# Gemini 3.1 Pro

Google, released 2026-02-19

**Verdict: conditional.** Gemini 3.1 Pro is Google's most capable publicly available Pro-tier model, delivering strong reasoning with a 1M-token context window at competitive pricing — and it's the migration target for GitHub Copilot users leaving deprecated Gemini 2.5 Pro today. But it's decidedly previous-gen: Gemini 3.6 and 3.5 Flash now beat it on coding and agentic benchmarks at lower cost. Stick with it if you're deep in the Google Cloud / Vertex AI ecosystem; otherwise, the Flash line or competitors deliver more for less. Last verified 2026-07-31.

## Pricing

- Input: $2 / 1M
- Output: $12 / 1M
- Context: 1M tokens

## Strengths

- Deep reasoning on complex scientific and mathematical problems — ARC-AGI-2 77.1% more than doubles Gemini 3 Pro's 31.1%, GPQA Diamond 94.3% leads at launch
- Very large context analysis (1M tokens) — entire codebases, multi-document research, lengthy contracts in a single session
- Cost-conscious teams needing Pro-tier reasoning at $2/$12 — 2.5x cheaper input than Claude Opus 4.6 ($5) and 7.5x cheaper than Fable 5 ($15)
- Google Cloud / Vertex AI ecosystem users who need the strongest available Google model for complex reasoning workloads
- Cutting-edge coding and agentic workflows — Gemini 3.6 Flash and 3.5 Flash surpass it on Terminal-Bench, SWE-Bench Pro, and MCP Atlas at lower cost
- Security-sensitive production deployments — FAR.AI found 249 universal jailbreaks for ~$278 in July 2026 testing; Claude Fable 5 and GPT-5.6 Sol were unbreachable

## Benchmarks

- ARC-AGI-2: 77.1% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- MMLU (MMMLU): 92.6% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- GPQA Diamond: 94.3% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- SWE-Bench Verified: 80.6% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- SWE-Bench Pro (Public): 54.2% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- Terminal-Bench 2.0: 68.5% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- LiveCodeBench Pro: 2887 Elo (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- APEX-Agents: 33.5% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- HLE (no tools): 44.4% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- HLE (with tools): 51.4% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- MCP Atlas: 69.2% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- BrowseComp: 85.9% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- MRCR v2 (128K): 84.9% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- MMMU-Pro: 80.5% (https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- GDPval-AA Elo: 1317 (https://deepmind.google/models/model-cards/gemini-3-1-pro/)

## Sources

- [Google Blog: Gemini 3.1 Pro announcement](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/)
- [Google DeepMind: Gemini 3.1 Pro Model Card](https://deepmind.google/models/model-cards/gemini-3-1-pro/)
- [Gemini API pricing (no Cyber model priced) and Fairwind page](https://ai.google.dev/gemini-api/docs/pricing)
- [GitHub Changelog: Gemini 2.5 Pro deprecation Jul 31, 2026](https://github.blog/changelog/2026-07-02-upcoming-deprecation-of-gemini-2-5-pro-and-gemini-3-flash/)
- [GitHub Changelog: Gemini 3.1 Pro in Copilot](https://github.blog/changelog/2026-02-19-gemini-3-1-pro-is-now-in-public-preview-in-github-copilot/)
- [FAR.AI AI Security Leaderboard](https://finance.yahoo.com/technology/ai/articles/far-ai-launches-ai-security-194200169.html)
- [nxcode.io: Gemini 3.1 Pro Complete Guide](https://www.nxcode.io/resources/news/gemini-3-1-pro-complete-guide-benchmarks-pricing-api-2026)
- [Metacto: Gemini API Pricing](https://www.metacto.com/blogs/the-true-cost-of-google-gemini-a-guide-to-api-pricing-and-integration)

Canonical: https://neomanex.com/models/gemini-3-1-pro