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O3 Turns Pro

thezvi.substack.com

164 pointsjsnider3132 comments

I'm using Pro. It's definitely a "hand it to the team and have them schedule a meeting to get back to me" speed tool. But, it "feels" better to me than o3, and significantly better than gemini/claude for that use case. I do trust it more on confabulations; my current trust hierarchy would be o3-pro -> o3 -> gemini -> claude opus -> (a bunch of stuff) -> 4o.

That said, I'd like this quality with a relatively quick tool using model; I'm not sure what else I'd want to call it "AGI" at that point.

what do you trust it to do?

the only example uses I see written about on HN appear to basically be Substack users asking o3 marketing questions and then writing substack posts about it, and a smattering of vague posts about debugging.

Long form research reporting.

Example: Pull together a list of the top 20 startups funded in Germany this year, valuation, founder and business model. Estimate which is most likely to want to take on private equity investment from a lower mid market US PE fund, as well as which would be most suitable taking into consideration their business model, founders and market; write an approach letter in english and in german aimed at getting a meeting. make sure that it's culturally appropriate for german startup founders.

I have no idea what the output of this query would be by the way, but it's one I would trust to get right on

* the list of startups

* the letter and its cultural sensitivity

* broad strokes of what the startup is doing

Stuff I'd "trust but verify" would be

* Names of the founders

* Size of company and target market

Stuff I'd double check / keep my own counsel on

* Suitability and why (note that o3 pro is def. better at this than o3 which is already not bad; it has some genuinely novel and good ideas, but often misses things.)

This is all stuff I would expect an LLM to "hallucinate" about. Every bit of it.

These tools can search the web to find this kind of data, and show you what they searched. Double checking is essential because hallucinations are still possible, but it's not like in the past where it would just try to make up the data from its training set. That said, it also may find bad data and give you a summary of that, which isn't a direct hallucination, but can still be inaccurate. This is why checking the sources is helpful too.

I wouldn't expect it to hallucinate but how do you evaluate it's ability to distinguish spam from good info? I.e. the "the first four pages of google results is all crap nowdays" problem.

By looking at the pages it looked at and deciding for yourself, just like you would with a web search you invoked yourself. I’ve generally found it to use trustworthy stuff like Stack Overflow, Wikipedia, and university websites. But I also haven’t used it in this way that much or for very serious things. I’d imagine more obscure questions are more likely to end up involving less trustworthy sites.

>By looking at the pages it looked at and deciding for yourself, just like you would with a web search you invoked yourself.

Or you could just cut out the middleman(bot) and just do the search yourself, since you're going to have to anyway to verify what the "AI" wrote. It's just all so stupid that society is rushing towards this iffy-at-best technology when we still need to do the same work anyway to verify it isn't bullshitting us. Ugh, I hate this timeline.

You could. What's correct depends on your needs. Search engines aren't useless because you have to make sure that the pages they return to you are quality, and so neither is using an agent in this way.

This is why I won't use these "AI" tools, because they obfuscate the bullshit. Since I have to check what it said anyway, it's easier if I just do a search and see where the info is coming from, and I can tell good from bad sources really easily. Putting an LLM in front off it is like polishing a turd - garbage in, garbage out.

I recently tried a version of this landscape analysis within a space I understand very well (CA college access nonprofits) and was shocked at how few organizations were named, let alone described in detail. Even worse, the scope and reach of the named orgs were pretty off the mark. My best guess is that they were the SEO winners of the past.

Well you’d be wrong in this case: Deep research will trigger a series of web searches first then reach out to tooling for follow ups as needed; most of the facts will be grounded in the sources it finds.

With no deep research - agreed; too recent to believe info is accurately stored in the model weights.

why would you trust it to get any of that right? things like "top 20 startups in Germany" sound hard to determine.

how do you validate all of that is actually correct?

A lot of stuff doesn't need to be accurate, it just needs to be enough information to act on.

Like how there's a ton of psychics, tarot and palm readers around Wall St.

That’s fine, but no one - not Sam Altman, not the fans on HN - are promoting them as $120/million token clairvoyants, they’re claiming they are srs bzns “iq maxxing” research tools.

If OP had suggested that they were just medium-quality nonsense generators I would have just agreed and not replied.

I’ve been using it in my job search by handing it stuff like the hn whose hiring threads, giving it a list of criteria i care about, and have it scour those posts for matching jobs, and then chase down all the companies posting and see if they have anything on their corporate site matching my descriptions.

Then I have it take those matches and try and chase down the hiring manager based on public info.

I did it at first just to see if it was possible, but I am getting direct emails that have been accurate a handful of times and I never would have gotten that on my own

This is a good data point - I guess another dimension is incompleteness-tolerance. An LLM is absolutely going to miss some but for your case that doesn’t matter very much.

Thank you!

I don't think it's necessarily a question of trust, it's a question of cost/benefit, and I can apply this just as much to myself. I have been using a lot more SQL queries lately when I use ChatGPT, because I trust it pretty well to write gnarly queries with subqueries and CASE statements. Things that I wouldn't write myself because it's not worth the time to make the query correct, but ChatGPT can do it in seconds.

I had an example where o1 really wowed me - something I don't want to post on the internet because I want to use it to test models. In that case I was thinking through a problem where I had made an incorrect mathematical assumption. I explained my reasoning to o1 and it was able to point out the flaw in my reasoning, along with some examples mathematical expressions that disproved my thinking.

The funny thing in this case it basically functioned as a rubber duck. When it started producing a response I had deduced essentially what it told me - but it was pretty nice to see the detailed reasoning with examples that might've taken me a few more minutes to work out. And I never would've produced a little report explaining in detail why I was wrong, I would've just adjusted my thinking. Having the report was helpful.

Are you setting the "reasoning effort"? I find going from the default (medium) to high makes a big difference on coding tasks for openai reasoning models.

what/how does that work internally?

https://learn.microsoft.com/en-us/azure/ai-services/openai/h...

It's just the number of tokens it's willing to expend in the little internal dialogue before it has to spit out an answer.

What are you using it for? It's not like that wouldn't matter.

With coding using anything is always a hit and miss, so I prefer to have faster models where I can throw away the chat if it turns into an idiot.

Would I wait 15 minutes for a transcription from Python to Rust if I don't know what the result will be? No.

Would I wait 15 minutes if I'd be a mathematician working on some kind of proof? Probably yes.

I feed most of my questions/code to 4o, Gemini, o3-pro (in that order). By the time I’ve read through 4o, Gemini is ready. Etc.

It’s the progressive jpg download of 2025. You can short circuit after the first model which gives a good enough response.

How do you reason about the energy consumption/climate impact of feeding the same question to three models? Im not saying there is a clear answer here, would just be interesting to hear your thinking.

It's a tough question and I do things the same way.

I feel like we are in awkward phase of: "We know this has severe environmental impact - but we need to know if these tools are actually going to be useful and worth adopting..." - so it seems like just keeping the environmental question at the forefront will be important as things progress.

I agree, we are totally in an exploratory phase. I think its always a little scary when so much money and big companies are involved though.

How much energy does an AI model use during inferencing versus a human being?

This is a rhetorical question.

Sure we aren’t capturing every last externality, but optimization of large systems should be pushed toward the creators and operators of those systems. Customers shouldn’t have to validate environmental impact every time they spend 0.05 dollars to use a machine.

I actually did the math on this last year some time. For gpt4 or so. Attempted to derive a per-user energy use value. Based on known data LLM training used many hundreds of times the energy use of agriculture and transport costs to feed a human to do equivalent mental work. Inference was much lower. But the climate critique of AI doesn’t distinguish.

100x more inefficient than a human in only food is pretty efficient. Consider that humans in the developed world spend far more in energy on heating/AC, transportation, housing, lawn care, refrigeration, washers and dryers, etc, and an LLM can probably be several factors more efficient.

I don't really understand the critique of GPT-4 in particular. GPT-4 cost >$100 Million to train. But likely less than 1 billion. Even if they pissed out $100 million in pure greenhouse gases, that'd be a drop in the bucket compared to, say 1/1000 of the US military's contributions

That sounds on the low side?

Does that "hundreds" include the cost of training one human to do the work, or enough humans to do the full range of tasks that an LLM can do? It's not like-for-like unless it's the full range of capabilities.

Given the training gets amortised over all uses until the model becomes obsolete (IDK, let's say 9 months?), I'd say details like this do matter — while I want the creation to be climate friendly just in its own right anyway, once it's made, greater or lesser use does very little:

As a rough guess, let's say that any given extra use of a model is roughly equivalent to turning API costs into kWh of electricity. So, at energy cost of $0.1/kWh, GPT-4.1-mini is currently about 62,500 tokens per kWh.

IDK the typical speed of human thought (and it probably doesn't map well to tokens), but for the sake of a rough guide, I think most people reading a book of that length would take something around 3 hours? Which means if the models burn electricity at about 333 W, they equal the performance (speed) of a human, whose biological requirements are on average 100 W… except 100 W is what you get from dividing 2065 kcal by 24h, and humans not only sleep, but object to working all waking hours 7 days a week, so those 3 hours of wall-clock time come with about 9 hours of down-time (40 hour work week/(7 days times 24 hours/day) ~= 1/4), making the requirements for 3 hours work into 12 hours of calories, or the equivalent of 400 W.

But that's for reading a book. Humans could easily spend months writing a book that size, so an AI model good enough to write 62,500 useful tokens could easily be (2 months * 2065 kcal/day = 144 kWh), at $0.1/kWh around $14.4, or $230/megatoken price range, and still more energy efficient than a human doing the same task.

I've not tried o3*, but I have tried o1, and I don't think o1 can write a book-sized artefact that's worth reading. But well architected code isn't a single monolith function with global state like a book can be, you can break everything down usefully and if one piece doesn't fit the style of the rest it isn't the end of the world, so it may be fine for code.

* I need to "verify my organisation", but also I'm a solo nerd right now, not an organisation… if they'd say I'm good, then that verification seems not very important?

I totally agree that the environmental cost SHOULD be pushed towards the creators, but as long as that doesn't happen, is the moral thing as a consumer to just carry on using it? This is not a rhetorical question.

Transporting something with a car using fossil fuel usually uses less energy than if a human did the same thing by hand, that doesnt mean fossil fuel is environmentally friendly. LLM:s does not decrease the population even if it can do human tasks. If the LLM is used for the good of humanity it is probably a win, but I mean obviously a lot of the use of AI is not.

I use LLM:s as well, I'm just saying, I dont think it is a totally strange question to ponder over the energy use of different use cases with LLM:s.

The same way you might reason about the climate impact of having a youtube video on in the background I expect.

I don’t have nearly a luxurious enough life for that to be a blip on my radar of concerns.

I don’t think about it at all. When they cost money we’ll care more about it.

Until then, the choice is being made by the entities funding all of this.

Likely how you reason about driving to the beach or flying to a vacation destination. Or playing a game in 4k high quality with ray tracing turned on.

Yeah, I try to avoid those things as much as possible. Eight years since I was last on an airplane. I'm not sure how that is relevant to my question?

Do you have any specific tooling for querying all three at once beyond just copy paste?

open-webui has support for doing this- https://imgur.com/a/XUEVgCT

OpenRouter lets you send the same prompt to several models.

https://github.com/caiyongji/ChatMultiAI

Ideally it should be able to do things outside of the realm of programming with strong reliability (at least as strong as human experts), as well as be able to pick up new skills and learn new facts dynamically.

The time o3 pro takes is so annoying. I still need some time to get used to that.

I haven't tested o3-pro yet enough to have a good hierarchy of confabulation.

I use AI a lot to double check my code via a code review what I've found is

Gemini - really good at contextual reasoning. Doesn't confabulate bugs that don't exist. Is really good at finding issues related to large context. (this method calls this method, and it does it with a value that could be this)

Sonnet/Opus - Seems to be the more creative. More likely to confabulate bugs that don't exist, but also most likely to catch a bug o3 and gemini missed.

o3 - Somewhere in the middle

Hmm, Gemini + O3 > Claude-Opus for ... what kinds of things?

> That said, I'd like this quality with a relatively quick tool using model; I'm not sure what else I'd want to call it "AGI" at that point.

Am I the only one who is looking around at the AI industry and seeing only developers and artists being replaced with AI?

It's hardly AGI when it can't replace a salesperson, or an accountant, or a lawyer, or a teacher, or ...

All the headlines I am seeing are software development related. In this post, you yourself are using s/ware development as a measure for how good/bad AI is.

I haven’t tried pro yet but just yesterday I asked O3 to review a file and I saw a message in the chain-of-thought like “it’s going to be hard to give a comprehensive answer within the time limit” so now I’m tempted

Chat just isn't the best format for something that takes 15-20 minutes (on average) to come up with a response. Email would unironically be better. Send a very long and detailed prompt, like a business email, and get a response back whenever it's ready. Then you can refine the prompt in another email, etc.

But I should note that o3-pro has been getting faster for me lately. At first every damn thing, however simple, took 15+ minutes. Today I got a few answers back within 5 minutes.

Don't you chat with someone over various apps, but then get a notification minutes, hours, or days later? Email is just an instance of chat?

So I disagree. Are you building an AI agent accessible over email?

Here's how I view it:

Chat is more of a "conversational" medium, where you dash off short and informal messages and expect a very quick if not immediate response.

Email is more of a "correspondence" medium, where you write longer and sometimes much more formal messages, and expect a response within ~24 hours.

The latter would be the better way to use ultra-compute-heavy/long-delay models like o3-Pro, because you don't expect the flow of a conversation, and it would nudge you to be much more detailed in your prompt. In fact, if it had a larger context window, an email-format o3-Pro would be perfect for lawyers and for certain types of data analysis.

I'm not building anything, but if you'd like to, lmk.

I’ve found throw the problem at 3 o3 pros and have another one evaluate and synthesize works really well

So like, a whole forest of trees per query is what we're saying here?

Ideally just a few split atoms

Now You’re Playing With Agent Power!

when o3 pricing dropped 80%, most wrote the entire model family off as a downgrade (including me). but usage patterns flipped people finally ran real tasks through it. it's one of the few that holds state across fragmented prompts without collapsing context. used it to audit a messy auth flow spread over 6 services. didn't shortcut, didn't hallucinate edge cases. slow, but deliberate. in kahneman terms, it runs system 2 by default. many still benchmark on token speed, missing what actually matters

This feels very Ai generated.

Some people write in similar ways yea. I've also been accused of writing as an AI.

But we're still human mate.

Stop discriminating or actually solve the problem. I've had enough of this attitude.

almost as though the AIs were trained on a corpus of text written by... humans

yes im agi by the way

hi agi we’ve been trying so hard to find you

I would say the opposite. Unless the person has a lot of custom instructions going on. Getting sentences like "but usage patterns flipped people finally ran real tasks through it." seem like it would take some amount of work.

Feels like a lot of software engineers I work with (including myself at times).

Short, concise statements that don't necessarily string together sequentially. However, they still aggregate to a holistic, meaningful thought. No that much different that how a lot of code is written.

Actually, it does not.

Really doesn't to be fair and I feel like I spot so many AI comments every day.

I have been using o3 almost exclusively in Cursor now for my "vibe coding" project. I was able to get to a point with faster models before hitting a thrashing problem of forgetting about structure/not updating types/no using right types/ignoring existing functions, etc. Even when providing specific context. o3 rarely hits those issues and can happily implement a fully feature without breaking anything that touches multiple files. Speed is definitely an issue, but much less hassle on the back side.

Did you do anything interesting to start using it? I checked o3 when the price dropped and it kept falling basic tasks. ¾ of command runs and edits failed. Claude and Gemini both worked just fine with the same prompts.

Yes, my codebase has gotten bigger and I hit a rut where every change was blowing up one way or another. The fix was to create a few Cursor rules files. You can have Cursor make this - one to define the overall structure of the project, one to define where dataclass types are (I have a Python codebase), a few different ones for generic patterns for what needs updating when adding new code paths. In Cursor you can also select when Cursor consults these files, every time or when doing specified changes.

This has made the biggest difference with o3. If I want to "add a new module" I first explain the goal and have it create a plan (no code). Then I have it save that plan to a TODO.txt. And I just have it start implementing it. I spot check the code along the way to make sure its going down the correct path. I run a few "continue implementing" until it has created tests that pass. Then I test things myself. If all that checks out, I actually look at the code. Most of the time now everything is functioning and I have just a few minor tweaks.

Because I'm working on a new project I let some things stay a little wrong because I know I can focus on an across-the-board solution to it later, with a single prompt. The most important thing is to have everything well-typed and well-structured. LLMs thrive in structure.

I didn't mean the finer details of what's produced. I just can't get o3 to reliably edit files or produce terminal commands. I get lots of errors in the Cursor UI itself, saying something like invalid command, which the agent seems to retry on multiple ways, failing each time.

> Arena has gotten quite silly if treated as a comprehensive measure (as in Gemini 2.5 Flash is rated above o3)

> The problem with o3-pro is that it is slow.

well maybe Arena is not that silly then. poorly argued/organized article.

What is the difference between o3 pro and deep research? From a glance, both seem to take 10-15mins to respond and use o3 as the base model.

I've tried o3 Pro for my use cases (parsing emails in the legal profession) and didn't have better results than the non pro.

In fact, o1-preview has given me more consistently correct results than any other model. But it's being sunset next month so I have to move to o3.

IMO 4o is much better at people-parsing. The reasoning models o1-pro / o3-pro are really good at writing code and solving algorithmic problems.

what is people parsing?

The email from the lawyer might mention lots of names. Who are the plaintiffs, who are defendants, their attorneys, assistants, or insurance adjusters. The model parses out who is who and connects names to titles to email addresses.

Interesting, that's what I thought it meant, but didn't realize it was a term of art.

Things like inferring the meaning of “people parsing” when it isn’t explicitly defined but can be implied by context.

Not strict rational A+B=C, nuance.

I've tried it with various models. And 4o is really good given that it returns data at least 10 times faster. But if you ask it to fill out a Json document, o3 (or other reasoning models) is still better, more correct and predictable. Or at least, better enough to justify waiting a minute for the API call to return vs 3-5 seconds.

Out of interest, how widespread would you say this usage is amongst your peers in the legal profession?

ChatGPT is pretty widespread. The only obstacle in the past was the fear that confidential documents might be used for training. OpenAI fixed that with a business account type that guarantees no training.

As far as usage of API for business processes (like document processing) - I can't say.

Guarantees ...... How?

You should assume Facebook level morality.

I hear you. But the in-house lawyer read and approved the SLA. So all asses are covered!

I use Claude Code a lot. A lot lot. I make it do Atomic Git commits for me. When it gets stuck and instead of just saying so starts to refactor half of the codebase, I jump back to commit where the issue first appeared and get a summary of the involved files. Those in full text (not files) into o3 pro. And you can be sure it finds the issue or gives a direction where the issue does not appear. Would love o3-pro as am MCP so whenever Claude Code goes on a "lets refactor everything" coding spree it just asks o3 pro.

> When it gets stuck and instead of just saying so starts to refactor half of the codebase

That's pretty scary.

Atomic Commits.

I put this into Claude.md and need to remind it every other hour. But yeah, you need to jump back every few hours or so.

Can you give an example what claude works on autonomously for hours? I only use the chat, maybe I’m just not prompting well, but I throw away almost everything claude writes and solve it in significantly less lines of code using the proper abstractions.

Yeah, so far, I've only seen cases where the work is extremely simple and using pervasively used libraries and solutions to create widely implemented solutions. Add something a little out there and things start to unravel.

currently i am coding a node/react/ts firebase app that allows dynamic multiagent workflows to automate content workflows (a workflow.json defines call this model and the pass this part of the output of that model to that model and then combine it with this model to do that)

my setup is claude code in yolo mode with playwright MCP + browser MCP (to do stuff in the logged i firebase web interface) plus search enabled.

the prototype was developed via firebase studio until i reached a dead end there, then i used claude code to rip out firebase genkit and hooked in google-genai, openai, ...

the whole codebase goes into google gemini studio (caus the million token window) to write tickets, more tickets and even more tickets.

claude code then has the job to implemt these tickets (create a detailed tasklist for each ticket first) and then code it until done. end of each tasklist is a working playwright end to end test with verified output.

and atomic commits.

i hooked anydesk to my computer so i can check i at some point to tell to to continue or to read Claude.md again (the meta instructions which basically tells it to not to fallbacks, mock data or cheat in amy other way.)

ever fourth ticket is refactoring for sinplicity and documentation.

the tickets mist be updated before each commit and moved to the do done folder only when 100 tested ok.

so yeah, when i wale up in the morning either magic happend and the tockets are all done. or it got stuck and refactores half the codebase. in that case it works for an hoor to go over all git commits to find out where it went wrong.

what i need are multiple coding agent which challenge each other at crucial points.

I have to ask a probably naive question - after the initial boilerplate/scaffolding is this actually any faster than just typing in the code you want? Or using the standard AI flow before these long task agents? It feels like you are juggling and bouncing async tools, doubling back on output, and constant trial and error to get things working.

I'm sure lots of code is being generated, but I do wonder about the effectiveness ratio of it when I read comments like above. Like there is a sweet spot after initial scaffold where its easier just to express yourself in code?

Sounds like you're doing the equivalent of Aider's architect mode (use one model for the reasoning, and another for the code changes).

I would encourage you to try it. It's generally (much) cheaper doing stuff in Aider, but if you're paying a monthly subscription and using it a lot, Claude Code may be cheaper...

I use O3-pro not as a coding model, but as a strategic assistant. For me, the long delay between responses makes the model unsuitable for coding workflows, however, it is actually a feature when it comes to getting answers to hard questions impacting my (or my friend's/family's) day to day life.

"'take your profits’ in quality versus quantity is up to you."

As mainly AI invester not AI user, I think profitability is great importance. It has been race to top so far, soon we see race to the bottom.

Right! We are in a sense lucky to be getting access to actual state-of-the-art models. Soon the actual model may be kept internal and the customers will get "good enough for solid ROI" distilled versions that can be hosted profitably.

Here are my own anecdotes from using o3-pro recently.

My primary use cases where I am willing to wait 10-20 minutes for an answer from the "big slow" model (o3-pro) is code reviews of large amounts of code. I have been comparing results on this task from the three models above.

Oddly, I see many cases where each model will surface issues that the other two miss. In previous months when running this test (e.g., Claude 3.7 Sonnet vs o1-pro vs earlier Gemini), that wasn't the case. Back then, the best model (o1-pro) would almost always find all the issues that the other models found. But now it seems they each have their own blindspots (although they are also all better than the previous generation of models).

With that said, I am seeing Claude Opus 4 (w/extended thinking) be distinctly worse at missing problems which o3-pro and Gemini find. It seems fairly consistent that Opus will be the worst out of the three (despite sometimes noticing things the others do not).

Whether o3-pro or Gemini 2.5 Pro is better is less clear. o3-pro will report more issues, but it also has a tendency to confabulate problems. My workflow involves providing the model with a diff of all changes, plus the full contents of the files that were changed. o3-pro seems to have a tendency to imagine and report problems in the files that were not provided to it. It also has an odd new failure mode, which is very consistent: it gets confused by the fact that I provide both the diff and the full file contents. It "sees" parts of the same code twice and will usually report that there has accidentally been some code duplicated. Base o3 does this as well. None of the other models get confused in that way, and I also do not remember seeing that failure mode with o1-pro.

Nevertheless, it seems o3-pro can sometimes find real issues that Gemini 2.5 Pro and Opus 4 cannot more often than vice versa.

Back in the o1-pro days, it was fairly straightforward in my testing for this use case that o1-pro was simply better across the board. Now with o3-pro compared particularly with Gemini 2.5 Pro, it's no longer clear whether the bonus of occasionally finding a problem that Gemini misses is worth the trouble of (1) waiting way longer for an answer and (2) sifting through more false positives.

My other common code-related use case is actually writing code. Here, Claude Code (with Opus 4) is amazing and has replaced all my other use of coding models, including Cursor. I now code almost exclusively by peer programming with Claude Code, allowing it to be the code writer while I oversee and review. The OpenAI competitor to Claude Code, called Codex CLI, feels distinctly undercooked. It has a recurring problem where it seems to "forget" that it is an agent that needs to go ahead and edit files, and it will instead start to offer me suggestions about how I can make the change. It also hallucinates running commands on a regular basis (e.g., I tell it to commit the changes we've done, and outputs that it has done so, but it has not.)

So where will I spend my $200 monthly model budget? Answer: Claude, for nearly unlimited use of Claude Code. For highly complex tasks, I switch to Gemini 2.5 Pro, which is still free in AI Studio. If I can wait 10+ minutes, I may hand it to o3-pro. But once my ChatGPT Pro subscription expires this month, I may either stop using o3-pro altogether, or I may occasionally use it as a second opinion by paying on-demand through the API.

> With that said, I am seeing Claude Opus 4 (w/extended thinking) be distinctly worse at missing problems which o3-pro and Gemini find. It seems fairly consistent that Opus will be the worst out of the three (despite sometimes noticing things the others do not).

I've found the same thing. That claude is more likely miss a bug than o3 or gemini but more likely to catch something o3 and gemini missed. If I had to pick one model I'd pick o3 or gemini, but if I had to pick a second model I'd pick opus.

It's also seems to have a much higher false positive rate where as gemini seems to have the lowest false positive rate.

Basically o3 and gemini are better, but also more correlated which gives opus a lot of value.

For the code review use case, maybe can try to create the diff with something like `git diff -U99999`, and then send only the diff.

I feel really sorry for anyone using o3. It is really, really bad...

not being ironic I would rather commit seppuku than use o3

> My experience so far is that waiting a long time is annoying, sufficiently annoying that you often won’t want to wait.

My solution for this has been to use non-reasoning models, and so far in 90% of the situations I have received the exact same results from both.

On the complete other end of the spectrum, I found deep research (whether it's actually performing searches or not) to be a significant upgrade in quality. But you need to be cool with having to wait 15-30 minutes. It's certainly not for everything, but definitely worth trying.

It tends to output significantly longer and more detailed output. So when you want that kind of thing- works well. Especially if you need up to date stuff or want to find related sources.

Deep research is very cool, no doubt, but run it on a problem space you are familiar with and you will see the shortcomings.

Anytime I do my own “deep” research I like to then throw the same problem at OpenAI and see how well it fares. Often it misses things or gets things subtly wrong. The results look impressive so it’s easy to fool people and I’m not saying the results are useless, I’ve absolutely gotten value out of it, but I don’t love using it for anything I actually care about.

I view the results more as a starting point than an end unto itself. For that I think it's pretty useful.

Same, it will pull enough sources together that I end up with an idea of where to go next.

Absolutely, I agree it's useful as a starting point, sometimes it's all I need (if it's low-stakes and I just wanted a bit more data). I was just cautioning "trusting" it completely, since it's very easy to fall into that trap (I've done it).

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