Micah-Hill Smith

speaker

601 appearances 1 recordings 1 series first heard Jan 2026 last heard 8 Jan

Micah-Hill Smith’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jan 2026 with 1.

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Latent Space: The AI Engineer Podcast · Artificial Analysis: Independent LLM Evals as a Service — with George Cameron and Micah-Hill Smith · 8 Jan 2026 podcast

Oh, okay.

I think.

Pretty sure.

Let's say G V D5 and ChatGPT, the consumer experience is a model router.

When you're hitting the API, like we can you can pick the different versions and you can pick reasoning strengths of the different versions.

But that that goes to why this is now such a complex thing.

So earlier this year, and probably when you and George last spoke for the AI NGS World's Fair, we had this great slide that was super easy where we would show that

The average reasoning model is using 10 times the number of tokens per query in our intelligence index as the average non-reasoning model.

And there was this moment where that was a pretty clear distinction and extremely useful to look at it just like that.

Definitely no longer the case, not least because you can think about reasoning strength for a bunch of these different models, but particularly because different models have wildly different token efficiency now, more than in order.

Of magnitude in difference, that means the the way that you probably need to think about cost for any application is to use something like our cost-to-run intelligence index metric as the starting point for what it's going to look like for these different models, these different reasoning strengths, and this continuous spectrum from non-reasoning to reasoning.

That's basically like where we're at.

So we will still show reasoning and non-reasoning and define reasoning as when there is that.

separated chain of thought that you're getting in a different parameter in than API normally, but it doesn't necessarily anymore mean that that model is actually going to have longer end to end latency, that is going to use more tokens than something that is branded on a non-reasoning model for the same task.

Oh book.

An extra thing.

Let's say, let's say, let's say that we've got that's a really important extra thing though, right?

That you've got not just the average number of tokens being used by the model, which we cover really well right now, but the behavior that you want in the model is it to use more tokens when it needs more tokens and not to use more tokens when it doesn't need more tokens.

So that's what OpenAI were basically claiming that 5.1 codex is better at.

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