Google’s Gemini 3 is here, and it seems to have lived up to its hype.
The tech giant’s latest flagship AI model, Gemini 3 Pro, has posted dominant results across a comprehensive suite of industry benchmarks, outperforming competing models from Anthropic and OpenAI in nearly all tests. The release marks a significant escalation in the ongoing AI arms race between the world’s leading technology companies.
Standout Performance Across Key Metrics
Gemini 3 Pro demonstrated particularly impressive capabilities in mathematical reasoning and coding tasks. The model achieved a perfect 100% score on AIME 2025, a challenging mathematics benchmark, matching Claude Sonnet 4.5’s performance while significantly outpacing Gemini 2.5 Pro’s 88.0% and GPT-5.1’s 94.0%. On the demanding MathArena Apex benchmark, which tests challenging math contest problems, Gemini 3 Pro scored 23.4%, dramatically outperforming all competitors.
In coding capabilities, Gemini 3 Pro posted strong results across multiple benchmarks. On LiveCodeBench Pro, which evaluates competitive coding problems from platforms like Codeforces and IOI, the model scored 2,439, placing it ahead of GPT-5.1’s 2,243 and well ahead of Claude Sonnet 4.5’s 1,418. For agentic coding tasks measured by Terminal-Bench 2.0, Gemini 3 achieved 54.2%, outperforming all competitors including Claude’s 42.8% and GPT-5.1’s 47.6%.
Visual Reasoning and Multimodal Capabilities
One of Gemini 3’s most striking achievements came in visual reasoning. On ARC-AGI-2, a benchmark designed to test visual reasoning puzzles, Gemini 3 Pro scored 31.1%, more than 6x of Gemini 2.5 Pro’s 4.9% and significantly exceeding Claude Sonnet 4.5’s 13.6% and GPT-5.1’s 17.6%. The model also dominated ScreenSpot-Pro, a screen understanding benchmark, with 72.7% compared to Claude’s 36.2% and GPT-5.1’s mere 3.5%.
For multimodal understanding, Gemini 3 Pro posted 81.0% on MMMU-Pro and 87.6% on Video-MMMU, demonstrating strong capabilities in processing and reasoning about both images and video content. These scores positioned it ahead of competing models, though the margins were narrower than in visual reasoning tasks.
Academic and Knowledge Benchmarks
The model showed robust performance on academic reasoning tasks. On GPQA Diamond, which tests scientific knowledge without tools, Gemini 3 Pro achieved 91.9%, edging out GPT-5.1’s 88.1% and Claude’s 83.4%. For Humanity’s Last Exam, an academic reasoning benchmark, Gemini 3 scored 45.8% with search and code execution enabled, though GPT-5.1 wasn’t tested under comparable conditions.
Long-context performance represented one area where Gemini 3 showed more modest leads. On MRCR v2 with an 8-needle test averaging 128k tokens, Gemini 3 achieved 77.0%, ahead of Claude’s 47.1% and GPT-5.1’s 61.6%.
Agentic and Real-World Task Performance
For agentic capabilities, Gemini 3 Pro demonstrated strong results across several benchmarks. On Vending-Bench 2, which measures long-horizon agentic tasks by net worth accumulation, Gemini 3 achieved $5,478.16, substantially outperforming Claude Sonnet 4.5’s $3,838.74 and GPT-5.1’s $1,473.43. The model also scored 85.4% on t2-bench for agentic tool use and 76.2% on SWE-Bench Verified for single-attempt coding tasks, though Claude’s 77.2% edged it out on the latter.
Industry Implications
The release of Gemini 3 Pro intensifies competition in the generative AI market, where companies are racing to demonstrate superiority across an expanding array of capabilities. The model’s strong showing across diverse benchmarks—from pure reasoning to coding to multimodal understanding—suggests Google has made significant architectural improvements since Gemini 2.5.
For enterprise customers and developers, the results indicate that Gemini 3 Pro could be particularly well-suited for applications requiring visual reasoning, mathematical problem-solving, and agentic workflows. The model’s performance on coding benchmarks also positions it as a strong contender for software development tools and assistants.