Gemini 3 Deep Think
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Re: Gemini 3 Deep Think
#12According to benchmarks in the announcement, healthily ahead of Claude 4.6. I guess they didn't test ChatGPT 5.3 though. Google has definitely been pulling ahead in AI over the last few months. I've been using Gemini and finding it's better than the other models (especially for biology where it doesn't refuse to answer harmless questions).
Re: Gemini 3 Deep Think
#13Here is the methodologies for all the benchmarks: https://storage.googleapis.com/deepmind-media/gemini/gemini_... The arc-agi-2 score (84.6%) is from the semi-private eval set. If gemini-3-deepthink gets above 85% on the private eval set, it will be considered "solved" >Submit a solution which scores 85% on the ARC-AGI-2 private evaluation set and win $700K. https://arcprize.org/guide#overview
Interestingly, the title of that PDF calls it "Gemini 3.1 Pro". Guess that's dropping soon.
edit: they just removed the reference to "3.1" from the pdf
Re: Gemini 3 Deep Think
#14Why a Twitter post and not the official Google blog post… https://blog.google/innovation-and-ai/models-and-research/ge...
Re: Gemini 3 Deep Think
#15Re: Gemini 3 Deep Think
#16Re: Gemini 3 Deep Think
#17According to benchmarks in the announcement, healthily ahead of Claude 4.6. I guess they didn't test ChatGPT 5.3 though. Google has definitely been pulling ahead in AI over the last few months. I've been using Gemini and finding it's better than the other models (especially for biology where it doesn't refuse to answer harmless questions).
Re: Gemini 3 Deep Think
#18OT but my intuition says that there’s a spectrum - non thinking models - thinking models - best of N models like deep think an gpt pro Each one is of a certain computational complexity. Simplifying a bit, I think they map to - linear, quadratic and n^3 respectively. I think there are certain class of problems that can’t be solved without thinking because it necessarily involves writing in a scratchpad. And same for b…
Yeah, these are made possible largely by better use at high context lengths. You also need a step that gathers all the Ns and selects the best ideas / parts and compiles the final output. Goog have been SotA at useful long context for a while now (since 2.5 I'd say). Many others have come with "1M context", but their usefulness after 100k-200k is iffy.
What's even more interesting than maj@n or best of n is pass@n. For a lot of applications youc an frame the question and search space such that pass@n is your success rate. Think security exploit finding. Or optimisation problems with quick checks (better algos, kernels, infra routing, etc). It doesn't matter how good your pass@1 or avg@n is, all you care is that you find more as you spend more time. Literally throwing money at the problem.
Re: Gemini 3 Deep Think
#19Google is absolutely running away with it. The greatest trick they ever pulled was letting people think they were behind.