Google’s Gemini 3 Professional turns sparse MoE and 1M token context right into a sensible engine for multimodal agentic workloads

How can we transfer from language fashions that solely reply prompts to methods that may purpose over million token contexts, perceive actual world alerts, and reliably act as brokers on our behalf? Google simply launched Gemini 3 household with Gemini 3 Professional because the centerpiece that positions as a serious step towards extra common AI methods. The analysis workforce describes Gemini 3 as its most clever mannequin to date, with cutting-edge reasoning, sturdy multimodal understanding, and improved agentic and vibe coding capabilities. Gemini 3 Professional 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 Professional is a sparse combination of consultants transformer mannequin with native multimodal assist for textual content, photos, audio and video inputs. Sparse MoE layers route every token to a small subset of consultants, so the mannequin can scale complete parameter depend with out paying proportional compute price 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 somewhat than as a high-quality tune of Gemini 2.5.

Coaching knowledge covers massive scale public net textual content, code in lots of languages, photos, audio and video, mixed with licensed knowledge, consumer interplay knowledge, and artificial knowledge. Publish 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 Items TPUs, with coaching applied in JAX and ML Pathways.

Reasoning benchmarks and tutorial fashion duties

On public benchmarks, Gemini 3 Professional clearly improves over Gemini 2.5 Professional and is aggressive with different frontier fashions resembling GPT 5.1 and Claude Sonnet 4.5. On Humanity’s Final Examination, which aggregates PhD stage questions throughout many scientific and humanities domains, Gemini 3 Professional scores 37.5 p.c with out instruments, in comparison with 21.6 p.c for Gemini 2.5 Professional, 26.5 p.c for GPT 5.1 and 13.7 p.c for Claude Sonnet 4.5. With search and code execution enabled, Gemini 3 Professional reaches 45.8 p.c.

On ARC AGI 2 visible reasoning puzzles, Gemini 3 Professional scores 31.1 p.c, up from 4.9 p.c for Gemini 2.5 Professional, and forward of GPT 5.1 at 17.6 p.c and Claude Sonnet 4.5 at 13.6 p.c. For scientific query answering on GPQA Diamond, Gemini 3 Professional reaches 91.9 p.c, barely forward of GPT 5.1 at 88.1 p.c and Claude Sonnet 4.5 at 83.4 p.c. In arithmetic, the mannequin achieves 95.0 p.c on AIME 2025 with out instruments and 100.0 p.c with code execution, whereas additionally setting 23.4 p.c on MathArena Apex, a difficult contest fashion benchmark.

Multimodal understanding and lengthy context behaviour

Gemini 3 Professional is designed as a local multimodal mannequin as a substitute of a textual content mannequin with add ons. On MMMU Professional, which measures multimodal reasoning throughout many college stage topics, it scores 81.0 p.c versus 68.0 p.c for Gemini 2.5 Professional and Claude Sonnet 4.5, and 76.0 p.c for GPT 5.1. On Video MMMU, which evaluates information acquisition from movies, Gemini 3 Professional reaches 87.6 p.c, forward of Gemini 2.5 Professional at 83.6 p.c and different frontier fashions.

Consumer interface and doc understanding are additionally stronger. ScreenSpot Professional, a benchmark for finding parts on a display screen, reveals Gemini 3 Professional at 72.7 p.c, in comparison with 11.4 p.c for Gemini 2.5 Professional, 36.2 p.c for Claude Sonnet 4.5 and three.5 p.c for GPT 5.1. On OmniDocBench 1.5, which stories general edit distance for OCR and structured doc understanding, Gemini 3 Professional achieves 0.115, decrease than all baselines within the comparability desk.

For lengthy context, Gemini 3 Professional is evaluated on MRCR v2 with 8 needle retrieval. At 128k common context, it scores 77.0 p.c, and at a 1M token pointwise setting it reaches 26.3 p.c, forward of Gemini 2.5 Professional at 16.4 p.c, whereas competing fashions don’t but assist 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 Professional tops the LMArena leaderboard with an Elo rating of 1501 and achieves 1487 Elo in WebDev Enviornment, which evaluates net improvement duties. On Terminal Bench 2.0, which exams the flexibility to function a pc via a terminal by way of an agent, it reaches 54.2 p.c, above GPT 5.1 at 47.6 p.c, Claude Sonnet 4.5 at 42.8 p.c and Gemini 2.5 Professional at 32.6 p.c. On SWE Bench Verified, which measures single try code modifications throughout GitHub points, Gemini 3 Professional scores 76.2 p.c in comparison with 59.6 p.c for Gemini 2.5 Professional, 76.3 p.c for GPT 5.1 and 77.2 p.c for Claude Sonnet 4.5.

Gemini 3 Professional additionally performs nicely on τ² bench for device use, at 85.4 p.c, and on Merchandising Bench 2, which evaluates lengthy horizon planning for a simulated enterprise, the place it produces a imply web value of 5478.16 {dollars} versus 573.64 {dollars} for Gemini 2.5 Professional and 1473.43 {dollars} for GPT 5.1.

These capabilities are uncovered in Google Antigravity, an agent first improvement setting. Antigravity combines Gemini 3 Professional with the Gemini 2.5 Laptop 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 Professional is a sparse combination of consultants transformer with native multimodal assist and a 1M token context window, designed for giant scale reasoning over lengthy inputs.
• The mannequin reveals massive beneficial properties over Gemini 2.5 Professional on tough reasoning benchmarks resembling Humanity’s Final Examination, ARC AGI 2, GPQA Diamond and MathArena Apex, and is aggressive with GPT 5.1 and Claude Sonnet 4.5.
• Gemini 3 Professional delivers sturdy multimodal efficiency on benchmarks like MMMU Professional, Video MMMU, ScreenSpot Professional and OmniDocBench, which goal college stage questions, video understanding and sophisticated doc or UI comprehension.
• Coding and agentic use instances are a major focus, with excessive scores on SWE Bench Verified, WebDev Enviornment, Terminal Bench and power use and planning benchmarks resembling τ2 bench and Merchandising Bench 2.

Gemini 3 Professional is a transparent escalation in Google’s technique towards extra AGI, combining sparse combination of consultants structure, 1M token context, and powerful efficiency on ARC AGI 2, GPQA Diamond, Humanity’s Final Examination, MathArena Apex, MMMU Professional, and WebDev Enviornment. The deal with device use, terminal and browser management, and analysis underneath the Frontier Security Framework positions it as an API prepared workhorse for agentic, manufacturing dealing with methods. General, Gemini 3 Professional is a benchmark pushed, agent centered response to the subsequent part of huge scale multimodal AI.

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