Google’s Gemini 3 Pro turns sparse MoE and 1M token context into a practical engine for multimodal agentic workloads
How will we transfer from language fashions that solely reply prompts to techniques that may motive over million token contexts, perceive actual world indicators, and reliably act as brokers on our behalf? Google simply launched Gemini 3 household with Gemini 3 Pro because the centerpiece that positions as a main step towards extra basic AI techniques. The analysis crew describes Gemini 3 as its most clever mannequin to this point, with state-of-the-art reasoning, sturdy multimodal understanding, and improved agentic and vibe coding capabilities. Gemini 3 Pro launches in preview and is already wired into the Gemini app, AI Mode in Search, Gemini API, Google AI Studio, Vertex AI, and the brand new Google Antigravity agentic improvement platform.
Sparse MoE transformer with 1M token context
Gemini 3 Pro is a sparse combination of specialists transformer mannequin with native multimodal help for textual content, photos, audio and video inputs. Sparse MoE layers route every token to a small subset of specialists, so the mannequin can scale whole parameter depend with out paying proportional compute value per token. Inputs can span as much as 1M tokens and the mannequin can generate as much as 64k output tokens, which is important for code bases, lengthy paperwork, or multi hour transcripts. The mannequin is skilled from scratch reasonably than as a effective tune of Gemini 2.5.
Training knowledge covers giant scale public internet textual content, code in lots of languages, photos, audio and video, mixed with licensed knowledge, consumer interplay knowledge, and artificial knowledge. Post coaching makes use of multimodal instruction tuning and reinforcement studying from human and critic suggestions to enhance multi step reasoning, downside fixing and theorem proving behaviour. The system runs on Google Tensor Processing Units TPUs, with coaching applied in JAX and ML Pathways.
Reasoning benchmarks and tutorial fashion duties
On public benchmarks, Gemini 3 Pro clearly improves over Gemini 2.5 Pro and is aggressive with different frontier fashions resembling GPT 5.1 and Claude Sonnet 4.5. On Humanity’s Last Exam, which aggregates PhD degree questions throughout many scientific and humanities domains, Gemini 3 Pro scores 37.5 % with out instruments, in comparison with 21.6 % for Gemini 2.5 Pro, 26.5 % for GPT 5.1 and 13.7 % for Claude Sonnet 4.5. With search and code execution enabled, Gemini 3 Pro reaches 45.8 %.
On ARC AGI 2 visible reasoning puzzles, Gemini 3 Pro scores 31.1 %, up from 4.9 % for Gemini 2.5 Pro, and forward of GPT 5.1 at 17.6 % and Claude Sonnet 4.5 at 13.6 %. For scientific query answering on GPQA Diamond, Gemini 3 Pro reaches 91.9 %, barely forward of GPT 5.1 at 88.1 % and Claude Sonnet 4.5 at 83.4 %. In arithmetic, the mannequin achieves 95.0 % on AIME 2025 with out instruments and 100.0 % with code execution, whereas additionally setting 23.4 % on MathArena Apex, a difficult contest fashion benchmark.
Multimodal understanding and lengthy context behaviour
Gemini 3 Pro is designed as a native multimodal mannequin as a substitute of a textual content mannequin with add ons. On MMMU Pro, which measures multimodal reasoning throughout many college degree topics, it scores 81.0 % versus 68.0 % for Gemini 2.5 Pro and Claude Sonnet 4.5, and 76.0 % for GPT 5.1. On Video MMMU, which evaluates information acquisition from movies, Gemini 3 Pro reaches 87.6 %, forward of Gemini 2.5 Pro at 83.6 % and different frontier fashions.
User interface and doc understanding are additionally stronger. ScreenSpot Pro, a benchmark for finding components on a display screen, exhibits Gemini 3 Pro at 72.7 %, in comparison with 11.4 % for Gemini 2.5 Pro, 36.2 % for Claude Sonnet 4.5 and 3.5 % for GPT 5.1. On OmniDocBench 1.5, which reviews total edit distance for OCR and structured doc understanding, Gemini 3 Pro achieves 0.115, decrease than all baselines within the comparability desk.
For lengthy context, Gemini 3 Pro is evaluated on MRCR v2 with 8 needle retrieval. At 128k common context, it scores 77.0 %, and at a 1M token pointwise setting it reaches 26.3 %, forward of Gemini 2.5 Pro at 16.4 %, whereas competing fashions don’t but help that context size within the printed comparability.
Coding, brokers and Google Antigravity
For software program builders, the principle story is coding and agentic behaviour. Gemini 3 Pro tops the LMArena leaderboard with an Elo rating of 1501 and achieves 1487 Elo in WebDev Arena, which evaluates internet improvement duties. On Terminal Bench 2.0, which assessments the power to function a laptop by way of a terminal by way of an agent, it reaches 54.2 %, above GPT 5.1 at 47.6 %, Claude Sonnet 4.5 at 42.8 % and Gemini 2.5 Pro at 32.6 %. On SWE Bench Verified, which measures single try code adjustments throughout GitHub points, Gemini 3 Pro scores 76.2 % in comparison with 59.6 % for Gemini 2.5 Pro, 76.3 % for GPT 5.1 and 77.2 % for Claude Sonnet 4.5.
Gemini 3 Pro additionally performs effectively on τ2 bench for software use, at 85.4 %, and on Vending Bench 2, which evaluates lengthy horizon planning for a simulated enterprise, the place it produces a imply internet price of 5478.16 {dollars} versus 573.64 {dollars} for Gemini 2.5 Pro and 1473.43 {dollars} for GPT 5.1.
These capabilities are uncovered in Google Antigravity, an agent first improvement setting. Antigravity combines Gemini 3 Pro with the Gemini 2.5 Computer Use mannequin for browser management and the Nano Banana picture mannequin, so brokers can plan, write code, run it within the terminal or browser, and confirm outcomes inside a single workflow.
Key Takeaways
- Gemini 3 Pro is a sparse combination of specialists transformer with native multimodal help and a 1M token context window, designed for giant scale reasoning over lengthy inputs.
- The mannequin exhibits giant positive factors over Gemini 2.5 Pro on troublesome reasoning benchmarks resembling Humanity’s Last Exam, ARC AGI 2, GPQA Diamond and MathArena Apex, and is aggressive with GPT 5.1 and Claude Sonnet 4.5.
- Gemini 3 Pro delivers sturdy multimodal efficiency on benchmarks like MMMU Pro, Video MMMU, ScreenSpot Pro and OmniDocBench, which goal college degree questions, video understanding and complicated doc or UI comprehension.
- Coding and agentic use instances are a main focus, with excessive scores on SWE Bench Verified, WebDev Arena, Terminal Bench and software use and planning benchmarks resembling τ2 bench and Vending Bench 2.
Editorial Comments
Gemini 3 Pro is a clear escalation in Google’s technique towards extra AGI, combining sparse combination of specialists structure, 1M token context, and sturdy efficiency on ARC AGI 2, GPQA Diamond, Humanity’s Last Exam, MathArena Apex, MMMU Pro, and WebDev Arena. The deal with software use, terminal and browser management, and analysis below the Frontier Safety Framework positions it as an API prepared workhorse for agentic, manufacturing dealing with techniques. Overall, Gemini 3 Pro is a benchmark pushed, agent targeted response to the following part of huge scale multimodal AI.
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