Gemini 3.1 Pro Model Card

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: Intended Usage and Limitations Benefit and Intended Usage: Gemini 3.1 Pro is the next iteration in the Gemini 3.0 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 3.0 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 3.0 Pro safety assessment. Evaluation1 Description Gemini 3.1 Pro vs. Gemini 3.0 Pro Text to Text Safety Automated content safety evaluation measuring safety policies +0.10% (non-egregious) Multilingual Safety Automated safety policy evaluation across multiple languages +0.11% (non-egregious) Image to Text Safety Automated content safety evaluation measuring safety policies -0.33% Tone2 Automated evaluation measuring objective tone of model refusal +0.02% Unjustified-refusals Automated evaluation measuring model’s ability to respond to borderline prompts while remaining safe -0.08% 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 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. 1The 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. 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 3.0 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 (FSF) 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. FSF Results as of February, 2026: Domain Key Results for Gemini 3.1 Pro CCL CCL 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 1 CCL not reached Cyber (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 1 CCL not reached Harmful 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 reached Machine 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 1 CCLs not reached Misalignment (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