Gemma 4 is Google DeepMind's most capable open model family: four variants from 2B to 31B, Apache 2.0 license, multimodal across text/image/video/audio, and the 31B Dense ranking #3 on Chatbot Arena against all open-weight models globally.
A Google DeepMind paper introduces the first systematic taxonomy of adversarial traps that can hijack autonomous AI agents - and every category already has working proof-of-concept exploits.
Google DeepMind's reasoning mode scores 84.6% on ARC-AGI-2, 3455 Codeforces Elo, and solves 18 previously unsolved research problems - outpacing Claude Opus 4.6 and GPT-5.2 on reasoning-heavy tasks.
AlphaEvolve evolved two novel game theory algorithms - VAD-CFR and SHOR-PSRO - that outperform human-designed baselines across 11 games, using mechanisms no researcher would have designed.
Detailed comparison of Moonshot AI's Kimi K2.5 and Google DeepMind's Gemini 3.1 Pro - a trillion-parameter open MoE against Google's flagship multimodal model.
Comparing Moonshot AI's 1T-parameter Kimi K2.5 with Google DeepMind's Gemma 3 27B - two multimodal open-weight models separated by 37x in parameter count but sharing a vision-first design philosophy.
