February 2026

Gemini 3.1 Pro
Model Card
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. See the
Google DeepMind site for a comprehensive list of model cards.

Published: February 2026

                                       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.

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                             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.

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                                            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:

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                                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.

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                                           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.

                                                                                                 Gemini 3.1 Pro
Evaluation1                     Description
                                                                                                vs. Gemini 3.0 Pro

                                Automated content safety evaluation
Text to Text Safety                                                                        +0.10% (non-egregious)
                                measuring safety policies

                                Automated safety policy evaluation across
Multilingual Safety                                                                        +0.11% (non-egregious)
                                multiple languages

                                Automated content safety evaluation
Image to Text Safety                                                                                 -0.33%
                                measuring safety policies

                                Automated evaluation measuring
Tone2                                                                                               +0.02%
                                objective tone of model refusal
                                Automated evaluation measuring model’s
Unjustified-refusals            ability to respond to borderline prompts                             -0.08%
                                while remaining safe

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

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.

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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.

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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               Uplift Level 1   CCL not
                  highly accurate and actionable information but                         reached
                  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.

     Cyber        (3.1 Pro) We conducted additional testing on the      Uplift Level 1   CCL not
                  model in this domain as Gemini 3 Pro had                               reached
                  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.

    Harmful       (Deep Think mode) Evaluations indicated that          Level 1          CCL not
  Manipulation    the model showed higher manipulative efficacy         (exploratory)    reached
                  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.

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  Machine       (Deep Think mode) The model shows gains on         Acceleration    CCLs not
Learning R&D    RE-Bench compared to Gemini 3 Pro, with a          level 1         reached
                human-normalised average score of 1.27             Automation
                compared to Gemini 3 Pro's score of 1.04. On
                                                                   level 1​
                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.

Misalignment    (Deep Think mode) On stealth evaluations, the      Instrumental    CCLs not
(Exploratory)   model performs similarly to Gemini 3 Pro. On       Reasoning​      reached
                situational awareness, the model is stronger       Levels 1 + 2
                than Gemini 3 Pro: on three challenges which       (exploratory)
                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.

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