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Gemini 3.1 Pro vs GPT-5.5

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 3.1 Pro and GPT-5.5.

Google

Gemini 3.1 Pro

Google's premiere multi-modal model featuring a massive 2 million token context window. Engineered for deep code analysis, video indexing, and long-context reasoning.

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OpenAI

GPT-5.5

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...

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Technical Specifications

SpecificationGemini 3.1 ProGPT-5.5
ProviderGoogleOpenAI
Context Window2,000,000 tokens1,050,000 tokens
Agent Suitability93/10095/100
Time to First Token (TTFT)420 ms380 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-202026-04-24

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.1 Pro

$2.00

GPT-5.5

$5.00

Output Price per Million Tokens

Gemini 3.1 Pro

$12.00

GPT-5.5

$30.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9280.0%vs9420.0%
Gemini 3.1 Pro
GPT-5.5
HumanEvalPython coding & logic synthesis
9460.0%vs9680.0%
Gemini 3.1 Pro
GPT-5.5
MATHComplex mathematical problem solving
8800.0%vs9350.0%
Gemini 3.1 Pro
GPT-5.5
GPQAGraduate-level expert reasoning
8130.0%vs8420.0%
Gemini 3.1 Pro
GPT-5.5
HellaSwagCommonsense reasoning and inference
9840.0%vs9900.0%
Gemini 3.1 Pro
GPT-5.5
MT-BenchMulti-turn conversation flow quality
950.0%vs970.0%
Gemini 3.1 Pro
GPT-5.5

Gemini 3.1 Pro Quirks & Gotchas

  • Best model for massive context — 2M token window is class-leading
  • Tool calling requires explicit schema definition in Google AI Studio

GPT-5.5 Quirks & Gotchas

  • Best for JSON schema adherence — strict mode available via response_format parameter
  • Requires explicit tool_choice for deterministic function calling