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Gemini 3.1 Pro

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Re: Gemini 3.1 Pro

#3
Appears the only difference to 3.0 Pro Preview is Medium reasoning. Model naming has long gone from even trying to make sense, but considering 3.0 is still in preview itself, increasing the number for such a minor change is not a move in the right direction.

Re: Gemini 3.1 Pro

#4
post #3

Appears the only difference to 3.0 Pro Preview is Medium reasoning. Model naming has long gone from even trying to make sense, but considering 3.0 is still in preview itself, increasing the number for such a minor change is not a move in the right direction.

Maybe that's the only API-visible change, saying nothing about the actual capabilities of the model?

Re: Gemini 3.1 Pro

#7
post #3

Appears the only difference to 3.0 Pro Preview is Medium reasoning. Model naming has long gone from even trying to make sense, but considering 3.0 is still in preview itself, increasing the number for such a minor change is not a move in the right direction.

I disagree. Incrementing the minor number makes so much more sense than “gemini-3-pro-preview-1902” or something.

Re: Gemini 3.1 Pro

#8
It seems google is having a disjointed roll out, and there will likely be an official announcement in a few hours. Apparently 3.1 showed up unannounced in vertex at 2am or something equally odd.

Either way early user tests look promising.

Re: Gemini 3.1 Pro

#9
Fine, I guess. The only commercial API I use to any great extent is gemini-3-flash-preview: cheap, fast, great for tool use and with agentic libraries. The 3.1-pro-preview is great, I suppose, for people who need it.

Off topic, but I like to run small models on my own hardware, and some small models are now very good for tool use and with agentic libraries - it just takes a little more work to get good results.

Re: Gemini 3.1 Pro

#10
Gemini 3 seems to have a much smaller token output limit than 2.5. I used to use Gemini to restructure essays into an LLM-style format to improve readability, but the Gemini 3 release was a huge step back for that particular use case.

Even when the model is explicitly instructed to pause due to insufficient tokens rather than generating an incomplete response, it still truncates the source text too aggressively, losing vital context and meaning in the restructuring process.

I hope the 3.1 release includes a much larger output limit.

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