GPT-5.5 Pro vs Gemini 3.1 Pro PreviewBenchmark Comparison 2026

Objective comparison based on public benchmarks updated weekly: Intelligence Index, GPQA Diamond, Chatbot Arena ELO, pricing and speed.

Overall winner (2026)

Gemini 3.1 Pro Preview

4 of 7 criteria won

OpenAI

GPT-5.5 Pro

3 criteria won

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Google

Gemini 3.1 Pro Preview

Winner

Intelligence Index

57.2

Coding Index

55.5

4 criteria won

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Detailed Comparison

CritérioGPT-5.5 ProGemini 3.1 Pro Preview
Chatbot Arena ELO
Intelligence Index (AA)57.2
Coding Index (AA)55.5
GPQA Diamond94.0%
Input price ($/1M tok)$2.00
Output price ($/1M tok)$12.00
Context window1.1M tokens1.0M tokens
Speed (tokens/s)136 tok/s

✓ = winner in this criterion • Source: Artificial Analysis, LMArena, official APIs • Updated weekly

Technical Specifications

GPT-5.5 Pro

Company
OpenAI
Context window
1.1M tokens
Input ($/1M tok)
Output ($/1M tok)
0
Release
Apr 2026
Multimodal
Yes
Open Source
No
Official website
Visit

Gemini 3.1 Pro Preview

Company
Google
Context window
1.0M tokens
Input ($/1M tok)
$2.00
Output ($/1M tok)
$12.00
Speed
136 tok/s
Release
Feb 2026
Multimodal
Yes
Open Source
No
Official website
Visit

When to use GPT-5.5 Pro vs Gemini 3.1 Pro Preview?

Choosing between GPT-5.5 Pro and Gemini 3.1 Pro Preview depends on your use case, budget and technical requirements. Below, a practical guide based on benchmark data and each model's specifications.

Use GPT-5.5 Pro when:

OpenAI · Multimodal

  • High-volume token projects — at $0/1M input tokens, the cost per call is low enough for production use at scale
  • Processing images, PDFs and visual documents alongside text — useful for analyzing contracts, reports with charts and mixed content
  • Long document analysis — 1.1M tokens context window allows processing books, legal databases and extensive logs
  • API integration in SaaS applications — direct API access with documented SLA
View full GPT-5.5 Pro profile

Use Gemini 3.1 Pro Preview when:

Google · Multimodal

  • Complex reasoning, math and advanced programming — reasoning models are optimized for problems requiring multiple logical steps
  • Processing images, PDFs and visual documents alongside text — useful for analyzing contracts, reports with charts and mixed content
  • Applications with audio input or output — transcription, call analysis and voice assistants
  • Long document analysis — 1.0M tokens context window allows processing books, legal databases and extensive logs
  • AI agents with tool calling — workflow automation, external API integration and data pipelines
  • API integration in SaaS applications — direct API access with documented SLA
View full Gemini 3.1 Pro Preview profile
SWEN Verdict: Gemini 3.1 Pro Preview wins in more objective criteria in this comparison (4 vs 3). For most use cases, Gemini 3.1 Pro Preview offers better aggregate performance — but GPT-5.5 Pro may be preferable if your project prioritizes high-volume token projects.

Frequently Asked Questions

GPT-5.5 Pro or Gemini 3.1 Pro Preview: which is better?

Gemini 3.1 Pro Preview wins in 4 of 7 criteria analyzed. Check the full table to choose based on your use case.

Where does this benchmark data come from?

Data is aggregated from Artificial Analysis (Intelligence Index, Coding Index) and Chatbot Arena/LMArena (ELO). Pricing and specs come from official APIs. Updated weekly.

What is the Intelligence Index?

The Intelligence Index is an aggregate score from Artificial Analysis that combines multiple academic benchmarks (MMLU, GPQA, LiveBench, etc.) into a single rating. The higher the score, the more capable the model is at reasoning tasks.

Is GPT-5.5 Pro cheaper than Gemini 3.1 Pro Preview?

Yes. GPT-5.5 Pro costs $0/1M input tokens, while Gemini 3.1 Pro Preview costs $2/1M tokens — Infinity% more expensive. For high-volume projects, GPT-5.5 Pro represents significant savings. Total cost also depends on output pricing and your application's usage pattern.

GPT-5.5 Pro or Gemini 3.1 Pro Preview: which has a larger context window?

GPT-5.5 Pro has the larger context window: 1.1M tokens vs 1.0M tokens. For long document analysis, extensive transcripts or full codebases, the larger context window is a decisive criterion.

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