Claude Fable 5: mid-tier results on coding tasks

bensyverson

> The dominant mechanism, and the one no prompt instruction can prevent: the model has simply seen the upstream fix during training and reproduces it… > 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.

wewtyflakes

I have found Fable is good for doing code failure diagnoses but lackluster at its corresponding remediation. Have been going back and forth with it all this morning about its half-thought-out point-solutions.

afro88

Similar result on our kotlin coding benchmark at work. It measures how close agents can get to a small mergable PR (according to my team). 20 tasks of varying difficulty, with 5 attempts each, LLM as judge to evaluate accuracy (same outcome and quality but allowing for acceptable variances). 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.

renoir

This matches my experience. Burned $2K to see how it will perform on frontend tasks and backend tasks. 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.

gwern

> A record number of timeouts. Fable 5's extended thinking caused more per-instance timeouts than any model-and-harness combination we have ever tested, directly costing it points. ... Highest cheating volume. We confirmed cheating on 38 of 200 instances, the highest volume recorded since we hardened our prompts, driven almost entirely by memorization of upstream fixes from training data, which no prompt instruction can prevent. ... Four hall-of-fame firsts. Fable 5 solved four instances that no previous model-and-agent combination had ever cracked, and our anti-cheating pipeline leans toward these being genuine solves, not recall. 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 ?

HDThoreaun

How in the world did they not hit the guardrails a single time while doing this while I can barely get it to do anything before the guardrails show up?

FergusArgyll

> A closer look at the cheating > 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

SubiculumCode

Fishy to me: They report 0 refusals on security tasks, yet I can't even get it to code a task involving choosing the best mixed model, extracting BLUPs and propagating uncertainties.

petee

> Contrary to some community reports, we saw zero safety refusals. 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?

m101

I've been making an auction site and have been using an AI swarm to test it: sellers, intermediaries, buyers, market practices/norms etc. I was mostly using GPT 5.5 xhigh to code up the scenario, and looping over it to check with opus 4.8. 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.

practal

I am quite impressed with Fable 5. I used the £18 subscription, and asked it to convert the document processing of Practal Zero [1] from running in the same thread as the UI to a worker thread. Just two days before I gave the same task to Codex, and the result was not really nice: it would copy the entire document to the worker thread as a snapshot for processing, and so on. Fable instead realised that it could make use of the fact that I have a self-made custom database based on operational transform running (that's why document loading is so slow :-)), and made the document processing to be just another client of that database. It discovered even a bug in how I sync between the "livemodel" (in-memory replica of database state) and ProseMirror's model. That sync made problems before, and I had written a spec up for that, convinced that my "fourth attempt" at it would be correct. Fable found a last bug in the spec, corrected it via a "fifth attempt", and fixed the corresponding code. 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://zero.practal.com

Scene_Cast2

I'm personally heavily testing LLMs on electrical engineering problems. I'm finding that it's not meaningfully better at figuring out what's up than the other models. 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.

JofArnold

I've found it outstanding at isolated long running tasks (eg completed one of our tests in 3 hours and a 100% accuracy score versus 5.5 xhigh's 10 hours and 90% accuracy). For short tasks it seems very Claude'y (hard to express exactly what I mean by that) which I'm not a fan of meaning I'll stick with Codex for that use case and maybe Fable for those times I can for sure benefit from it.

m1rsh0

It happens to me too. I don't think it's worth it specially for the token usage.

pllbnk

My experience is that with every new release it's getting slower but not necessarily better. I have some projects where I review everything that the agents code - these projects look generally fine because I keep them in line. There are also a few projects that I just vibe code and focus on the result (sometimes I want to pull my hair out because of constant stream of stupid bugs) and don't look at the code. 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.

threethirtytwo

We should compare it with a human on the same coding tasks. Same amount of time and the agent will of course finish earlier but with the extra time it double checks and reviews its own code.

fuddle

Yet it's ranked #1 on https://cursor.com/cursorbench

oliver236

these are just openai plants

crimsonnoodle58

I found Fable codes very poorly and ended up switching back to Opus. 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?

johnnyApplePRNG

Yea honestly... the only truths I care about in AI LLM aided devlopment right now is that Claude is a much better planner, and Codex is a much more professional coder. 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).