We've been really impressed with the performance of ~30B parameter class models and how close they are to the frontier from ~6-12 months ago, which begs the question, are the frontier labs really serving 10T parameter models? Seems unlikely.

If these Gemini 3.5 numbers are accurate, then I'd wager GPT 5.5 and Opus 4.7 are a lot smaller than people have speculated, too. It's not that frontier labs can't create a 5T+ parameter model, but they don't have the data to optimize a model of that size.

Gemini 3.5 Flash is really smart in one-shot coding reasoning, btw. Near the frontier. But it doesn't do so well in long horizon agentic tasks with arbitrary tool availability. This is a common theme with Google models, and the opposite of what we see with Chinese models (start dumb, iterate consistently toward a smart solution).

Data at https://gertlabs.com/rankings

If this is accurate it raises the question: why is this model so expensive? DeepSeek v4 Flash is 284B total/13B active, FP4/FP8 mixed, and only costs $0.14/$0.28 - even less from OpenRouter. Of course Gemini 3.5 Flash is most likely a better product, and therefore it can command a higher price from an economics perspective, but does this imply Google is taking roughly a 90% profit margin on inference? If so they're either very compute-limited or confident in the model and wanting to recoup training/fixed costs (or both).

Do you have similar math for the flash-lite variant of the models? I'd be curious. Based on my testing / benchmark i think it's around the 100-120B mark.

With the Pro variant being around 600B - 800B

My testing is comparing it's performance / output to other models in the same size range, so not as scientific as yours.

Tell me more about what your day looks like. What do you think of the LLMOps books from Abi, in case you have read it ? Any other resources you can recommed?

given this, is it safe to assume that inference pricing is barely related to cost to serve at this point and there is considerable margin?

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