It could be interesting to do the metric of intelligence per second.

ie intelligence per token, and then tokens per second

My current feel is that if Sonnet 4.6 was 5x faster than Opus 4.6, I'd be primarily using Sonnet 4.6. But that wasn't true for me with prior model generations, in those generations the Sonnet class models didn't feel good enough compared to the Opus class models. And it might shift again when I'm doing things that feel more intelligence bottlenecked.

But fast responses have an advantage of their own, they give you faster iteration. Kind of like how I used to like OpenAI Deep Research, but then switched to o3-thinking with web search enabled after that came out because it was 80% of the thoroughness with 20% of the time, which tended to be better overall.

I think there's clearly a "Speed is a quality of it's own" axis. When you use Cereberas (or Groq) to develop an API, the turn around speed of iterating on jobs is so much faster (and cheaper!) then using frontier high intelligence labs, it's almost a different product.

Also, I put together a little research paper recently--I think there's probably an underexplored option of "Use frontier AR model for a little bit of planning then switch to diffusion for generating the rest." You can get really good improvements with diffusion models! https://estsauver.com/think-first-diffuse-fast.pdf

Maybe make that intelligence per token per relative unit of hardware per watt. If you're burning 30 tons of coal to be 0.0000000001% better than the 5 tons of coal option because you're throwing more hardware at it, well, it's not much of a real improvement.

We agree! In fact, there is an emerging class of models aimed at fast agentic iteration (think of Composer, the Flash versions of proprietary and open models). We position Mercury 2 as a strong model in this category.

Intelligence per second is a great metric. I never could fully articulate why I like Gemini 3 Flash but this is exactly why. It’s smart enough and unbelievably fast. Thanks for sharing this

Yeah I agree with this. We might be able to benchmark it soon (if we can’t already) but asking different agentic code models to produce some relatively simple pieces of software. Fast models can iterate faster. Big models will write better code on the first attempt, and need less loop debugging. Who will win?

At the moment I’m loving opus 4.6 but I have no idea if its extra intelligence makes it worth using over sonnet. Some data would be great!

Interesting suggestion.

Maybe we could use some sort of entropy-based metric as a proxy for that?

Interesting perspective. Perhaps also the user would adopt his queries knowing he can only to small (but very fast) steps. I wonder who would win!

Useful for evaluating people as well

I really thought this was sarcasm. Intelligence per token? Intelligence at all, in a token? We don’t even agree on how to measure _human_ intelligence! I just can’t. Artificially intelligent indeed. Probably the perfect term for it, you know in lieu of authentic intelligence.

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