Gemini 3.1 Pro
Gemini 3.1 Pro: Google DeepMind advanced reasoning model with 1M context window, multimodal capabilities, and 94.3% GPQA Diamond score.
Advanced reasoning model with 1M context window and exceptional multimodal capabilities. Strengths: scientific reasoning, video understanding, extended context. Best for batch processing and complex analysis tasks.
Gemini 3.1 Pro available globally via Gemini API, Vertex AI, and Gemini CLI. Deployed on TPU v5p infrastructure across Google Cloud regions worldwide. Teams in Singapore, US, Europe can access consistent capabilities with regional pricing variations. Extended context above 200K tokens applies globally.
Provider: Google DeepMind · Family: Gemini 3.1
Context window: 1,000,000 tokens · Max output: 65,536
About Gemini 3.1 Pro
Gemini 3.1 Pro represents Google DeepMind's latest advancement in frontier AI, released on February 19, 2026. Building on the success of Gemini 3, this model introduces a 1 million token context window paired with exceptional multimodal reasoning capabilities that span text, images, video, and audio. The architecture leverages a Sparse Mixture of Experts design, where parameter efficiency meets inference speed through selective expert activation across 50-80 billion active parameters per token. The model excels across multiple modality boundaries, processing up to 900 images, 8.4 hours of continuous audio, and 1 hour of video at 10 frames per second for detailed action understanding. This multimodal depth positions Gemini 3.1 Pro as a specialized tool for enterprises handling complex, diverse data sources ranging from scientific research to video analysis. The instruction-following capabilities have been refined significantly, enabling sophisticated tool use and agentic workflows that exceed prior generations. On benchmark performance, Gemini 3.1 Pro demonstrates particular strength in mathematical and scientific reasoning, achieving 91.2% on AIME 2025 and 94.3% on GPQA Diamond, placing it ahead of competing models in these specialized domains. While SWE-bench performance at 80.6% trails slightly behind pure code-focused models, the broader reasoning capabilities and vision understanding offer distinct advantages for multi-step problem solving that combines code, mathematics, and visual analysis. The model operates with a measured approach to safety and instruction following. Evaluations confirmed that Gemini 3.1 Pro maintains child safety thresholds and reduces unjustified refusals compared to prior releases, striking a configurable balance suitable for enterprise environments requiring nuanced content policies. Pricing remains competitive at $2.00 per million input tokens and $12.00 per million output tokens for standard context lengths, with extended context above 200K tokens priced at $4.00 and $18.00 respectively. Output latency characteristics show a high time-to-first-token of approximately 28 seconds, reflecting the model's extended thinking and reasoning optimizations, balanced by strong output generation speed of 116 tokens per second once generation begins. This profile makes Gemini 3.1 Pro most suitable for non-interactive reasoning tasks, batch processing, and scenarios where inference time is secondary to reasoning quality. Deployment flexibility spans Google's Gemini API, Vertex AI on Google Cloud, and the Gemini CLI, providing enterprise teams with multiple integration pathways. The model's ability to handle massive context windows opens new possibilities for summarization, analysis, and retrieval workflows over entire documents, codebases, and research corpora that were previously infeasible within token constraints.
Frequently Asked Questions
What makes Gemini 3.1 Pro different from Gemini 3 Pro?
Gemini 3.1 Pro introduces several refinements over Gemini 3, including improved instruction following for tool use and agentic workflows, enhanced video understanding at 10 frames per second sampling, and better performance on mathematical reasoning benchmarks like AIME. The multimodal capabilities remain foundational, but the reasoning improvements and extended context handling represent meaningful upgrades. Safety evaluations show reduced unjustified refusals while maintaining child safety thresholds. The architecture continues using Sparse Mixture of Experts for parameter efficiency.
How does the 1 million token context window enable new use cases?
The 1M context window allows processing entire books, months of conversation history, complete codebases, and comprehensive research papers in a single prompt without chunking or retrieval augmentation. This enables direct analysis of massive datasets, detailed video-to-text summarization of hour-long recordings, extraction of patterns across large documents, and complex reasoning over entire repositories. Use cases include analyzing full regulatory documents, summarizing complete video transcripts, and in-context learning across extensive examples without hitting token limits.
What are the pricing implications for organizations using Gemini 3.1 Pro?
Standard pricing is $2 per million input tokens and $12 per million output tokens for contexts under 200K tokens. Extended context usage above 200K tokens doubles the cost to $4 and $18 respectively. For organizations processing 10 million output tokens monthly, Gemini 3.1 Pro costs approximately $120, significantly lower than Claude Opus 4.7 at $250 and GPT-5.5 at $300. The extended context pricing creates a cost trade-off where 1M context processing becomes expensive but viable for critical analysis tasks.
How does Gemini 3.1 Pro perform on code-related benchmarks?
Gemini 3.1 Pro achieves 80.6% on SWE-bench Verified, trailing Claude Opus 4.7 which scores 64.3% on the stricter SWE-bench Pro metric. While this positions Gemini solidly in the frontier tier for code generation, it indicates that pure software engineering tasks remain better suited to Claude or GPT-5 models. However, when code tasks combine with mathematical reasoning, system design, or architecture analysis, Gemini 3.1 Pro excels due to its superior reasoning capabilities and ability to incorporate long context of existing codebases.
What safety features and content policies does Gemini 3.1 Pro implement?
Gemini 3.1 Pro operates with a configurable safety posture, allowing enterprise teams to tune content policies to their specific requirements. The model was trained with dedicated child safety evaluations and confirmed to meet Google launch thresholds for online child protection. Safety training data and mitigations are documented in the official Model Card. The system maintains lower rates of unjustified refusals compared to Gemini 3 Pro, enabling more nuanced handling of sensitive topics while preserving safety guardrails for illegal content and harmful requests.
Which deployment platforms support Gemini 3.1 Pro?
Gemini 3.1 Pro is available through multiple deployment pathways including the Gemini API for web and mobile applications, Vertex AI on Google Cloud for enterprise deployments, and the Gemini CLI for development workflows. The Gemini app provides access with Google AI Pro and Ultra subscription tiers. Developers can choose between these platforms based on latency requirements, integration depth, scale demands, and cost optimization preferences. All platforms expose the same 1M context window and multimodal capabilities.
How does Gemini 3.1 Pro handle video and audio understanding compared to text?
Gemini 3.1 Pro processes video at 10 frames per second, enabling detection of rapid motion and detailed action sequences that lower sampling rates would miss. The model handles up to 1 hour of continuous video and 8.4 hours of audio in a single request. Video understanding benchmarks show particular strength in analyzing complex visual scenes, understanding fast-paced actions like golf swings, and correlating visual patterns with temporal context. This multimodal depth makes Gemini 3.1 Pro exceptional for video analysis, while text-only tasks remain optimized for other models depending on the specific domain.
What is the knowledge cutoff date for Gemini 3.1 Pro?
Gemini 3.1 Pro was trained with a knowledge cutoff of January 2025. Information beyond this date requires tool use or real-time data integration for accurate responses. The model cannot reliably answer questions about events, publications, or developments after January 2025 without external information sources. For time-sensitive applications requiring current information, teams should implement prompt-based grounding with live data sources or recent retrieval results.