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Gemini 3.1 Pro Preview

by Google DeepMind · Released Feb 2026

Multimodal1M ContextPreview
65.0
avg score
Rank #57
Compare
Better than 82% of all models
Context
1.0M tokens (~524 books)
Input $/1M
$2.00
Output $/1M
$12.00
Type
multimodal
License
Proprietary
Benchmarks
51 tested
Data as of
About

Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation...

Tested on 51 benchmarks · BenchGecko score 65.0. Top scores: Chatbot Arena Elo — Overall (1487.0%), Chatbot Arena Elo — Coding (1446.2%), ARC-AGI (98.0%).

Looking for similar performance at lower cost?
GPT-5.6 Luna scores 64.8 (100% as good) at $0.20/1M input · 90% cheaper
Capabilities
coding
45.2
#115 globally
reasoning
83.5
#12 globally
math
47.1
#125 globally
knowledge
52.2
#109 globally
agentic
35.3
#34 globally
general
36.7
#86 globally
speed
57.1
#15 globally
Benchmark Scores
Compare All
Tested on 51 benchmarks · Ranked across 8 categories
Score Distribution (all 312 models)
0255075100
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Terminal Bench

Complex terminal-based engineering tasks. Models must use command-line tools, navigate filesystems, and debug systems through shell interaction.

80.2·
SWE-Bench verified

Real-world software engineering tasks from GitHub issues. Models must diagnose bugs and write patches that pass test suites. Human-verified subset of SWE-bench.

75.6·
WeirdML

Unusual and adversarial machine learning challenges. Tests robustness of reasoning about edge cases in ML systems.

72.1·
ARC-AGI

Abstraction and Reasoning Corpus. Tests fluid intelligence through novel visual pattern recognition puzzles. Core measure of general intelligence.

98.0·
ARC-AGI-2

ARC-AGI 2, harder sequel to ARC. More complex abstract reasoning patterns that test generalization ability beyond training data.

77.1·
SimpleBench

Deceptively simple questions that humans find easy but AI models often get wrong. Tests common sense and reasoning gaps.

75.5·
OTIS Mock AIME 2024-2025

Mock AIME (American Invitational Mathematics Exam) problems from OTIS. Tests mathematical competition performance.

95.6·
FrontierMath-2025-02-28-Private

Original research-level math problems created by professional mathematicians. Problems are unpublished and cannot be memorized.

36.9·
Excellent (85+) Good (70-85) Average (50-70) Below (<50)
Links
Documentation
BenchGecko API
gemini-3-1-pro-preview
Specifications
  • Typemultimodal
  • Context1.0M tokens (~524 books)
  • ReleasedFeb 2026
  • LicenseProprietary
  • Statuspreview
  • Cost / Message~$0.016
Available On
Google DeepMind logoGoogle DeepMind$2.00
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Gemini 3.1 Pro Preview is a proprietary multimodal AI model by Google DeepMind, released in February 2026. It has an average benchmark score of 65.0. Context window: 1M tokens.

Key facts · as of 2026-10-05

  • Gemini 3.1 Pro Preview by Google DeepMind. BenchGecko score 65.0, rank 57 of 312 scored models (normalized average of public benchmark scores).
  • List price $2.00 input · $12.00 output per 1M tokens (as of 2026-10-05).
  • Sold by 6 providers (as of 2026-10-05): Google $1.00 in / $6.00 out · Google AI Studio $1.00 in / $6.00 out · Google $2.00 in / $12.00 out · Google AI Studio $2.00 in / $12.00 out · Google $3.60 in / $21.60 out · and 1 more. Every provider

How to cite · data as of 2026-10-05

Gemini 3.1 Pro Preview · benchmarks, pricing and providers. BenchGecko, data as of 2026-10-05. https://benchgecko.ai/model/gemini-3-1-pro-preview

Credit "Source: BenchGecko" with a link. Prices per provider and Gecko Tests are BenchGecko data (CC BY 4.0); benchmark scores keep their original source, listed in the JSON. JSON · llms.txt · MCP