Google Launches Gemini 3.1 Pro for Enhanced Developer Coding Capabilities

February 19, 2026

Details

  • Google for Developers announced Gemini 3.1 Pro, a smarter AI model optimized for complex coding problems, available now via Gemini API in Google AI Studio, Vertex AI, Gemini CLI, Android Studio, and the new agentic development platform.
  • The model features advanced reasoning for synthesizing large datasets, generating animated SVGs from text prompts, and handling multi-step technical tasks with a 1M-token context window supporting text, images, video, audio, and code.\[1]\[3][4]
  • Key improvements include better software engineering (SWE) performance, agentic capabilities in finance and spreadsheets, enhanced token efficiency, and a new MEDIUM thinking level to balance cost, speed, and performance.\[2]\[3]
  • Benchmarks show strong results: 77.1% on ARC-AGI-2 (double Gemini 3 Pro), 94.3% on GPQA Diamond, 80.6% on SWE-Bench Verified, 85.9% on BrowseComp, and 2887 Elo on LiveCodeBench Pro.[1]
  • Access is in preview for developers; also rolling out to Gemini app, NotebookLM, GitHub Copilot (Pro, Pro+, Business, Enterprise), with general availability soon. Knowledge cutoff is January 2025.\[1]\[5]
  • This is Google's first .1 increment, bringing Deep Think reasoning from Gemini 3 to a wider audience, outperforming prior versions significantly.[1]

Impact

Google's Gemini 3.1 Pro rollout intensifies competition in the AI coding space, where it claims top scores like 2887 Elo on LiveCodeBench Pro and 80.6% on SWE-Bench Verified, surpassing Gemini 3 Pro and pressuring rivals like OpenAI's GPT series and Anthropic's Claude, which lag on agentic coding benchmarks per recent evaluations. By integrating into GitHub Copilot and Vertex AI with 1M-token context and multimodal support, it lowers barriers for developers building agentic workflows, potentially accelerating adoption in enterprise tools for finance, spreadsheets, and front-end tasks like SVG animation. This positions Google ahead in on-device and cloud inference efficiency, with new thinking levels optimizing cost-performance trade-offs amid GPU constraints. Over the next 12-24 months, expect it to steer R&D toward reliable long-horizon agents, influencing funding toward hybrid reasoning models while aligning with trends in AI safety through grounded, fact-consistent outputs.

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