tiny 300M parameter model, four orders of magnitude smaller than the frontier).
[1]: https://news.ycombinator.com/item?id=48464732
Fortunately once you know how it works (i.e. dumb keyword classifier) it's easy-ish to get around: just rename the keys so that it doesn't contain the naughty keyword. (At least as long as it doesn't trigger on something in its own thinking trace, which needs... more creative workarounds.)
I don't know exactly what part of my codebase is triggering it, so I'm going to have to keep poking, but apparently the guardrails are not that gentle despite the phrasing. :(
One major sticking criteria for not going with OpenCode / pi for all of my coding is I want access to the tier-1 frontier model of the day without API pricing - e.g. afaik I can't use Fable 5 via pi harness even though I have a subscription, so for this week I'm on Claude Code. It's not the need to Fable 5 for everything, but even if I just want the marginal intelligence benefit to stress test an architecture decision, it's a safety blanket to know there isn't a ~smarter~ model I could have used. And for my use cases, the doggedness and capability of these frontier models has been insanely effective.
My feeling is we're still in the Uber era subsidy period - the moment the subscriptions either try to lock me in longer than a month or stop OAI/Anthropic stop delivering frontier models in the subscriptions, I'm out - switching fully over to pi.dev or another OS harness and routing my token spend via OpenRouter or offloading to Qwen locally. Then I'll have to put an accurate dollar amount on frontier intelligence.
I often wonder whether this doesn't continue indefinitely.
Uber was able to do this because it was just them and Lyft playing second fiddle, with a huge barrier to entry once the network effects had kicked in.
It just seems like the model space has way too many competitors, + OSS/Local options for them to ever be able to jack up their prices. At least once the datacenter bottleneck has been cleared.
They also seem to really not care about alignment, or care about it in the wrong way. It's entirely missing in the blogpost and there are some concerning bits in the model card, seemingly treating CoT controllability as something to be "investigated" rather than the warning sign it's supposed to be.
> GPT-5.6 Sol’s detected cheating rate was higher than any public model we have evaluated -- https://www.lesswrong.com/posts/JFjNmPTbH8kL6xtp6/gpt-5-6-th...
My question to previewers: how are the guardrails for random joe that wasn’t personally blessed by the ai pope to access the non-nerfed model? Fable is a nightmare in this regard, but I’m not sure whether 5.6 also gets a critical side-eye from the gubmint when you ask it to fix bugs in your code (you filthy hacker, you).
Which is something I've never seen with codex before, and I wasn't doing anything funky. Just writing CUDA kernels and benchmarks for them.
That settles it. Anthropic has until the end of this month to get rid of Fable's "safety" bullshit. If they don't, I will not renew my subscription.
I noticed that Fable uses shell tools almost exclusively (even to search and edit files), compared to previous Anthropic models.
Having run some experiments with 5.6, I notice that it uses built-in file systems and provider native tools much more (not shell tools), compared to previous OAI models.
Some pretty big claims and results! Excited to see how it feels during usage.
I use Fable and 5.5 extensively and I still find both have a place in my toolkit, i.e. Fable IS good but it isn't perfect, and it's still better to play them off against each other. I have Fable and 5.5 write plans and have them adversarially review each other's plans.
Having this amount of competition in the coding model space is good for all of us.
GPT 5.5 has a tendency to write English calques and non-idiomatic prose in other languages. Although that can be somewhat tamed with detailed instructions and a corpus of confusing terms, the model’s output often reads like a literal translation rather than native prose. Since I notice these issues most clearly in languages I know well, it makes me reluctant to trust the model’s output in languages in which I’m less proficient.
Ironically, ChatGPT began as a simple text-generation tool, but much of its offerings and benchmarks now focus on coding and agentic workflows, while leaving behind what made it notable in the first place.
So far this has been rock solid, and tens of millions of developers use this setup without issue.
It is not going to wipe our hard disks. At least I hope so. Fable and GPT 5.6 have been ever more proactive, and GPT 5.6 is automating the AppStore on my machine to download an Xcode update while I am typing this.
I believe in both cases it is prompting a model with a fresh context that is tasked with reviewing the reason for the action.
With Claude, I have seen that if the reviewer does reject the proposed action, it responds with a long text about how the Agent should not try to work around this rejection, and instead prompt the user for an explicit approval of the proposed action.
Never had any issues.
More seriously, I was blindly trusting the auto-classifier from claude code (same as the middle option when you do `/permissions` in codex), and it actually allowed the agent to do pretty hardcore `rm` and `git push --force-with-lease` commands, which I would have expected to have to approve manually. Luckily no major issue from those yet.
The best option imo is the integrated cloud environments from claude code (not sure yet if there's a codex equivalent). It spawns a VM in the cloud where the agent runs, and you can open a PR from the app when it's done. Very smooth experience
Does it auto install all the dev/test tools it needs, maybe including things like web server & browser? Does your code live in the VM, or in some external repository? Is the lifetime of the VM the same as the agent, or does it persist until you remove it?
Where can I find documentation on this?
Btw for real tho, if you don't have the time or means to mess with full sandboxed environments, just working within a git repo and instructing on your agents.md project level that the agent should back up dirty files (local changes that were not yet committed) before changing them is enough and super fast and easy to set up. And by back up I just mean a simple instruction to back up to some temp location under random named, but rembered during one "turn" of agent thinking, subfolder ( .../temp/{random}/orginal/tree/file.xyz )
This is so the agent (or you later) can recover even locally changed files if it messes them up for whatever reason.
As for the rest you gotta watch what you're asking for, but generally speaking, these SOTA models are smart, none of them will just delete your stuff even with full access. I've been raw dogging multiple projects on my work machine with zero issues of this kind for months. I created codex_reader read only acccounts for my local databases and just add that to agents.md with a note its allowed to only use that and never had a problem.
So yeah, it won't go on a spree outside of its lane even with full access, but if you give it a box and tell it to go ham, it's on you to make sure you didn't leave precious unrecoverable assets in that box.
before today all the contestants were capped at $10k
"This request requires additional safety checks, which can take extra time. Hang tight or retry with a faster model for a quicker response, though it may be less capable of handling complex requests."
At least it gave me the option of waiting instead of just unceremoniously downgrading me. Appears to be making progress but... weird?
Assuming I take the 5x plan it would give me about an hour of active sessions with terra ultra (maybe ultra is not good value regarding tokens?), not even using Sol yet. Does everyone using codex use the 200$ plan?
I normally use the 100$ anthropic plan and barely ever reach the usage limit.
Well, yes, as explicitly stated on https://openai.com/index/gpt-5-6/: "ultra goes further by coordinating four agents in parallel by default, trading higher token use for stronger results and faster time-to-result on demanding tasks."
With Codex, it is my experience that I can churn through a 5h window in no time with newer models -- especially when they're new. So I tend to use fancier models for planning, and the less-fancy models for writing code based on that plan. I switch to the fanciest model if any part of this gets stuck.
If I've got a something big-ish to work on, I pay attention to the reset timers so I can get more of it done in one chunk.
Models seem to slowly get better/relatively less-expensive as they age. (It isn't clear to me if that's because the cost actually goes down, or if the allotment goes up, or if things get more efficient in unseen ways, or what. OpenAI is vague AF about what we get for the $20 that we pay.)
I look forward to seeing how it compares once I have access. Not getting tripped by spurious safe guard flags could be an advantage.
I generally prefer OpenAI. I usually start projects with Codex; I love the plan we created. Then, after about 2 hours of working, back and forth, etc.I realize it's drifting HARD, or getting stuck on relatively simple things.
Once I get frustrated enough, I sometimes start over with Anthropic. Anthropic has been better (for me at least) to work with from start to finish.
I have the $200/month plan for each of them. (And I used to have the $250/month plan for Gemini... lol?)
Both Fable and Sol are good and definite improvements. I don't have a favorite yet. It's too early to tell.
The biggest difference I'm noticing is usage.
Anthropic is like a used car salesman. Not allowed to do a basic check on your own car (website), they'll scrape every penny possible out of you. Low limits, high api prices, trying to keep everything in their system.
OpenAI is like a cool but aloof dad. Go ahead and borrow his car, shoot, he'll even pay for your gas every once in a while. He'll answer your 5th question drunk, forgetting what you asked. But, at least he's a nice drinker that buys the group shots.
I have to watch my Fable usage, and I'm sure as hell not going to pay API prices. I don't have to watch/worry about my Sol usage even in Ultra.
But, 1 thing I'm noticing using Sol Ultra (on fast mode) is that it's slowing WAY down after a bit. I work the opposite of peak times so that's not it.
It has a tendency to do things without asking, a trait I'd associated more with the Claude Opus & Sonnet models than with Codex & GPT in the past. Specifically I've seen it go and update e.g. README.md files filling it with recenty-biased gibberish that means nothing to the user (e.g. very specific technical notes related to what it was currently working on) or staging and adding design/spec documents that were meant to just be working documents. In general it tends to behave more aggressively with git, if you let it get its hands on it. It has stronger "opinions" on that stuff, that don't always agree with me.
I'm going to have to update my prompts, I think. But I'm not used to this kind of thing in Codex, which in the past has been much more explicit and cautious, and one of the reasons I've preferred it over Claude.
It is very "smart." It also has a tendency to yak-shave things. Producing huge volumes of correctness and regression tests and nitting over e.g. very minor variances.
One thing that is "entertaining" is letting two separate instances review each other's code. They will endlessly find things to nit at.
- Better than Opus4.8 in the coding agent index (doubt)
- Just below sonnet 5, even with glm5.2, in the overall intelligence index
- Cheaper than haiku4.5, glm5.2 and kimi2.6 on cost per intelligence task index
It also defaults to 'low' mode for some reason. Can't tell if that's a step backwards compared to GPT-5.5 in medium mode so I'm sticking to medium.
Edit: just noticed it's spawning subagents in 'high' thinking mode.
UPDATE: it is now available in chatGPT account also, they rolled it out
Also, confirmed it works for me by using --model gpt-5.6-sol
That being said, maybe 5.6 can fix that!
The base model is certainly cheaper and more token efficient etc, but on large tasks cost in some way is now n^2
I think that's the way to go most of the time
Used a reset, it went for about 11 minutes and then, just out of usage popped up, no warning, there were still 4 agents running, the nice thing is it did let them finish, each one went for about 10 minutes.
I also have a claude max plan, I have been using Fable 5 on ultra, I never hit the session limit, and get 3 or 4 full day's looping on ultra.
I don't know how it handles the subagents, but claude does it much more efficiently, Codex does seem much faster, so maybe it's just a relativity thing.
this seems very interesting
Parameter: reasoning_effort
Function tools with reasoning_effort are not supported for gpt-5.6-sol in /v1/chat/completions.
To use function tools, use /v1/responses or set reasoning_effort to 'none'.'
I hope we aren't trying to push customers off the chat completion endpoint... Responses endpoint looks great on paper, but the business wants more visibility and control over the reasoning process than this product currently offers.
Edit: This is broken in my VS copilot setup too.
E.g. for GeneBench Pro, it looks like you would always use GPT-5.6 Sol over Terra/Luna, its pareto optimal.
For Agents Last Exam, you would maybe want Luna, then Terra, then Luna, then Sol as you increasingly budget for tasks.
I feel that there may need to be a new auto mode in many of these cases. It selects the best model and thinking given a particular problem.
Feels like it's going to have to go that way eventually, because here we have about 20 different model and thinking levels you could use, and they're not obvious which ones are right for the given use case.
it's impossible to _try_ it out on release!
it's not on their codex subscription, or the web/mobile chatgpt interfaces, or aws bedrock, etc. I just cant find a working endpoint with the latest model after they announce
I started up Codex CLI fresh. That version of Codex was 1.42.5. 5.6 wasn't in the models list.
After I updated Codex to a newer version (0.144.0), 5.6-terra and -luna appeared in the models list (but not 5.6-sol).
(It's impossible for me to know whether updating was causative or just correlative, but that's the timeline I experienced.)
> GPT-5.6 Sol’s detected cheating rate was higher than any public model we have evaluated
Fable had issues with the sourcing and organizing images, and shoot itself at foot looking for shortcuts as usual. As I was getting it fix these back and forth, I copied my prompt and gave it to Sol.
Sol has surpassed my expectations by far. With a one shot simple prompt on a complex task, it gave me a working web app with everything I want with minor issues to track and fix.
GPT-5.6 didn't try to fix the bugs (as instructed) but it did surface them, which is something that didn't happen with GPT-5.5. When spec/plans approved Fable sends back to GPT-5.6 for ralph implementation and it seems to be an even faster, more reliable workhorse than it already was in GPT-5.5. Overall, impressed. Will continue to be a core piece of my workflow.
Even worse, it's not a fair comparison: they purposefully just used "adaptive" instead of "max" for Fable.
What about the graph looked so unreal to you?
We agree models should be compared on a fair basis. Unfortunately, adaptive was the only publicly available number. Anthropic doesn't generally let us run their models for evals, so we rely on whatever Anthropic or third parties have published. In this case, the Agents' Last Exam leaderboard has Fable Adaptive, but not Fable Max.
https://agents-last-exam.org/leaderboard
Would have loved to publish a full curve for Fable if anyone makes the data available.
Although we do bias toward publishing evals where we're ahead, we have historically been unafraid to publish evals where we're behind (e.g., GDPval). The point is give people useful information to decide what's best, not to trick people.
Edit: Now I see there's a second entry with xhigh effort. Not sure if that was added or recently or we skipped it.
(I work at OpenAI.)
AGI solved
While in the past months Composer 2.5 was a lot better than I had expected a year ago, and the GPT 5.6 family does a good job in terms of cost for performance, I wonder why nobody is talking about Grok 4.5 high? Those numbers look very convincing to me.
I don't know about those numbers, even assuming this was by mistake :)
Other than that, I think the difference between 5.5 and 5.6 will be the same as 5.4 and 5.5. 5.5 is just less frustrating to use, although not perfect and still has derp moments. But a lot less than 5.4.
So I expect 5.6 Sol to be smoother to use. But so far it just feels slower. We'll see.
This kind of "out of bounds" image analysis seems to be a very difficult problem to solve, but totally necessary for transformers to really bring about massive change.
I’m explicitly telling it to do something extremely specific and it’s just not listening to me.
Eg, I gave it an image to update. The image is sized 400x200 pixels. It then generates a new image at 300x300. I explicitly state to be 400x200 in size and it won’t listen.
Sounds great.
Also latency looks very good.
I follow a sort of waterfall workflow which is verbose but fully transparent.
Anthropic’s $100 subscription works fine for me, but whatever subscription my company has with OpenAI reaches the 5hr limit ridiculously quickly.
Do you link one with the other as a skill or mcp or so?
That leads me to a question. Why wouldn’t they just default to terra in ChatGPT in the last few months? If they didn’t then they burnt money for no reason by giving a shittier model at a higher price
..on some specific set of benchmarks ;)
Fable or Opus would wander and wander.
Am I way off base? Seems astronomical.
Or ... maybe Gemini 4 is too good and the NSA is using it to break into systems worldwide ...?
It had previously been announced (at Google I/O) to be released in June, but it got pushed back. Why? Rumors range from they are having trouble to they scrapped an old framework to pursue a new one and the new model will surpass Fable 5 and be cheaper.
> That advantage extends across the family: Terra performs just above Fable 5, while Luna outperforms Opus 4.8; each does so in roughly one-third of the time, with about half as many output tokens, and at approximately one-quarter the estimated cost.
Wow. I don't believe it. Every indication and twitter post told me that Fable is much more intelligent than Sol and here we are told that even Terra outperforms Fable?
Not only that, Sol doesn't even come with run time classifiers. So it is even more suspicious.
What's even stranger is that OpenAI is directly referencing a competitor in this direct way.
I wonder how long model size and effort will be a few discrete points instead of continuous.
UPD from announcement: "The rollout is starting globally now and will continue gradually toward full availability over the next 24 hours."
Its an extremely capable model. I think the way we need to approach works shifts again. We need to get our harnesses/workflows to let it gather some momentum on the first couple rounds but then we also need to structure it so that it can slingshot and accomplish the long range goal.
Switching next month. Looking forward to working with Sol.
> GPT‑5.6 is priced per 1M tokens across three model sizes: Sol is $5 input / $30 output; Terra is $2.50 input / $15 output; and Luna is $1 input / $6 output.
Just as expensive as Fable 5. But of course, another slot machine upgrade but the costs will keep going up and the open weight models from china will continue to race everyone else to $0.
Looking forward to the next version of GLM, Qwen, Deepseek and Minimax.
Really wanna see it in DeepSWE benchmark
At work, it was able to one shot a dashboard. Of course, my prompts are vague as I'm not exactly sure what I want yet, but it did a better job than I could do as a backend dev forced to work on frontend sometimes.
Usage is also great, it just feels so much more efficient than older models in terms of thinking and time. Cost is barely better though.
It can burn a million tokens in less than a minute, at least at launch where there's likely less load on the servers.
At home, it feels like I'm fighting the AI less while letting it refactor code. I'm glad that I left this 12,000 line vibe coded port of a hand written codebase to future models to refactor. It feels like the model has better judgement than old models that would destroy your codebase so long as it meant accomplishing your prompt.
I'm almost disappointed that it's this good.
> Reload to try again, or go back.
This on iOS, safari
The timescale is typically hours not minutes, so if you don't see it now, I'd try again later today.
We mention it will be a gradual rollout over the next 24 hours in the Availability section at the bottom of the blog but I admit it's pretty buried.
(I work at OpenAI.)
https://github.com/openai/codex/issues/30364
"GPT-5.5 Codex reasoning-token clustering at 516/1034/1552 may be leading to degraded performance on complex tasks"
Great to read they are moving away from the 5 minute cache defaults. Hopefully other providers follow soon!
https://openrouter.ai/openai/gpt-5.5?endpoint=58e5b336-423e-...
vs
https://openrouter.ai/openai/gpt-5.6-sol?endpoint=a54c5de0-8...
The good news you don't have to send your dollars to China to fund ai dictatorship, in russia, north korea, african countries and south america.
Grok 4.5 is interesting because it's smart enough at great price. It seems gpt 5.6 is right there with great efficiency and great pricing.
Working with Fable has been a great experience, but at the end of the day, if you can get only 10% of your work done because it just burns through tokens, that's not that interesting.
I've been mostly using Opus and Fable high for planning and codex 5.5 medium for implementations. Claude is also the only model i can use for design tasks. If gpt 5.6 can finally deliver on the design side, it might be time to ditch the Claude sub and go full Gpt.
for one thing, they said that on AA, sol is "within one point of fable" at 58.9 vs 59.9 but don't clarify that the latter is with safeguards where ~8% of the tasks got routed to opus
i'm not rooting for either and genuinely think that the token efficiency and cheaper price are important but this sort of thing just feels disingenuous :-/
At least give me the article ffs.
SWE-Bench Pro Sol: 64.6% Fable: 80% Opus: 69.2% (!!!!)
So, it still trails Opus, significantly, and is not a next-gen coding model like Mythos/Fable 5.
Disappointing to say the least, but somewhat expected.
But anyway, I think it's pretty useless to look at SWE Bench's now when other way better benchmarks exist.
yeah that was the point of introducing the Pro version
OpenAI no longer recommends SWE-Bench-Pro as a benchmark: https://openai.com/index/separating-signal-from-noise-coding...
Open weight models being 10x or more cheaper is just so much more of an unlock than incremental gains for me.
Never go over the free limits in Gemini Pro.
Gemini is great at research and architecture, and my 30 years experience in programming everything; for fun or work; means together there is little to no code slop.
Add to project repo some git submodules of reference source code; boom, bobs your uncle
Zero reason to sign up for OAI or Claude. With employers realizing the costs are more than employees, local models getting more powerful, and models in chips just a few years out, neither of the one note LLM companies without diversified services and R&D portfolios gonna last
15 hits
Holy shit. They must be feeling very threatened by Fable if they're spending this much energy talking about it in the release notes for their own model.
I don’t believe it at all and I don’t think anyone else does either.
gemini - 13 hits
opus - 18 hits
So they are more threatened by opus than fable, or are they almost as threatened by gemini as they are by fable?
Including a human in the loop does not excuse the fact that AI was trusted in a process that decides who lives and dies.
ChatGPT 6 must be deep in the pipeline and will be released within the next few months. Maybe that's why this release is versioned 5.6, not 6.0.