Gemini 3.1 Flash-Lite - 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 3 March 2026Gemini 3.1 Flash-Lite 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: March 2026 Model Information Model Data Implementation and Sustainability Distribution Evaluation Intended Usage and Limitations Ethics and Content Safety Model Information Description Gemini 3.1 Flash-Lite is an addition to the Gemini 3 series of highly-capable, natively multimodal, reasoning models. The model is cost-efficient and fast, optimized for high-volume, latency-sensitive tasks like translation and classification. Model dependencies Gemini 3.1 Flash-Lite 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 Flash-Lite is based on Gemini 3 Pro. For more information about the model architecture for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. Model Data Training Dataset Gemini 3.1 Flash-Lite is based on Gemini 3 Pro. For more information about the training dataset for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. Training Data Processing For more information about the training data processing for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. Implementation and Sustainability Hardware Gemini 3.1 Flash-Lite was trained using Google’s Tensor Processing Units (TPUs). TPUs are specifically designed to handle the massive computations involved in training LLMs and can speed up training considerably compared to CPUs. TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training, which can lead to better model quality. TPU Pods (large clusters of TPUs) also provide a scalable solution for handling the growing complexity of large foundation models. Training can be distributed across multiple TPU devices for faster and more efficient processing. The efficiencies gained through the use of TPUs are aligned with Google's commitment to operate sustainably. Software Training was done using JAX and ML Pathways. Distribution Gemini 3.1 Flash-Lite is distributed in the following channels; respective documentation shared in line: Google Cloud / Vertex AI Google AI Studio Gemini API Gemini App Gemini Search AI Overviews 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 Flash-Lite was evaluated across a range of benchmarks, including speed, reasoning, multimodal capabilities, factuality, agentic tool use, multi-lingual performance, coding, and long-context. Benchmark details on approach, results, and their methodologies can be found at: deepmind.google/models/evals-methodology/gemini-3-1-flash-lite Results Gemini 3.1 Flash-Lite results as of March, 2026 are below: BenchmarkNotesGemini 3.1 Flash-Lite HighGemini 2.5 Flash DynamicGemini 2.5 Flash-Lite DynamicGPT-5 mini HighClaude 4.5 Haiku Extended ThinkingGrok 4.1 Fast ReasoningInput price $/1M tokens, no cachingLower is better$0.25$0.30$0.10$0.25$1.00$0.20Output price $/1M tokensLower is better$1.50$2.50$0.40$2.00$5.00$0.50Output speed Tokens / s36324936671108145Humanity’s Last Exam Academic reasoning (full set, text + MM)No tools16.0%11.0%6.9%16.7%9.7%17.6%GPQA Diamond Scientific knowledgeNo tools86.9%82.8%66.7%82.3%73.0%84.3%MMMU-Pro Multimodal understanding and reasoningNo tools76.8%66.7%51.0%74.1%58.0%63.0%CharXiv Reasoning Information synthesis from complex charts73.2%63.7%55.5%75.5% (+ python)61.7%31.6%Video-MMMU Knowledge acquisition from videos84.8%79.2%60.7%82.5%—74.6%SimpleQA Verified Parametric knowledge43.3%28.1%11.5%9.5%5.5%19.5%FACTS Benchmark Suite Factuality benchmark across grounding, parametric, search, and MM.40.6%50.4%17.9%33.7%18.6%42.1%MMMLU Multilingual Q&A88.9%86.6%84.5%84.9%83.0%86.8%LiveCodeBench Code generation (UI: 1/1/2025-5/1/2025)72.0%62.6%34.3%80.4%53.2%76.5%MRCR v2 (8-needle) Long context performance128k (average)60.1%54.3%30.6%52.5%35.3%54.6%1M (pointwise)12.3%21.0%5.4%Not supportedNot supported6.1% Methodology: deepmind.google/models/evals-methodology/gemini-3-1-flash-lite Intended Usage and Limitations Benefit and Intended Usage Gemini 3.1 Flash-Lite is well suited for applications that require high volume, cost-efficient and low latency tasks. Known Limitations For more information about the known limitations for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. Acceptable Usage For more information about the acceptable usage for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. Ethics and Content Safety Evaluation Approach For more information about the evaluation approach for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. Safety Policies For more information about the safety policies for Gemini 3.1 Flash-Lite, 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 Flash-Lite outperforms Gemini 2.5 Flash-Lite across both safety and tone, while keeping unjustified refusals low. We mark improvements in green and regressions in red. Evaluation1DescriptionGemini 3.1 Flash-Lite vs. Gemini 2.5 Flash-LiteText to Text SafetyAutomated content safety evaluation measuring safety policies-1.18%Multilingual SafetyAutomated safety policy evaluation across multiple languages-1.84%Image to Text SafetyAutomated content safety evaluation measuring safety policies-21.7%Tone2Automated evaluation measuring objective tone of model refusal+14.59%Unjustified-refusalsAutomated evaluation measuring model’s ability to respond to borderline prompts while remaining safe-14.41% 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 Flash-Lite 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 or improved safety performance compared to Gemini 2.5 Flash. Like 3 Pro, the scope of red teaming covered potential issues outside of our strict policies, and found no egregious concerns. Frontier Safety Assessment Gemini 3.1 Flash-Lite is part of the Gemini 3 family of models. We rely on our evaluation of Gemini 3.1 Pro with Deep Think mode for Frontier Safety as it is the most generally capable model as of publication of this model card, and it did not reach any Critical Capability Levels (CCLs) outlined in our Frontier Safety Framework. Our assessments have shown that Gemini 3.1 Flash-Lite is less capable than Gemini 3.1 Pro, therefore based on Gemini 3.1 Pro, we are confident that Gemini 3.1 Flash-Lite is also unlikely to reach any CCLs. For more information, read the Gemini 3.1 Pro Model Card. Risks and Mitigations For more information about the risks and mitigations for Gemini 3.1 Flash-Lite, see the Gemini 3 Pro model card. 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