Gemini 3.1 Pro: A Hands-On Test of Google’s Newest AI

2026-02-19 · Source: Analytics Vidhya · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Google DeepMind has released Gemini 3.1 Pro, an iteration of its large language model family, just three months after Gemini 3 Pro. This new model features an industry-leading 1 million token context window, capable of processing over 1,500 pages of text or entire code repositories. It demonstrates more than double the reasoning performance of its predecessor, achieving 77.1% on the ARC-AGI-2 benchmark, up from 31.1%. Gemini 3.1 Pro also includes enhanced agentic reliability with a dedicated API endpoint for tool orchestration, improved visual coding capabilities for generating animated SVGs, and a significant reduction in hallucination rates from 88% to 50% on the AA-Omniscience benchmark. The model maintains the same cost per token as Gemini 3 Pro and offers granular thinking parameters (Low, Medium, High).

Key takeaway

For AI/ML Directors evaluating frontier models for complex enterprise applications, Gemini 3.1 Pro offers substantial improvements in reasoning, long-context processing, and agentic reliability. Your teams should consider integrating its dedicated API endpoint for autonomous workflows and leveraging its 1 million token context window for advanced analytical tasks, especially given its reduced hallucination rate and consistent pricing.

Key insights

Gemini 3.1 Pro significantly advances reasoning, context handling, and agentic capabilities while reducing hallucinations.

Principles

• Large context windows enhance complex task processing.
• Agentic optimization improves autonomous workflow reliability.
• Granular thinking parameters allow latency/output control.

Method

The model was evaluated through multi-step logical reasoning, code generation and refactoring, and long-context analytical synthesis tasks to assess its practical capabilities beyond standard benchmarks.

In practice

• Utilize the 1M context window for large document analysis.
• Employ the agentic API for high-precision tool orchestration.
• Adjust thinking parameters for latency-sensitive applications.

Topics

• Gemini 3.1 Pro
• Large Language Models
• Context Window
• AI Benchmarks
• Code Generation

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Machine Learning Engineer, AI Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Analytics Vidhya.