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Comment by heliosAtwork | Hacker News Reader

Focus on speed and being OK with temporarily being #3/4 in intelligence might be the counterintuitive approach which makes Google win long term (whether accidentally or strategically). Can't wait to try Gemini Pro later this year!

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Yeah, anthropic's models, even opus, are so slow I constantly find myself wishing for something a little bit dumber but a lot faster as most of the work is mechanical. If you have a clever controller agent driving some slightly dumber workhorses you get a lot more done and the quality drop is neglible.

I honestly can't believe serious people are making this argument on a straight face.

Gemini 3.7 flash outputs so many tokens per answer it doesn't matter how fast its TPS is, sol will end up being both cheaper and faster than Gemini. So ppl are paying more for a given task, waiting longer and using a dumber intelligence because "TPS number shiny".

Gemini 3.8 outputs 11k more tokens PER TASK on average in AAII than 3.7 putting it dead last in output tokens per task in the leaderboard.

AA isn't the only benchmark

https://deepswe.datacurve.ai/

There are numerous benchmarks that measure cost per task, which factors out tokens entirely. Gemini 3.8 flash is significantly lower than Sol on basically all of them

https://artificialanalysis.ai/#cost-tabs

That said, Luna is the undisputed king here at the moment and is what I use as my workhorse model.

It's so funny how many people diverge on the same model.

Ps. For the last week I diverged to Luna too, still need to check 3.8 flash.

But 3.6 flash was my go-to model 3 weeks ago and before it was deepseek flash/pro for a while.

None of the claude models seemed cost effective though.

>There are numerous benchmarks that measure cost per task, which factors out tokens entirely. Gemini 3.8 flash is significantly lower than Sol on basically all of them https://artificialanalysis.ai/#cost-tabs

Not sure if you read your own link but Sol 56 high ranks smack between Gemini 3.8 flash medium and high. Gemini 3.8 flash comes in as more expensive per task than Sol 56 high according to artificial analysis.

Luna high is literally 30X cheaper than Gemini 3.8 flash high.

You can limit the model viewer and they're getting better at testing multiple effort levels now: https://artificialanalysis.ai/?models=gpt-5-6-sol-medium%2Cg...

One reason is clear: Sol uses dramatically fewer output tokens than Gemini 38 flash https://artificialanalysis.ai/?models=gemini-3-8-flash%2Cgem...

I open the link and I see Flash 3.8 high at 0.58 and Sol at 0.95. I don't understand why you say that "Sol 56 high ranks smack between Gemini 3.8 flash medium and high" but that is clearly wrong.

On cost per intelligence task, Gemini38flash and Sol56 trade back and forth on cost depending on effort level. https://i.imgur.com/zPaWPXx.png As seen in this image, literally: Sol56 high ranks in between Gemini 38 medium and high. The image proves it.

I also included Sol56 xhigh, which ranks above even Gemini38 high.

I don't know if my code is just "complex", but I find that Luna on max ignores the surrounding style and completely ignores logical consequences of a change, like just writing `del arg1, del arg2, ...` instead of dropping it from the surrounding code. All LLMs make questionable decisions at times, but Luna requires so much guidance that it's faster to just type it out yourself. What kind of routine tasks can one accomplish with such a model?

Do you have code formatters, linters and static analysis?

I can get extremely dumb models to get our code style correct because of those guard rails and a specific style document.

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