Google Releases Gemini 3.1 Pro
Gemini 3.1 Pro is Google's latest reasoning-focused multimodal AI model, released February 19, 2026, achieving top benchmarks like 77.1% on ARC-AGI-2 and 80.6% on SWE-Bench for complex tasks such as coding, visual explanations, and agentic workflows. It improves on Gemini 3 Pro with deeper reasoning, efficiency, and 1M token context, now available via Gemini app and API for developers.
Gemini 3.1 Pro, released in preview on February 19, 2026, marks Google's latest advancement in AI reasoning capabilities, building directly on the Gemini 3 series launched in November 2025. Top news highlights its record-breaking benchmarks, including 77.1% on ARC-AGI-2 (more than double Gemini 3 Pro's ~31-37.5%), 80.6% on SWE-Bench Verified for coding, and 94.3% on GPQA Diamond for expert-level questions, positioning it as a leader in abstract reasoning and complex problem-solving. It's now rolling out to consumers via the Gemini app (for Pro/Ultra plans), developers through Gemini API, Google AI Studio, Vertex AI, and enterprises, enabling practical uses like code-based animations, live dashboards, and interactive 3D prototypes.
Key improvements focus on core reasoning depth for tasks beyond simple answers, such as synthesizing complex data, visual explanations, and creative coding projects like SVG animations or 3D simulations with hand-tracking. This is achieved through enhanced model architecture emphasizing "unprecedented depth and nuance," resulting in 15% better output quality with fewer tokens, lower latency (medium mode matches prior high mode), improved long-task stability for agents, and slight safety boosts without compromising capabilities. These gains stem from rapid iterations driven by user feedback since Gemini 3 Pro, making it more efficient for real-world applications like aerospace telemetry visualization or literary-themed websites.
Compared to rivals, Gemini 3.1 Pro leads on 13 of 16 key benchmarks, surpassing earlier models like Claude 3.5 Sonnet (strong in reasoning/coding but smaller 200K context vs. 1M tokens) and GPT-4o, while offering multimodal strengths (image/voice/video) over Llama 3.1's open-source math focus. It doubles ARC-AGI-2 performance over its predecessor and provides agentic advantages like tool orchestration, though pricing remains $2 input/$12 output per million tokens. Next steps include preview validation, general availability soon, and advancements in ambitious agentic workflows, with higher limits for Pro users and expanded API access.
Source : Google Gemini Models