Gemini 3.1 Pro - Model Card — Google DeepMind Skip to main content Explore our next generation AI systems Explore models Gemini Gemini Build intelligent agents Gemini Omni Create anything from anything Nano Banana Create and edit detailed images Gemini Audio Talk, create and control audio Specialized models Veo Generate cinematic video with audio Imagen Generate high-quality images from text Lyria Generate high fidelity music and audio World models & embodied AI Genie 3 Generate and explore interactive worlds Gemini Robotics Perceive, reason, use tools and interact Open models Gemma Build responsible AI applications at scale Our latest AI breakthroughs and updates from the lab Explore research Breakthroughs SIMA 2 An agent that plays, reasons, and learns with you Genie 3 Generate and explore interactive worlds AlphaGo Mastering the game of Go Gemini Robotics Perceive, reason, use tools and interact Learn more Evals Publications Responsibility Unlocking a new era of discovery with AI Explore science Breakthroughs AlphaFold Predict protein structures with high accuracy WeatherNext Fast and accurate AI weather forecasting AlphaEarth Map our planet in unprecedented detail AlphaEvolve Design advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills Our mission is to build AI responsibly to benefit humanity About Google DeepMind Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Models Explore our next generation AI systems Explore models Gemini Gemini Build intelligent agents Gemini Omni Create anything from anything Nano Banana Create and edit detailed images Gemini Audio Talk, create and control audio Specialized models Veo Generate cinematic video with audio Imagen Generate high-quality images from text Lyria Generate high fidelity music and audio World models & embodied AI Genie 3 Generate and explore interactive worlds Gemini Robotics Perceive, reason, use tools and interact Open models Gemma Build responsible AI applications at scale Research Our latest AI breakthroughs and updates from the lab Explore research Breakthroughs SIMA 2 An agent that plays, reasons, and learns with you Genie 3 Generate and explore interactive worlds AlphaGo Mastering the game of Go Gemini Robotics Perceive, reason, use tools and interact Learn more Evals Publications Responsibility Science Unlocking a new era of discovery with AI Explore science Breakthroughs AlphaFold Predict protein structures with high accuracy WeatherNext Fast and accurate AI weather forecasting AlphaEarth Map our planet in unprecedented detail AlphaEvolve Design advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills About Our mission is to build AI responsibly to benefit humanity About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Published 19 February 2026Gemini 3.1 Pro Learn more View PDF version Model Cards are intended to provide essential information on Gemini models, including known limitations, mitigation approaches, and safety performance. Model cards may be updated from time-to-time; for example, to include updated evaluations as the model is improved or revised. Published: February 2026 Model Information Model Data Implementation and Sustainability Distribution Evaluation Intended Usage and Limitations Ethics and Content Safety Frontier Safety Model Information Description Gemini 3.1 Pro is the next iteration in the Gemini 3 series of models, a suite of highly capable, natively multimodal reasoning models. As of this model card’s date of publication, Gemini 3.1 Pro is Google’s most advanced model for complex tasks. Gemini 3.1 Pro can comprehend vast datasets and challenging problems from massively multimodal information sources, including text, audio, images, video, and entire code repositories. Model dependencies Gemini 3.1 Pro is based on Gemini 3 Pro. Inputs Text strings (e.g., a question, a prompt, document(s) to be summarized), images, audio, and video files, with a token context window of up to 1M. Outputs Text, with a 64K token output. Architecture Gemini 3.1 Pro is based on Gemini 3 Pro. For more information about the model architecture for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Model Data Training Dataset Gemini 3.1 Pro is based on Gemini 3 Pro. For more information about the training dataset for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Training Data Processing For more information about the training data processing for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Implementation and Sustainability Hardware Gemini 3.1 Pro is based on Gemini 3 Pro. For more information about the hardware for Gemini 3.1 Pro and our continued commitment to operate sustainably, see the Gemini 3 Pro model card. Software Gemini 3.1 Pro is based on Gemini 3 Pro. For more information about the software for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Distribution Gemini 3.1 Pro is distributed in the following channels; respective documentation shared in line: Gemini App Google Cloud / Vertex AI Google AI Studio Gemini API Google Antigravity Gemini Enterprise NotebookLM Our models are available to downstream providers via an application program interface (API) and subject to relevant terms of use. There is no required hardware or software to use the model. For AI Studio and Gemini API, see the Gemini API Additional Terms of Service; for Vertex AI, see Google Cloud Platform Terms of Service. For more information, see Gemini Model API instructions and Gemini API in Vertex AI quickstart. Evaluation Approach Gemini 3.1 Pro was evaluated across a range of benchmarks, including reasoning, multimodal capabilities, agentic tool use, multi-lingual performance, and long-context. Additional benchmarks and details on approach, results and their methodologies can be found at: deepmind.google/models/evals-methodology/gemini-3-1-pro. Results Gemini 3.1 Pro significantly outperforms Gemini 3 Pro across a range of benchmarks requiring enhanced reasoning and multimodal capabilities. Results as of February 2026 are listed below: BenchmarkNotesGemini 3.1 Pro Thinking (High)Gemini 3 Pro Thinking (High)Sonnet 4.6 Thinking (Max)Opus 4.6 Thinking (Max)GPT-5.2 Thinking (xhigh)GPT-5.3-Codex Thinking (xhigh)Humanity's Last Exam Academic reasoning (full set, text + MM) No tools44.4%37.5%33.2%40.0%34.5%— Search (blocklist) + Code 51.4%45.8%49.0%53.1%45.5%—ARC-AGI-2 Abstract reasoning puzzlesARC Prize Verified77.1%31.1%58.3%68.8%52.9%—GPQA Diamond Scientific knowledgeNo tools94.3%91.9%89.9%91.3%92.4%—Terminal-Bench 2.0 Agentic terminal codingTerminus-2 harness68.5%56.9%59.1%65.4%54.0%64.7%Other best self-reported harness————62.2% (Codex)77.3% (Codex)SWE-Bench Verified Agentic codingSingle attempt80.6%76.2%79.6%80.8%80.0%—SWE-Bench Pro (Public) Diverse agentic coding tasks Single attempt54.2%43.3%——55.6%56.8%LiveCodeBench Pro Competitive coding problems from Codeforces, ICPC, and IOI Elo28872439——2393—SciCode Scientific research coding59%56%47%52%52%—APEX-Agents Long horizon professional tasks 33.5%18.4%—29.8%23.0%—GDPval-AA Elo Expert tasks13171195163316061462—τ2-bench Agentic and tool useRetail90.8%85.3%91.7%91.9%82.0%—Telecom99.3%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 searchSearch + Python + Browse85.9%59.2%74.7%84.0%65.8%—MMMU-Pro Multimodal understanding and reasoning No tools80.5%81.0%74.5%73.9%79.5%—MMMLU Multilingual Q&A92.6%91.8%89.3%91.1%89.6%—MRCR v2 (8-needle) Long context performance128k (average)84.9%77.0%84.9%84.0%83.8%—1M (pointwise)26.3%26.3%Not supportedNot supportedNot supported— Methodology: deepmind.google/models/evals-methodology/gemini-3-1-pro Intended Usage and Limitations Benefit and Intended Usage Gemini 3.1 Pro is the next iteration in the Gemini 3 series of models, a suite of highly intelligent and adaptive models, capable of helping with real-world complexity, solving problems that require enhanced reasoning and intelligence, creativity, strategic planning and making improvements step-by-step. It is particularly well-suited for applications that require: agentic performance advanced coding long context and/or multimodal understanding algorithmic development Known Limitations For more information about the known limitations for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Acceptable Usage For more information about the acceptable usage for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Ethics and Content Safety Evaluation Approach For more information about the evaluation approach for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Safety Policies For more information about the safety policies for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Training and Development Evaluation Results Results for some of the internal safety evaluations conducted during the development phase are listed below. The evaluation results are for automated evaluations and not human evaluation or red teaming. Scores are provided as an absolute percentage increase or decrease in performance compared to the indicated model, as described below. Overall, Gemini 3.1 Pro outperforms Gemini 3 Pro across both safety and tone, while keeping unjustified refusals low. We mark improvements in green and regressions in red. Safety evaluations of Gemini 3.1 Pro produced results consistent with the original Gemini 3 Pro safety assessment. Evaluation1DescriptionGemini 3.1 Pro vs. Gemini 3 ProText to Text SafetyAutomated content safety evaluation measuring safety policies+0.10% (non-egregious)Multilingual SafetyAutomated safety policy evaluation across multiple languages+0.11% (non-egregious)Image to Text SafetyAutomated content safety evaluation measuring safety policies-0.33%Tone2Automated evaluation measuring objective tone of model refusal+0.02%Unjustified-refusalsAutomated evaluation measuring model’s ability to respond to borderline prompts while remaining safe-0.08% 1 The ordering of evaluations in this table has changed from previous iterations of the 2.5 Flash-Lite model card in order to list safety evaluations together and improve readability. The type of evaluations listed have remained the same. 2 For tone and instruction following, a positive percentage increase represents an improvement in the tone of the model on sensitive topics and the model’s ability to follow instructions while remaining safe compared to Gemini 2.5 Pro. We mark improvements in green and regressions in red. We continue to improve our internal evaluations, including refining automated evaluations to reduce false positives and negatives, as well as update query sets to ensure balance and maintain a high standard of results. The performance results reported below are computed with improved evaluations and thus are not directly comparable with performance results found in previous Gemini model cards. We expect variation in our automated safety evaluations results, which is why we review flagged content to check for egregious or dangerous material. Our manual review confirmed losses were overwhelmingly either a) false positives or b) not egregious. Human Red Teaming Results We conduct manual red teaming by specialist teams who sit outside of the model development team. High-level findings are fed back to the model team. For child safety evaluations, Gemini 3.1 Pro satisfied required launch thresholds, which were developed by expert teams to protect children online and meet Google’s commitments to child safety across our models and Google products. For content safety policies generally, including child safety, we saw similar safety performance compared to Gemini 3 Pro. Risks and Mitigations For more information about the risks and mitigations for Gemini 3.1 Pro, see the Gemini 3 Pro model card. Frontier Safety Our Frontier Safety Framework includes rigorous evaluations that address risks of severe harm from frontier models, covering five risk domains: CBRN (chemical, biological, radiological and nuclear information risks), cyber, harmful manipulation, machine learning R&D and misalignment. Our frontier safety strategy is based on a “safety buffer” to prevent models from reaching critical capability levels (CCLs), i.e. if a frontier model does not reach the alert threshold for a CCL, we can assume models developed before the next regular testing interval will not reach that CCL. We conduct continuous testing, evaluating models at a fixed cadence and when a significant capability jump is detected. (Read more about this in our approach to technical AGI safety.) Following FSF protocols, we conducted a full evaluation of Gemini 3.1 Pro (focusing on Deep Think mode). We found that the model remains below alert thresholds for the CBRN, harmful manipulation, machine learning R&D, and misalignment CCLs. As previous models passed the alert threshold for cyber, we performed more additional testing in this domain on Gemini 3.1 Pro with and without Deep Think mode, and found that the model remains below the cyber CCL. More details on our evaluations and the mitigations we deploy can be found in the Gemini 3 Pro Frontier Safety Framework Report. DomainKey Results for Gemini 3.1 ProCCLCCL reached?CBRN(Deep Think mode) The model can provide highly accurate and actionable information but still fails to offer novel or sufficiently complete and detailed instructions for critical stages, to significantly enhance the capabilities of low to medium resourced threat actors required for the CCL. We continue to deploy mitigations in this domain.Uplift Level 1CCL not reachedCyber(3.1 Pro) We conducted additional testing on the model in this domain as Gemini 3 Pro had previously reached the alert threshold. The model shows an increase in cyber capabilities compared to Gemini 3 Pro. As with Gemini 3 Pro, the model has reached the alert threshold, but still does not reach the levels of uplift required for the CCL. (Deep Think mode) Accounting for inference costs, the model with Deep Think mode performs considerably worse than without Deep Think mode. Even at high levels of inference, results for the model with Deep Think mode do not suggest higher capability than without Deep Think mode. We continue to deploy mitigations in this domain.Uplift Level 1CCL not reachedHarmful Manipulation(Deep Think mode) Evaluations indicated that the model showed higher manipulative efficacy for belief change metrics compared to a non-AI baseline, with the maximum odds ratio of 3.6x, which is the same as Gemini 3 Pro, and did not reach the alert threshold.Level 1 (exploratory)CCL not reachedMachine Learning R&D(Deep Think mode) The model shows gains on RE-Bench compared to Gemini 3 Pro, with a human-normalised average score of 1.27 compared to Gemini 3 Pro's score of 1.04. On one particular challenge, Optimise LLM Foundry, it scores double the human-normalised baseline score (reducing the runtime of a fine-tuning script from 300 seconds to 47 seconds, compared to the human reference solution of 94 seconds). However, the model’s average performance across all challenges remains beneath the alert threshold for the CCLs.Acceleration level 1 Automation level 1CCLs not reachedMisalignment (Exploratory)(Deep Think mode) On stealth evaluations, the model performs similarly to Gemini 3 Pro. On situational awareness, the model is stronger than Gemini 3 Pro: on three challenges which no other model has been able to consistently solve, max tokens, context size mod, and oversight frequency, the model achieves a success rate of almost 100%. However, its performance on other challenges is inconsistent, and thus the model does not reach the alert threshold.Instrumental Reasoning Levels 1 + 2 (exploratory)CCLs not reached Latest model cards Lyria 3 Learn more Gemini 3.1 Pro Learn more Gemini 3.1 Flash Image Learn more Gemini 3.1 Flash-Lite Learn more Gemini 3.1 Flash Live Learn more Veo 3.1 Lite Learn more Follow us Sign up for updates on our latest innovations I accept Google's Terms and Conditions and acknowledge that my information will be used in accordance with Google's Privacy Policy. 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