Comment by rubicon33
3 months ago
I think they neutered Fable. When it first came out it was indeed revolutionary. But what we have today, is not what we had before the ban.
3 months ago
I think they neutered Fable. When it first came out it was indeed revolutionary. But what we have today, is not what we had before the ban.
Noticed that too. I wonder if these things just degrade over time, perhaps with the way it writes memories about my project as it goes
I’ve observed the degradation, but I suspect what’s happening is they’re tuning it for lower inference costs. Maybe turning down the amount of thinking, maybe quantizing, maybe something else.
It seems like there’s a week by week and sometimes day by day change in performance when on a subscription plan using their harnesses.
https://marginlab.ai/trackers/claude-code/ their tracker generally shows that isn’t the case. The only times I’ve seen it drop is something broken and just before fable launched.
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i thought i had noticed a degradation, but it turned out claude code had swapped itself back to opus.
might be the case for you as well
I feel like a conspiracy theorist but it feels like every new model release from both Anthropic and OpenAI has 1-2 weeks of fantastic performance then a gradual (and sometimes not so gradual) decline in intelligence. It's like they're quantising the model in the background to optimise for available compute/RAM. Which, if I put my MBA hat on, would make perfect sense. Why not halve the required RAM for "90%" of the performance? They get fantastic benchmarks and glowing reviews on release, then slowly squeeze more performance out of the model. By the time the next model is ready for release, the jump feels quite large again.
This is also my experience. I don't know if it's because of the quantization theory, or if it's just me getting used to a certain level of coding performance and gradually less tolerant of the mistakes it makes more over time.
And yet: https://marginlab.ai/trackers/claude-code-historical-perform...
There's clearly random variation, but it also shows each model release is just genuinely better. ( With the exception of a heavily degraded week of opus 4.7, which was acknowledged as a problem at the time. )
There's a psychology of getting used to models after being wowed by the new performance. It sets in as your new baseline expectations, and then when it doesn't deliver, it's felt more acutely. When it does deliver, it's just meeting expectations.
Then a new better model comes along and it's a step up again, another wow moment for a week or two until expectations adjust to meet the new baseline.
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You should. Feel like a conspiracy theorist when saying things like this.
Users are not reliable or consistent model evaluators. Users adapt to models - the moment they get a model that performs better, their expectations rise, their tasks get harder and their prompts get shorter.
"They made the model worse" is PEBKAC in 9 cases out of 10.
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At this point I don't even bother with it. Constantly falls back to Opus anyway, so I may as well save myself some time.