Gemini 3.1 Pro is based on Gemini 3 Pro
Gemini 3.1 Pro
21–30 of 951 posts
Re: Gemini 3.1 Pro
#22blog post is up- https://blog.google/innovation-and-ai/models-and-research/ge... edit: biggest benchmark changes from 3 pro: arc-agi-2 score went from 31.1% -> 77.1% apex-agents score went from 18.4% -> 33.5%
Does the arc-agi-2 score more than doubling in a .1 release indicate benchmark-maxing? Though i dont know what arc-agi-2 actually tests
Re: Gemini 3.1 Pro
#23Price is unchanged from Gemini 3 Pro: $2/M input, $12/M output. https://ai.google.dev/gemini-api/docs/pricing
Knowledge cutoff is unchanged at Jan 2025. Gemini 3.1 Pro supports "medium" thinking where Gemini 3 did not: https://ai.google.dev/gemini-api/docs/gemini-3
Compare to Opus 4.6's $5/M input, $25/M output. If Gemini 3.1 Pro does indeed have similar performance, the price difference is notable.
Re: Gemini 3.1 Pro
#24Another preview release. Does that mean the recommended model by Google for production is 2.5 Flash and Pro? Not talking about what people are actually doing but the google recommendation. Kind of crazy if that is the case
Re: Gemini 3.1 Pro
#25I've been playing with the 3.1 Deep Think version of this for the last couple of weeks and it was a big step up for coding over 3.0 (which I already found very good).
It's only February...
Re: Gemini 3.1 Pro
#26Where is Simon's pelican?
Re: Gemini 3.1 Pro
#27Somehow doesn't work for me :) "An internal error has occurred"
Re: Gemini 3.1 Pro
#28Has anyone noticed that models are dropping ever faster, with pressure on companies to make incremental releases to claim the pole position, yet making strides on benchmarks? This is what recursive self-improvement with human support looks like.
Only using my historical experience and not Gemini 3.1 Pro, I think we see benchmark chasing then a grand release of a model that gets press attention...
Then a few days later, the model/settings are degraded to save money. Then this gets repeated until the last day before the release of the new model.
If we are benchmaxing this works well because its only being tested early on during the life cycle. By middle of the cycle, people are testing other models. By the end, people are not testing them, and if they did it would barely shake the last months of data.
Re: Gemini 3.1 Pro
#29Has anyone noticed that models are dropping ever faster, with pressure on companies to make incremental releases to claim the pole position, yet making strides on benchmarks? This is what recursive self-improvement with human support looks like.
Remember when ARC 1 was basically solved, and then ARC 2 (which is even easier for humans) came out, and all of the sudden the same models that were doing well on ARC 1 couldn’t even get 5% on ARC 2? Not convinced these benchmark improvements aren’t data leakage.