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- Feb 19, 202690% CONFIDENCEfrom the source
Gemini 3.1 Pro Released
- Google released Gemini 3.1 Pro in preview on 19 February 2026, three months after Gemini 3 Pro, describing it as the upgraded core intelligence behind recent science and research breakthroughs[1]
- It is "a smarter, more capable baseline for complex problem-solving", with a verified 77.1% on ARC-AGI-2, which tests entirely new logic patterns[1]
- Google kept it in preview to validate the updates and improve agentic workflows before general availability "soon"[1]
Notable features
- Available in the Gemini app with higher limits for Google AI Pro and Ultra plans, and in NotebookLM for Pro and Ultra users only[1]
- Developers and enterprises get it in preview through the Gemini API in AI Studio, Antigravity and Vertex AI, plus Gemini Enterprise[1]
- Google's demo has it code an interactive 3D starling murmuration that users steer with hand tracking and that plays a generative score[1]
Benchmarks[3]
| Benchmark | Gemini 3.1 Pro | Gemini 3 Pro | Sonnet 4.6 | Opus 4.6 | GPT-5.2 | GPT-5.3-Codex |
|---|---|---|---|---|---|---|
| Humanity's Last Exam (Academic reasoning (full set, text + MM), No tools) | 44.4% | 37.5% | 33.2% | 40.0% | 34.5% | — |
| Humanity's Last Exam (Academic reasoning (full set, text + MM), Search (blocklist) + Code) | 51.4% | 45.8% | 49.0% | 53.1% | 45.5% | — |
| ARC-AGI-2 (Abstract reasoning puzzles, ARC Prize Verified) | 77.1% | 31.1% | 58.3% | 68.8% | 52.9% | — |
| GPQA Diamond (Scientific knowledge, No tools) | 94.3% | 91.9% | 89.9% | 91.3% | 92.4% | — |
| Terminal-Bench 2.0 (Agentic terminal coding, Terminus-2 harness) | 68.5% | 56.9% | 59.1% | 65.4% | 54.0% | 64.7% |
| Terminal-Bench 2.0 (Agentic terminal coding, Other best self-reported harness) | — | — | — | — | 62.2% | 77.3% |
| SWE-Bench Verified (Agentic coding, Single attempt) | 80.6% | 76.2% | 79.6% | 80.8% | 80.0% | — |
| SWE-Bench Pro (Public) (Diverse agentic coding tasks, Single attempt) | 54.2% | 43.3% | — | — | 55.6% | 56.8% |
| LiveCodeBench Pro (Competitive coding problems from Codeforces, ICPC, and IOI, Elo) | 2887 | 2439 | — | — | 2393 | — |
| SciCode (Scientific research coding) | 59% | 56% | 47% | 52% | 52% | — |
| APEX-Agents (Long horizon professional tasks) | 33.5% | 18.4% | — | 29.8% | 23.0% | — |
| GDPval-AA Elo (Expert tasks) | 1317 | 1195 | 1633 | 1606 | 1462 | — |
| τ2-bench (Agentic and tool use, Retail) | 90.8% | 85.3% | 91.7% | 91.9% | 82.0% | — |
| τ2-bench (Agentic and tool use, Telecom) | 99.3% | 98.0% | 97.9% | 99.3% | 98.7% | — |
| MCP Atlas (Multi-step workflows using MCP) | 69.2% | 54.1% | 61.3% | 59.5% | 60.6% | — |
| BrowseComp (Agentic search, Search + Python + Browse) | 85.9% | 59.2% | 74.7% | 84.0% | 65.8% | — |
| MMMU-Pro (Multimodal understanding and reasoning, No tools) | 80.5% | 81.0% | 74.5% | 73.9% | 79.5% | — |
| MMMLU (Multilingual Q&A) | 92.6% | 91.8% | 89.3% | 91.1% | 89.6% | — |
| MRCR v2 (8-needle) (Long context performance, 128k (average)) | 84.9% | 77.0% | 84.9% | 84.0% | 83.8% | — |
| MRCR v2 (8-needle) (Long context performance, 1M (pointwise)) | 26.3% | 26.3% | — | — | — | — |
References 385% CONFIDENCEOverall confidence: 85%How well the pin's source and references back up its dates.Weighted average of how firmly 3 references, the source included, support the pin's start and end times; a reference counts half as much for every 180 days older than the newestShow all pins at 75% confidence or better
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- [1]90%blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-problog.google· Posted Sep 30, 2026· Starts Feb 19, 2026 ✓· 41% of score
Google's post "Gemini 3.1 Pro: A smarter model for your most complex tasks" is dated 19 February 2026 and says it is rolling out that day to developers, enterprises and consumers.
- [2]75%Gemini (language model) - Wikipediaen.wikipedia.org· Added Sep 30, 2026· 41% of score
The Wikipedia article lists Gemini 3.1 Pro as released in preview on 19 February 2026.
- [3]95%Gemini 3.1 Pro - Model Carddeepmind.google· Published Feb 19, 2026· 17% of score
DeepMind's model card gives the benchmark table against the model's predecessor and named rivals.
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