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Gemini 3.1 Pro vs MiniMax M3

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 MiniMax M3.

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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MiniMax

MiniMax M3

MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...

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Technical Specifications

SpecificationGemini 3.1 ProMiniMax M3
ProviderGoogleMiniMax
Context Window2,000,000 tokens1,048,576 tokens
Agent Suitability93/100N/A
Time to First Token (TTFT)420 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablebeta
API AvailableYesYes
Released Date2026-04-202026-05-31

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.1 Pro

$2.00

MiniMax M3

$0.30

Output Price per Million Tokens

Gemini 3.1 Pro

$12.00

MiniMax M3

$1.20

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%vs8400.0%
Gemini 3.1 Pro
MiniMax M3
HumanEvalPython coding & logic synthesis
9460.0%vs8350.0%
Gemini 3.1 Pro
MiniMax M3
MATHComplex mathematical problem solving
8800.0%vs6200.0%
Gemini 3.1 Pro
MiniMax M3
GPQAGraduate-level expert reasoning
8130.0%vs4250.0%
Gemini 3.1 Pro
MiniMax M3
HellaSwagCommonsense reasoning and inference
9840.0%vs8520.0%
Gemini 3.1 Pro
MiniMax M3
MT-BenchMulti-turn conversation flow quality
950.0%vs888.0%
Gemini 3.1 Pro
MiniMax M3

Gemini 3.1 Pro Quirks & Gotchas

  • โ–ธBest model for massive context โ€” 2M token window is class-leading
  • โ–ธTool calling requires explicit schema definition in Google AI Studio

MiniMax M3 Quirks & Gotchas

No developer gotchas reported.