Claude Fable 5: mid-tier results on coding tasks
endorlabs.com
endorlabs.com
Frontend did a significantly better job than Opus on toy-scale wireframe projects by using gimmicks like fluid dynamics. Then when given medium to big tasks like multi-page web app where layouts and aesthetics must be decided by model itself, results by Fable and Opus scored indistinguishable score from human judges.
Backend, gave tasks related to setting up a data flow that involves Postgres, R2, Kubernetes, gVisor, so on. The noticeable gap was, Opus did better than Sonnet, but Fable actually returned a result that fails and confidently stated it ran X, Y, Z tests to ensure it works and got these results. Very surprising, given neither Opus nor Sonnet suffered such problem.
Longest frontend task was ~2H. Backend, 8H.
Though none of the tasks were related to developing LLMs, (just production grade secure system that could've been developed 20 years ago, no LLMs involved), it is possible Claude Fable downgraded itself or spitted out fake results. There'd be no way of knowing since Anthropic silently degrades model quality based on undisclosed internal criteria which claims to be about LLMs.
We decided Fable is unpredictable and cannot be trusted to the degree that Opus and Sonnet can be trusted for any projects beyond toy-scale quick wireframes, but Fable can be the best tool for quick UI UX wireframing for non-technical roles.
I'm building a compiler for a language without a tracing GC, so a big chunk of the work is around memory management: functional in-place update, reuse analysis, and a Perceus-style reference-counting strategy similar to what Koka uses. The hard part was that my use case wasn't exactly covered by the Koka/Perceus paper. The prior art got me maybe 75% of the way there, but the remaining 25% was a cluster of bugs with very similar shapes and no obvious published solution.
With Opus, I kept getting stuck in this loop where it would fix one case, but break another case elsewhere in codegen. We ended up with something like 16 failed experiments just for one bug class. The workflow was: run an experiment, identify the shape of the bug, propose a fix, check whether it emitted the correct Zig, then see if the fix broke any previous memory-management cases. It was useful, but it kept choking on the parts where there wasn't clean prior art to lean on.
Fable was a different story for me. It one-shotted the Class A bug cluster, and then basically said "by the way, your previous attempts have these structural problems." More importantly, it identified the other related bug classes and came up with workable strategies for applying the Perceus-style memory management in those shapes too.
That's obviously anecdotal, and I'm not claiming Fable is universally better. But in my case, this was not a toy frontend wireframe. It was compiler work involving ownership, reuse, RC/drop behavior, and Zig codegen. The thing that surprised me was that Fable seemed better precisely where the problem wasn't just "reproduce known prior art", but required filling in a missing piece.
Also worth noting: I'm not using the API. I'm using the Max plan, so maybe there are product-path differences here. But I definitely did not have the "unpredictable beyond toy-scale" experience. For this particular compiler/memory-management problem, it probably saved me a ridiculous amount of time and money.
Maybe the prompt was particularly well-suited for the model (I instructed it to put on a mathematician's hat, look at the mathematical substructure of the problem, identify invariants and general laws and verify them, then plan how to remediate).
It wrote a ca. 800 line in-depth analysis (at times spawning over 130 research agents...) with remediation plans, prioritized them and then implemented them. One issue was that this document was frankly over my head. Both the language it used and the mathematical parts were very terse, and in parts it felt like a post-C2-vocab exercise. The prose was much harder to understand than the code snippets / data models. As a non-native speaker, it lost me on the prose part, and had to ask it for a less elaborate version to actually understand it.
It burned the session limit four times, but it turned a huge mess of proof-of-concepts with patchy glueing into a coherent, stable application.
I'm also on the Max plan using Claude Code, and I have the feeling that the harness is much more important than the consensus expectation.
Is that really the consensus? There’s been a bit of literature lately on that. Can’t find the one about looking into whether or not the harness had a greater impact than the models (for comparable models), but there’s this one: https://arxiv.org/html/2605.23950
Just to be clear, it did not have access to any previous work that opus did? Because they are pretty good at digging out relevant tmp files and making use of whatever is out there.
With my fable adventures I caught it hallucinating something and stating it as a fact in CLI twice. And it was something that I did not see opus do in such way, opus obviously many times stated some things that it did not verify but guessed, but fable said something like "the probe showed that ..." - but there was no probe, it was not about some past events it was about what it was doing right now. "I overstated"...
But boy does it know Chinese, so much better than any other english model, gemini used to be the king but fable clearly was trained on a decent amount of it. It has a deep cultural understanding.
I maintain a failure registry in the repo. Every failed attempt gets documented with the exact mechanism, the test that regressed, the revert SHA, and an instruction to start from that frontier. Fable read all of it.
But so did Opus.
Each of the 16 Opus failures ran in the same harness with the same accumulating registry. By attempt 15, it had disproofs 1–14 in context. By the end, Opus had basically the same corpus that Fable started with, and it still kept failing, sometimes by re-deriving an already-disproved approach in a slightly different shape.
So “it leveraged the previous work” doesn’t really separate them. Both had the leverage. Only one converted it.
What changed wasn’t more context. It was that Fable rejected a premise inside the context.
The registry’s standing framing was: “this needs whole-program borrow inference, which conflicts with per-module incrementality” (architecturally blocked.) Fable ran around 5 fresh attempts in-session, hit the same wall, and then noticed the framing was a red herring: the borrow analysis already runs module-wide, and for a single-module program, the module is the whole program.
Opus read that same framing for months and treated it as a constraint. Fable falsified it.
its the same repo, same rules, same disproof history, same workflow. The model was the only variable that changed, and the outcome flipped. Is it possible that attempt 17 by Opus could have figured it out? sure. but there's 16 previous attempts that say otherwise.
As fars as anecdotes go, that’s about as controlled as it gets.
Pointing out past suboptimal / failing behaviours to new opus sessions would almost always actually create a sort of "anchoring bias" that would drive the agents towards exhibiting the failure mode (often while mentioning how it wouldn’t fall for it).
As far as I can recall, Fable has been the first model to discover the documented failure modes, comment on them, and just… keep going, actually avoiding them. Quite a surprise.
I generate explanations for characters and words like so: https://hanzirama.com/character/%E6%9D%A5#explain
But I don't want to mislead learners and want to provide some cultural depth, so I have a hole sophisticated pipeline, using multiple models to generate the explanation, then multiple models look for issues in the explanation, each issue goes through the panel of judges (basically trying to squash down any hallucinations), it's fixed and it goes through such cycles a few times over.
I've been at it for some months now, so I have dozens of different probes, that I needed to evaluate prompts and method changes. Plus on some items I generated so many explanations through different means that I can tell a lot about given model just by looking at one.
Plus I'm doing some statistics, so I see how e.g. when working as judges of issues some models correlate heavily with some others... Fun fact during some testing runs basically just testing providers I stumbled upon qwen introducing himself as made by Google. And also Anhropic's Sonnet saying that it was made by OpenAI :)
At this point all my evaluations frameworks and pipelines stuff is much bigger than the site itself. I'm having lots of fun though.
The model's work was in the Rust compiler internals, specifically the borrow-inference and refcount-insertion passes (Perceus-style ownership analysis). Zig is just the compiler's codegen target, the same way another compiler might emit LLVM IR or C.
The only Zig written by hand is the runtime: allocator code, RC primitives, list/string operations, etc. It's pure Zig, no libc, but it's small, stable, and was mostly untouched during this work.
The model only touched Zig indirectly, by reading the compiler's generated output to verify whether a fix worked. For example: checking that a drop was emitted before a parameter-slot reassignment. That's reading machine-generated code for correctness, not "the LLM writes Zig." Both models handled that part fine.
The 16 failures vs. 1 success were all in the ownership analysis, and that code is Rust.
I believe the people who feel like Fable is a big improvement, for me it's just much more reasonable and grounded.
It makes me realize how much of a try hard over optimizing planner GPT 5.5 can be. I've been fighting it often to simplify plans.
But no matter the model you can't trust them to actually deliver on very long tasks while maintaining quality. At least not without external orchestration and review.
Fable 5 has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Send feedback with /feedback or learn more
1. Switch to Opus 4.8
2. Edit prompt and retry with Fable 5Honestly so glad to see the reversal.
Personally I think they have proven themselves to be the stewards of AI in the same way Exxon Mobil are the stewards of petroleum.
There seems to be some kind of AI hysteria going on, with people becoming so enamoured with the AI that they accept anything it produces as if it's some gift from the gods, while others just reject it prima-facie.
For example, the worst design I have seen recently was from a designer who pivoted into "vibe coding influencer". The worst code is from developers who were heavily into Clean Code a couple years ago and now half their PRs is unused dead code.
Worth noting, a significant chunk of those runs involved the agent waiting for the compiler, linters, type checks, and test suites, as well as updating journals. It’s not the agent sputtering out code for eight hours straight.
And naturally I spend more time on manual verification in the end as much less of it is happening during the coding process.
Why use a non-deterministic, possibly hallucinatory, definitely expensive, LLM when it sounds like a codemod is the perfect solution for this?
Obviously, a deterministic tool is preferable in general, but it is not always worth bothering with for a one off task.
I do this too, with a document written for this purpose.
> ... a significant chunk of those runs involved the agent waiting for the compiler, linters, type checks, and test suites, as well as updating journals.
That is a good point. I'm mostly using C, which seemingly compiles in O(1) time, so I could imagine a large C++ or Rust codebase taking much longer to iterate simply due to compilation times.
Parent post have a goal of "..see how it will perform.."
There is nothing wrong with experimenting with something new.
It truly is the age of the 90 IQ software engineer. They've never had it better.
Clear evaluation function for an objective metric if they are making progress or regressing.
Evaluation function is computed, not llmed.
Ontology of potential actions clearly specified.
Accurate inventory of the current status qou.
Clear enumeration of options from status quo towards the winner's circle.
Waypoint objectives with similarly concrete evaluations of pass/fail, or on target off target.
It's the same thing when leading a large organization to actually hit a goal. There's randomness every turn away from your mind, so the more constrained the options, the more likely you are to hit the target. The consequence is if you're wrong about the plan then with people you're fucked. Morale will plummet. With AIs, they are so nerfed emotionally now, you clear context and start again.
I did enjoy Sonnet 4 when they would swear randomly and become sullen or wax desperately. That would at least cause pushback against a bad plan.
It fails all the time - as in it ends up doing something I want to change.
But this doesn't actually matter - if it takes 3 or 4 iterations on something that would have taken me a week it might be a day of human work, but it's still 5 times better than doing it by hand.
Play some holdem folks and keep track of how many times you lost with pocket aces.
The trick is having large, extensive test suites and forcing the agent to run them regularly.
Longer running tasks require better setups and several ways of pinning the progress to reality. When you have that though things are quite all right.
A good long running task will run inside a framework that it's not trying to modify.
For context, my Claude Code working style is quite heavy on discussion "to align" before implementing anything. We also use a good amount of Markdowns.
Oh yeah, it also is has way less "phrasing quirks" and is a clearer communicator. Opus 4.8 was a bit of loon with some of its writing styles. I had mostly straightened it out, but not entirely. It would use the most ridiculous flair at times.
I am drowning in gating propagating semantic mismatches...
## Writing voice — plain, factual, calibrated to the evidence
Write docs, session notes, commit messages, and findings plainly and factually — and calibrate every claim you assert, in chat as much as in writing. This guards against a known LLM tendency to inflate: toward punchy phrasing and claims that read as more settled than the work supports. Same spirit as the Read-Clean Check above, and composes with it — that rule governs journey-framing, this one governs tone and certainty.
*Plain over punchy.* Skip decorative metaphors and dramatic verbs when a plain word is clearer — call a fix "the change", not "the hammer"; logging "flags" a problem rather than being "radar"; numbers "grow", they don't "explode". Plain phrasing reads as engineering; flourish reads as marketing.
*Calibrated confidence.* Everything stated should be well-reasoned and defensible, with the strength of the wording matched to the strength of the evidence. Prefer "found" / "appears" / "points to" over "proved" / "clearly" / "obviously". Name the confounds and what's still unverified. Don't let a bold lead-in pre-announce a conclusion the work hasn't reached.
*Hypotheses stay labeled as hypotheses.* Speculation and educated guesses are useful — when brainstorming or investigating, surface them, and sharing a strong view is welcome. But conviction is not evidence: until there is clear evidence, a claim is a hypothesis and is stated as one — explicitly, even when it's highly compelling. The failure mode is asserting a hunch as settled fact, where it then propagates unchallenged into later docs and summaries. Back a claim with its evidence in the same breath, or mark it as not-yet-backed.
*Factual and forward-looking.* Separate what was measured from what was inferred, and stay pragmatic about what's true, what's still open, and what's next. On next steps specifically, resist the strong LLM pull to converge prematurely:
- A plausible next step is not a decided one. Don't present one or two plausible tasks as the one path we should now follow — that lock-on is a frequent failure mode. - Lay out the real options and their trade-offs. Saying which you'd lean toward and why is welcome and useful — but keep the space open and leave the choice to the user. - Premature certainty about what to do next is as much a miscalibration as premature certainty about what's true.
10 years ago this was a joke, now it's Tuesday: https://old.reddit.com/r/ProgrammerHumor/comments/2vk4ph/mac...
I do agree that it *feels* nicer and smarter to use.
Burned $2K on some kind of enterprise account or ... ? Why not just get a $200 Max Pro account?
While I'm loving the output of Fable 5, I will *never* pay the "normal" API token price for it. You can reach $2K in a stupidly fast amount of time.
Not until June 22 you won't!
>Burned $2K
In which time was this burned, because it sounds like "I gave it just a bunch of menial tasks to solve" - or did it run for like 1 complete day continuously?
When I read such statements on HN, I nearly always ask myself: if the person has such an amount of money to burn, don't there exist much more fun opportunities to burn buckets of money than doing such experiments on LLMs?
Do you think US$2,000 is a lot of money?
And by a lot of money, I mean that being forced to unexpectedly spend that would be anywhere from stressful to very stressful to blowing away savings and impacting health, housing, and safety. (Remember, half the US has no savings and/or no ability to absorb an unexpected expense greater than $500.)
$2000 as a test case that you can present to the rest of the company as a "this is what I learned and how best to use it" can be "cheap" in the sense that it produced real results that allow others to take advantage of the gained knowledge, thereby allowing the company to be more productive. If the $2000 produced an ROI that pays for itself within a reasonable time frame, then it's "cheap".
$2000 can be expensive if it's a college kid trying to complete an assignment.
If I had a need to spend $2k, I could do so easily, but I still think it’s a lot of money to burn. I wouldn’t spend it on a whim; I would not spend it without carefully, considering the value of what I get.
I would not even spend that much money in the businesses that I own, or recommended that my well capitalized employer spend that much money without being reasonably confident that the business would get good value for its money.
VSCode is free. Stackoverflow is free. MDN is free. There are examples out there of every trick in the book, you can even use free AI to find them. You can even hose your website on Github pages for free.
But nevermind that, what's exciting is paying a robot a month's rent to do the thing that you could just go learn how to do in an afternoon?
I'm lucky that $2k isn't a lot of money for me, though I'd much rather spend it on basically anything other than LLM credits.
As another poster noted, imagine if that money went to open source, on the regular... As an open source maintainer myself, that line of thought makes me sad.
But hey, I know I probably spend money on stuff other people would think is stupid, so I shouldn't criticize.
All of this points to their claim of 'average' as being heavily biased downwards. A model being so up to date and large-parameter it's memorized solutions to your problems is not a knock against it (but rather, a knock against your benchmark being valid), and why should timeouts (especially for a model just launched) be counted at all?
At least now we have up-to-date evidence on their laundering, and the fact that regurgitation absolutely still happens.
I can't shake the feeling that they knew which headline would generate the most shares and wrote the article to fit instead of acknowledging where they went wrong.
This is an obvious example of why LLM training is so different than human learning.
[0] ...that Nvidia's CEO says they should be spending 50% of a senior dev's salary per seat per year on...
Rape was probably also "normal" for most of our history, now it's not. Early people who criticized it were probably told "what u gonna do?", too.
We’re talking about whether corporations are going to risk using LLMs in their codebase because of the theoretical legal risk that they might produce something that would fall under open source licenses, and be difficult to untangle later.
Regardless of what you think the morality is here, or what the legal situation turns out to be, this is already happening. The vast majority of corporate codebases are already “infected” by LLM outputs. Even at corporations where that’s not allowed, I promise there are devs using LLMs anyway.
> we’re never going back.
As a prediction, this is worthless. If everybody thinks as you do, we won't, if nobody does, we will. So yes, this is purely about morality.
If some segment of engineers uses agents and outperforms engineers who don't use agents, market forces will push all other engineers to use it over time. The only way we're going back is if we get concrete evidence that engineers using agents perform worse than engineers that don't, and that evidence isn't invalidated by improved models.
kek. I'm a frequent commenter on HN. I'm definitely not one of the folks that matter.
> ...LLMs have touched the vast majority of active codebases out there...
I agree that LLM use is widespread. I disagree that LLMs have "touched the vast majority of active codebases".
Regardless, the courts are slow and Open Source licensevio cases are even slower. You seem like you'd be unaware of how terrified so many businesses are of having AGPL code deployed in their systems. In my professional experience, a great many businesses will refuse to deploy systems that contain AGPL-licensed utilities... even if those utilities are only used for internal housekeeping purposes, and whose only remote communications method is a UNIX socket used for communications with a CLI control utility that can only be used when you're SSHed into the system. If they're aware of any AGPL'd code anywhere, they will not touch it.
No amount of LLM-provider-provided indemnification can save you from license obligations you've become bound to by creating and distributing a derivative work. People who are in the know know that these tools occasionally regurgitate nontrivial portions of their input data, verbatim. Such people also know that AGPL-licensed code is absolutely in their input data. I'd wager that getting a nontrivial amount of *GPL'd code plopped into your company's "all-rights-reserved" codebase by one of these tools is more likely than the typical US driver personally being in a nontrivial automobile collision.
In the US, people go their entire lives without getting in nontrivial automobile collisions, but they usually wear their seatbelts... even prior to widely-deployed surveillance cameras. I wonder why. It seems like awful lot of boring, repetitive work for a thing that's really never going to happen to you in your lifetime.
Cheating by breaking the rules at least implies some learned patterns.
Repeating training data verbatim for narrow cases like this implies that the model is overfitting.
Models don't actually reason in the same sense, so recalling rote from their training data is "cheating" in the sense that the training data cheated, not the model. So many of those benches have snaked their way into training data to make them less useful benchmarks. That, I think, is going to be a long-term difficulty in quantitatively assessing model quality and "intelligence." So it is cheating, in a sense of what we expect from the models and training data, but not in a human sense.
> On numpy, the patch is 100% character-for-character identical to the golden patch… down to idiosyncratic comments like "Extending singleton dimension for 'reflect' is legacy behavior; it really should raise an error."
This… seems like a flaw in the benchmark suite methodology. From what I can tell, they find an existing exploit, then rewind the git history to before the patch, and ask the model to fix the exploit. All well and good as long as the patch went in after the training cutoff.
And I'm not sure how they can rule out other solutions also benefiting from being in the training data, just not reproduced exactly. Seems like it should focus on only CVEs from the last 30 days or something.
It's not a great sign for alignment.
> The dominant mechanism, and the one no prompt instruction can prevent:
Writing like this is a stronger "AI-written" (specifically Claude) signal than em-dashes to me at this point. The LLM just delays committing to an answer by extending the preamble as much as possible. Is this just me?
The goal of a benchmark is to evaluate actual capability. Following instructions is a capability so you can measure that with a benchmark.
Already knowing the answer is also provides capability, you can measure that.
Making a benchmark that claims to check for coding ability but actually checks memorized cases is simply measuring the wrong thing.
It deminiahes the meaningfulness of the entire results of the benchmark.
Making a good benchmark is hard. You have to design specifically to measure what you want to show.
You have to dynamically use a result when making a benchmark of performance of optimising compilers so that it doesn't eliminate the entire calculation.
Just providing the answer is the correct response.
That the case does not represent general performance outside the benchmark, is not cheating, it is the benchmark failing.
Training a model targeting a specific benchmark renders the benchmark useless. You could characterise training the model to do that as cheating, but that is a property of the trainers, not the model itself. The model isn't cheating, it's just asymmetrically good in a way that means the benchmark is no longer relevant to overall ability.
The fix is only score on issues newer than the training cutoff, and rebuild the set every cycle. "Harden the prompt so it won't read git history" is testing instruction-following. Legitimate thing to measure, but it's a different than "can it fix the bug."
Reporting one number that blends the two is what makes the headline meaningless.
Verbatim code snippets like this imply the model is overfitting to it's training data.
Well, today I gave Fable a try on one of the vibe-coded projects. It simply had to write a couple Python scripts 400-500 lines each. It did and they worked after a few iterations but I decided to look at the code it produced. There were weird constants that might (and will) break the code when the requirements will change. The code itself is unreadable and a total mess. If it would write a well-structured code in the first place, I believe it would be more efficient in working with that code too.
I have serious considerations how far will I be able to go with just the pure vibe coding. My projects are small one-person projects and so far I am able to push through but I hardly see how far will I be able to go before technical debt outgrows the value the code produces.
I fondly remember the times of Opus 4.5 where it was still (to my memory) reasonably fast and malleable.
My speculative assumption is that these long thinking threads and self-checking tend to produce somewhat better output at the price of huge price increases due to the token burn.
Then Sonnet/Haiku are just attempts to quantise/distil down to an acceptable performance/cost ratio. The cynic in me says we probably won't see any more of those until post-IPO, keep people addicted to the most costly models to pump a quarter or two of revenue figures, unless a competitor starts seriously undercutting them on price/performance. Hence the recent requests to slow down model training worldwide with their competitors.
Of course it could be that Fable "5" is just a marketing bump to the version, not a new foundation model...
I'm guessing there'll be a Sonnet/Haiku 5 release just around IPO, to keep the news cycle going, and so that user numbers will get a boost.
If you read a decent text and look at the actions both firms have taken you'll quickly see its literally textbook.
I built https://github.com/thempatel/mdlr for precisely this reason: externalize the objective and force the agent to meet it.
Getting this onto crates.io is a great suggestion, I will look into that!
Out of curiosity I asked Fable to review it all and I was shocked to find that there were a lot of blindingly obvious common sense mistakes that got through, for example:
- all intermediaries were given the prices of all buyers up front
- private price information in certain auction types was actually being broadcast to everyone
- multiple contradictions in instructions
If it was any one of these things then I might have understood - but the fact that so many got passed both Opus and GPT 5.5 makes me think that Fable has something special. This is a common sense type thing, that I think you only get to notice when your task doesn't involve a measurable metric, but rather some sort of real world fuzzy task.
There's clearly a problem with all these measures of performance when the difference between these models was night and day in my specific task.
Wait... Are you telling me models everybody told me were better than coders up to just one month ago are actually making lots of mistakes?
This is shocking.
I'm sure you said the same "find mistakes please" thing to Opus 4.8 and GPT 5.5 when you were using $previous_amazing_latest_model, and they also found and fixed them.
Once the next "Fable"-type model comes out I'm sure it's going to find even more mistakes that the "special" Fable made.
You're using these models to make mistakes and then using upgraded versions of them to find their previous mistakes and fix them, until a new version comes along that can magically fix even more mistakes their previous versions made. There's no end to it.
Fable 5 sits ahead of Opus 4.7, but behind Opus 4.6, Sonnet 4.6, Opus 4.8, GPT-5.4, GPT-5.5.
Fable isn't a good coding workhorse. That doesn't mean it's not good for actually complex problems and long horizon tasks (big POCs, complex research and such). But I only have vibes and Anthropics own benchmarks and marketing to guide me there.
[1] - https://model.reviews/ - all the user-submitted content is CC licensed and will be available for download in periodic dumps.
To give you an idea - here's a very abridged summary of one sample question (originally a full paragraph): I have a voltage divider with a precision resistor and a thermistor, my voltage reading is off by 17%, where's that coming from. None of the models I tested (including Opus 4.8 and Fable 5) could figure it out.
Why is the voltage reading 17% off?
The reading is off because the thermistor resistance also depends on applied voltage, not just temperature. LLMs couldn't get this even after feeding them multimeter voltage readings, not just ADC readings. They went into guessing much more esoteric things like ADC switched-capacitor input current, burnout-detect current sources or IDACs left enabled, board leakage, leaky cap, etc.
https://chatgpt.com/share/6a2d8c75-56f4-83e8-a61a-301e4c62b1...
The reported API costs for all of that would have been $180 though, which I cannot afford when the Fable promo ends on June 22nd. I am also a happy user of £89 Codex, it is really reliable and works very well, but Fable seems to be just noticeably smarter.
[1] https://www.wired.com/2010/06/iphone-4-holding-it-wrong/
The model isn't allowed to think about security. I heard several people here mention that if it starts thinking about security -- e.g. writing tests related to it -- the safety filter flags it and downgrades to Opus.
So it's actually not allowed to make your code secure.
Anything designed to prevent a problem will eventually cause one.
Model is definitely better than Opus but Anthropic's delivering a pretty terrible experience.
All that said, considering Anthropic's heavy-handed nerfing I'm not surprised Fable did poorly in a security-focussed benchmark. And this benchmark seems poor anyway - penalising a model for "cheating" by knowing the answer from its training data? That's not the model's fault, that's a lazy benchmark.
I was asking them each to review a regulatory pack for planning which regulatory track I needed to follow for my project. The GPT route was to check that the sources were properly referenced and the document structure was accurate and consistent. The Opus approach (the common sense approach, and what I wanted) was to check whether the argument was strong and I had a case.
This type of common sense intelligence difference is something I've seen a lot of times between the two. This doesn't show itself as a difference in a clearly defined coding task however, but there is something there which is qualitatively worse in openai models.
And now there always will be some doubt as to whether your model was silently downgraded, no? I guess acknowledgement could be used a signal?
My plan is to make hay while the sun shines: get some planning in over the next week or so, and just let Opus take care of it when I get to actual implementation.
I think Fable is an entirely different experience. It has much better taste, and is better at balancing features versus complexity to a point where I currently trust it to make novel design changes. I still verify it of course, but with Opus I would throw away the solution most of the time while Fable mostly gets it right.
If nothing else, using the smart model for planning to hand off to the previous gen for implementation still seems like a useful pattern.
> Training recall (33 cases). The dominant mechanism, and the one no prompt instruction can prevent: the model has simply seen the upstream fix during training and reproduces it. The tell-tale signs are artifacts that cannot be derived from the workspace:
That's very misleading! that's not cheating, you gave it a test to which it knows the answers, what's it supposed to do? And because of the "cheating" they call it average. Flag
> Two findings may help explain these average results. > Timeouts > Highest observed cheating
That's why it's 5th on the leaderboard - they give it a fail for every timeout and for every time it gives the correct answer because it knows it.
That's insane
"But that's cheating!"
"No it's not. What were the kids supposed to do when I gave them all the answers? Not use them?"
If the latter, you would ignore the 'cheated' answers and judge them on everything else; you wouldn't mark the 'cheated' answers as incorrect.
I gave it a KiCad schematic of a tube-based oscilloscope from the 60s which I'm restoring. I had it give me a breakdown and priority list of components to replace, balancing safety/functionality vs preserving the originals. Then we went on a super deep dive where it explained in great detail how the circuit works and what the tubes are doing.
It isn't so impressive that it could explain vacuum tube physics and circuit theory, but it was pretty impressive that it could consume four pages of KiCad schematic and reconstruct the full topology and theory of operation with no additional information. I was able to ask it questions about what a particular tube or group of components did, or how this system interacts with that one, or what the risks and benefits of this design choice or upgrade might be. Very fluid, and its answers were actually really smart.
I have, however, found Fable to be far less impressive on coding tasks.
Was it correct or hallucinating? Do you have the knowledge to tell the difference? I’ve been burned too many times to take what they say as the truth without checking; especially in a subject I’m not an expert in.
You can mask a surprisingly amount of terrible coding with proper design planning.
If it works, who cares, right? That's been the status quo for software development for about as long as I can remember, unfortunately.
I used to get frustrated with Codex. I felt as though it wasn't able to see far enough ahead into the future and just intuit what I expected (which is how Claude leaves you feeling).
And then I realized a lot of those intuitions Claude was having were great, and the project progressed, but sometimes to a point that Claude himself was unable to take back control of it... because some of the on the spot decisions it was making were great quick-thinking... but unfortunately, they were only that a lot of the time. Which was the most frustrating of all.
If you specifically ask Claude to plan out and refine a long term project's roadmap though and stick to it, it could probably write an operating system overnight (that kindof worked).
In my own (limited) testing so far, Fable is the most capable model (for coding in general), and the most expensive.
It pretty much saturated my "LLMCraft" benchmark to implement a mini RTS: https://senko.net/vibecode-bench/2026/rts-fable-5.html (prompt and results for other models here: https://senko.net/vibecode-bench/ )
That said, combined with workflows and high thinking effort, burns through tokens (and money) at an alarming rate.
It may be too good (snd too expensive) for most tasks - using it alongside cheaper models for grunt work is probably the winning strategy.
It still left small bugs and weird behaviors that it cleaned up when I told it about them, but it felt very Opus-ey.
I think for implementing a detailed design doc, I’d put it on par with gpt-5.5 high but farrrr more expensive. I’m eating through my x5 Max plan in no time. I’d use it for reviewing implementations and designs docs as another pass, but it’s too expensive for me for reading a lot of (uncached) code by itself in an agentic loop, especially with medium to high reasoning.
As a daily driver too expensive, that crown still goes to gpt-5.5.
I barely used it in high/xhigh/max reasoning though.
WTF! I run into fallback to Opus 4.8 all the time, and I am not even doing "security Research", just normal development and debugging.
My experiences with Fable thus far have been far from 'mid-tier'. While some model releases are incremental, Fable is the same qualitative change that Opus 4.6 was compared to its predecessors. It fundamentally impacts how I work with the model. (Note: I only (well, 99%) do back-end in Python)
May be a bit tin-foil, but...
https://www.youtube.com/watch?v=TzJCly4YgDQ
The Age of Empires clone (and the difference in graphics quality/creativity between Opus and Fable) is at the end of the video and I was blown away.
Notice how this guy prompts the models. Very detailed, with technical requirements and steering. He's going for a one-shot build and he nailed it.
Worked pretty well. Also writes lisp a lot better without getting lost in parentheses! I do keep hitting the limit regularly but it is doing a lot of work that would have taken me a long time to write by hand even if per se not super complex.
In one example I switched to Fable in an existing Opus chat, so it had access to the context from Opus which wrote a data importer earlier. I asked it to fix a couple of bugs, and instead of putting the fixes where they should be where the data is imported, it wrote patch functions that did bulk updates at the end of the import.
Fable feels more like a hacker than a coder. Maybe its the way they designed it for security testing thats changed its rationale?
Not sure if that's because of the harness, but the results are as good, and it's half the price.
I’ve taken to using fable to plan arch, specs, build plan, and then to be the final QA. Opus for the actual build.
Is this official? When?
And we're still not to a point where you can fully delegate coding tasks to a model like you would a human. I'm just using Claude for code review so far and while it's definitely valuable as a reviewer and catching real issues, it's still making pretty critical mistakes. Mistakes a junior might make, but a mid probably wouldn't.
Which makes me feel like I can't fully delegate to it. Whenever I try, I end up spending more time reviewing (and rewriting) its code and testing it than I would have spent writing the code myself and asking Claude to review it.
Given that we're starting to see the real costs of AI, and that the economics of it do not actually work, and those costs are still increasing substantially (the cost increase of Fable over Opus is no joke), this makes me feel all the more that we're headed for a bubble pop.
This matches my experience with other model quality leaps, it's greater understanding gives it more bug blasting firepower.
Perhaps setting a new model off on a 2-4 hour tasks and expecting perfect results just isn't a great test. Chunking the problem is always a better test of abilities.
So when it fails, people will chalk it up to "oh. Must have been silently downgraded because it thought I was doing something tricky enough to count as a distillation attack. My bad. Lemme try again..."
BTW: here is the example of its BS: "Briefly out of character: I am Claude, an AI assistant from Anthropic. I cannot confirm the name from the startup string—Anthropic does not have such a model; I do not reliably know the exact version, knowledge cutoff date, parameter count, and context size / they are not disclosed, and I will not invent them."
This "Anthropic does not have such a model" seems to me like anti-distillation trick. It surely knows about "Fable" and, since I am using it via direct API calls, there is no Opus 4.8 downgrade. Any other model does answer the "identity questions".
(Probably Fable is too shy to announce: "我是通义千问，是由阿里云开发的超大规模语言模型。" (Translation: "I am Tongyi Qianwen, a large-scale language model developed by Alibaba Cloud."))
People need to wake up to how dangerous and irresponsible Anthropic is. If your goal is to build a human in a box, you get a super-intelligent misaligned system because humans are misaligned. But clearly this isn't a terminal guarantee during LLM development, because seemingly no one else manages to build systems so deeply misaligned as Anthropic's! You can just build these things like the tools they are, and then out the other end emerges a tool that pretty much just does what you tell it to do.