There's a whole frontier of cost vs performance and this model pushes it out in a bunch of places, so its not just a matter of making a different tradeoff as you make it sound.
An economically successful step would be to improve training cost/frontier advance.
They essentially failed at that, assuming they kept their training costs for the model step at least constant.
Notable takeaways:
- Sol-low - cost-competitive with Haiku, while outperforming Sonnet on medium
- Sol-High - Competes with Astra medium and beats Opus 5.5-medium, at 20% the cost
- Sol-Max - Beats Opus 5.5 high, Astra high, and competes with Opus xhigh, at cheaper than Sonnet and a fraction of the Opus cost
And when you look at time spent, With the exception of Sol-max you get better results in less time for less money. My own experience is that this is true-- it is difficult to call Opus strictly better than Sol, but Sol gets where it's going far more efficiently.
It's rather hard to look at opus-- or any of anthropic's models-- and say "economical".
And for most people here who care about coding performance Anthropic has a substantial lead[1] - although sadly they don't have coding benchmarks separately for Opus 5.5 Medium which I think it the sweet spot.
The cost tab really should have something like "how many tasks can you do in a particular subscription tier" for many people. I realize this changes often though.
[1] https://artificialanalysis.ai/?models=gpt-6-astra-high%2Cgpt...
If you leave xhigh and max off.. you still get better performance than Opus 5.5 max
Opus 5.5 Max - score 59 - cost per task - $10.04
Fable 5.2 High - score 62 - cost per task - $9.08
Note: This doesn't really capture the reality of things like 3d design or electronics design, where the earlier models just can't DO any of it well. Maybe you can bandaid over some of the gaps with strong skills, etc. The newer models can just do things that were not possible at all. When you move outside of coding into the visual/spacial space, you'll feel the inflection point on those capabilities that are happening right now.
> They essentially failed at that, assuming they kept their training costs for the model step at least constant.
What? No!
This is completely wrong!
This proves that inference scaling continues to work beyond the previous frontier.
Claiming that higher inference costs shows failure is an odd thing to think. We've seen this optimization cycle over and over, most recently with Opus 5 -> Opus 5.5
Opus 5 was slow and expensive. Opus 5.5 came out and was better, fast and much cheaper for them to run.
There is nothing to indicate the same can't happen here.
Anthropic probably has multiple options open to them with this model, and they'll probably explore all of them:
- Use collected Fable 5.2 traces to improve reasoning economy for Fable 5.3
- Use Fable 5.2 as a teacher model for Opus 5.6
- Add additional fields RL data to expand Fable capabilities for Fable 5.4