# Gemini 3 Pro ![Organization](https://img.shields.io/badge/Org-Google%20DeepMind-4285f4) ![Type](https://img.shields.io/badge/Type-Multimodal%20reasoning-555) ![Context](https://img.shields.io/badge/Context-1M%20tokens-1f6feb) ![Pricing](https://img.shields.io/badge/Price-%242%20%2F%20%2412%20per%201M-2da44e) ![License](https://img.shields.io/badge/License-Proprietary-d29922) ![Modality](https://img.shields.io/badge/Modality-Multimodal-8957e5) ![Released](https://img.shields.io/badge/Released-Nov%202025-555) > **Quick answer:** Gemini 3 Pro is Google DeepMind's most intelligent model, released in preview on 18 November 2025. It is a natively multimodal reasoning model with a 1M-token context window, positioned as Google's best vibe-coding and agentic model. Google reports 91.9% on GPQA Diamond, 76.2% on SWE-bench Verified, and a leading 1501 Elo on LMArena. It is a closed, API-only model. ## At a Glance **Where Gemini 3 Pro leads** - Top-tier reasoning — 91.9% on GPQA Diamond and a leading 1501 Elo on LMArena. - Native multimodality — leading Video-MMMU (87.6%) and MMMU-Pro (81%), with text, image, video, and audio input. - Long-horizon planning — tops Vending-Bench 2 across a simulated business year; 1M-token context. **Where it lags** - Coding trails the very top — 76.2% on SWE-bench Verified, below Claude Opus 4.6's 81.42%. - Closed and API-only with no open weights to self-host, and the launch release is a preview. **Best for:** multimodal understanding and long-horizon agentic work where video/audio input and a 1M-token window matter. ## What Gemini 3 Pro Is Gemini 3 Pro is the first release in Google's Gemini 3 era — the model Google describes as its most intelligent yet, combining state-of-the-art reasoning, native multimodality, agentic tool use, and a 1M-token context window in a single system. It launched in preview across Google's product stack on day one: the Gemini app, AI Mode in Search, AI Studio, Vertex AI, Gemini CLI, and Google's new agentic development platform, Antigravity. A separate enhanced reasoning mode, Gemini 3 Deep Think, pushes scores higher and is gated behind additional safety review. Google's pitch centres on three jobs — learn, build, plan. On reasoning it tops LMArena at 1501 Elo and posts PhD-level scores on Humanity's Last Exam (37.5% without tools) and GPQA Diamond (91.9%). On building it leads WebDev Arena at 1487 Elo, scores 54.2% on Terminal-Bench 2.0, and reaches 76.2% on SWE-bench Verified. On planning it tops Vending-Bench 2, maintaining consistent tool use and decision-making across a full simulated year of running a business — a direct signal for long-horizon agent reliability. For Benchgen, Gemini 3 Pro is an API-benchmarked model with no open weights. A leaderboard Elo tells you it wins head-to-head preference battles, but only a verified, repeated-trial run on a real domain task shows whether that translates into reliable task completion under a consistent rubric. That reliability-under-repetition view is what Benchgen is built to surface. ## Specifications | Field | Value | |---|---| | Organization | Google DeepMind | | Model type | Natively multimodal reasoning model (with Deep Think mode) | | API identifier | `gemini-3-pro` (preview) | | Access | Closed, API only (Gemini app, AI Studio, Vertex AI, Gemini CLI, Antigravity) | | Context window | 1M tokens | | License | Proprietary | | Release date | 18 November 2025 (preview) | | Modality | Multimodal (text, image, video, audio, code) | ## Pricing | | Input (per 1M tokens) | Output (per 1M tokens) | |---|---|---| | Google AI Studio / Vertex AI | $2.00 | $12.00 | Prompts above 200k tokens are billed at $4.00 / $18.00 per 1M (output includes thinking tokens). Source: [Gemini API pricing](https://ai.google.dev/gemini-api/docs/pricing). ## Context Window Gemini 3 Pro has a **1M-token context window** — roughly **1,500 pages** of text in a single request — and reasons natively across text, images, video, and audio. That is enough to hold a large codebase, a long set of documents, or hours of video and audio at once, which underpins its leading multimodal and long-horizon planning results. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [GPQA Diamond](/benchmarks/gpqa-diamond) | 91.9% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | Humanity's Last Exam (no tools) | 37.5% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | [SWE-bench Verified](/benchmarks/swe-bench-verified) | 76.2% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | Terminal-Bench 2.0 | 54.2% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | MMMU-Pro | 81% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | Video-MMMU | 87.6% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | SimpleQA Verified | 72.1% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | MathArena Apex | 23.4% | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | LMArena (Elo) | 1501 | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | | WebDev Arena (Elo) | 1487 | [Google — A new era of intelligence with Gemini 3](https://blog.google/products/gemini/gemini-3/) | 2025-11 | *Scores above are reported by Google and shown for context. They are not Benchgen measurements. Figures depend on harness, tool, and effort settings — see Google's [evaluation methodology](https://deepmind.google/models/evals-methodology/gemini-3-pro) for details. Deep Think mode posts higher scores (e.g. GPQA Diamond 93.8%, HLE 41.0% without tools).* ## Gemini 3 Pro vs Alternatives | Model | Context | SWE-bench Verified | Price (in/out per 1M) | |---|---|---|---| | **Gemini 3 Pro** | 1M | 76.2% | $2 / $12 | | [GPT-5](/models/openai/gpt-5) | 400K | 74.9% | $1.25 / $10 | | [Claude Opus 4.6](/models/anthropic/claude-opus-4-6) | 1M (beta) | 81.42% | $5 / $25 | Gemini 3 Pro's edge is multimodality and long-horizon planning rather than peak coding: it matches GPT-5 on SWE-bench Verified but trails Claude Opus 4.6, while undercutting Opus on price and matching its 1M-token window. For a builder, it is the obvious pick when a workload involves video, audio, or very long multimodal context. *(Rival scores attributed to [OpenAI](https://openai.com/index/introducing-gpt-5/) and [Anthropic](https://www.anthropic.com/news/claude-opus-4-6); not Benchgen measurements.)* ## How Gemini 3 Pro Performs on Real Agent Tasks Gemini 3 Pro's most agent-relevant result is Vending-Bench 2: Google reports it tops the leaderboard for long-horizon planning, holding consistent tool use and on-task decision-making across a full simulated year of running a business without drifting. Paired with a 54.2% Terminal-Bench 2.0 score and the Antigravity platform — where agents plan, code, and validate end-to-end via browser and terminal — the model is clearly engineered for autonomous, multi-step work rather than single-turn answers. What those benchmarks cannot tell you is how Gemini 3 Pro behaves on a *specific* domain workflow, run many times, under a consistent rubric. Vending-Bench measures planning in a synthetic business sim; what agent builders actually need is reliable, repeated completion of the task in front of them — including how often the model fails, and why. That reliability-under-repetition view is what Benchgen is built to surface, and it is the dimension public benchmarks leave open. ## Use Gemini 3 Pro via API ```python from google import genai client = genai.Client(api_key="YOUR_API_KEY") response = client.models.generate_content( model="gemini-3-pro", contents="Summarize this contract clause...", ) print(response.text) ``` ## Related Models & Benchmarks - Compare: [GPT-5](/models/openai/gpt-5) · [Claude Opus 4.6](/models/anthropic/claude-opus-4-6) · [Grok 4](/models/xai/grok-4) · [Claude Sonnet 4.6](/models/anthropic/claude-sonnet-4-6) - Benchmarks: [GPQA Diamond](/benchmarks/gpqa-diamond) · [SWE-bench Verified](/benchmarks/swe-bench-verified) ## Frequently Asked Questions

What is Gemini 3 Pro?

It is Google DeepMind's most intelligent model, released in preview on 18 November 2025 — a natively multimodal reasoning model with a 1M-token context window, built for reasoning, agentic coding, and long-horizon planning.

What is Gemini 3 Pro's context window?

1 million tokens — roughly 1,500 pages of text — across text, image, video, and audio.

How much does Gemini 3 Pro cost?

$2 per million input tokens and $12 per million output tokens for prompts up to 200k tokens; $4 / $18 per million above 200k.

How does Gemini 3 Pro score on coding benchmarks?

Google reports 76.2% on SWE-bench Verified, 54.2% on Terminal-Bench 2.0, and a leading 1487 Elo on WebDev Arena.

Is Gemini 3 Pro open source?

No. It is a proprietary, closed model available only through Google's apps and API (AI Studio, Vertex AI); there are no downloadable weights.

--- *Specs and scores sourced from [Google's Gemini 3 announcement](https://blog.google/products/gemini/gemini-3/). Third-party benchmark scores attributed inline to Google. Benchgen scores, where shown, are produced on Benchgen infrastructure. Last updated 2026-06-17.*

Gemini 3 Pro — Training jobs

Training jobs