Today we’re sharing an early look at our latest Gemini update for I/O! Introducing the updated Gemini 2.5 Pro (I/O edition), which ranks #1 on WebDev Arena and surpasses our previous 2.5 Pro model by +147 Elo points. 🏆 blog.google/products/gemini/…

We’ve been thrilled by the positive reception to Gemini 2.0 Flash Thinking we discussed in December. Today we’re sharing an experimental update (gemini-2.0-flash-thinking-exp-01-21) with improved performance on math, science, and multimodal reasoning benchmarks 📈: • AIME: 73.3% • GPQA: 74.2% • MMMU: 75.4%

I’m very excited to share our work on Gemini today! Gemini is a family of multimodal models that demonstrate really strong capabilities across the image, audio, video, and text domains. Our most-capable model, Gemini Ultra, advances the state of the art in 30 of 32 benchmarks, including 10 of 12 popular text and reasoning benchmarks, 9 of 9 image understanding benchmarks, 6 of 6 video understanding benchmarks, and 5 of 5 speech recognition and speech translation benchmarks. Gemini Ultra is the first model to achieve human-expert performance on MMLU across 57 subjects with a score above 90%. It also achieves a new state-of-the-art score of 62.4% on the new MMMU multimodal reasoning benchmark, outperforming the previous best model by more than 5 percentage points. Gemini was built by an awesome team of people from @GoogleDeepMind, @GoogleResearch, and elsewhere at @Google, and is one of the largest science and engineering efforts we’ve ever undertaken. As one of the two overall technical leads of the Gemini effort, along with my colleague @OriolVinyalsML, I am incredibly proud of the whole team, and we’re so excited to be sharing our work with you today! There’s quite a lot of different material about Gemini available, starting with: Main blog post: blog.google/technology/ai/go… 60-page technical report authored by th Gemini Team: deepmind.google/gemini/gemin… In this thread, I’ll walk you through some of the highlights.

Honored that our 2012 paper "Building High-level Features Using Large Scale Unsupervised Learning" received an @icmlconf Test of Time Award honorable mention! Joint work with @quocleix, @MarcRanzato, @RajatMonga, Matthieu Devin, Kai Chen, @greg_corrado, myself, & @AndrewYNg.