According to Jeff Dean on X, Google’s Gemini 3.1 Pro achieves 77.1% on the ARC-AGI-2 benchmark, more than doubling the reasoning performance of Gemini 3 Pro, with a side-by-side comparison showing visible improvements (source: Jeff Dean, X, Feb 19, 2026). According to Jeff Dean, the result signals stronger general reasoning and tool-use potential, positioning Gemini 3.1 Pro for complex enterprise workflows like multi-step data analysis, agentic planning, and code synthesis. As reported by Jeff Dean, the performance gain suggests improved chain-of-thought and test-time reasoning efficiency, which can reduce inference steps and costs for production deployments in finance, healthcare, and customer support. According to Jeff Dean, the public claim centers on ARC-AGI-2, a reasoning-focused benchmark, indicating competitive pressure on frontier models and creating opportunities for tiered product packaging, premium API pricing, and upsell paths in Google Cloud’s AI stack.

According to Sundar Pichai on X, Google’s Gemini 3.1 Pro achieved 77.1% on the ARC-AGI-2 benchmark, more than doubling Gemini 3 Pro’s score, signaling a step forward in core reasoning for complex tasks such as visualizing intricate concepts, synthesizing multi-source data, and creative problem solving. As reported by Sundar Pichai, this stronger baseline positions Gemini 3.1 Pro for enterprise use cases like decision intelligence dashboards, multimodal analytics, and advanced RAG orchestration that demand consistent reasoning across long contexts. According to Sundar Pichai, the gains suggest immediate business impact in areas like financial modeling, scientific analysis, and product design workflows where structured synthesis and visual explanation quality can reduce time-to-insight and error rates.