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Gemini 3.1 Pro vs Ministral 3 14B 2512

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 Ministral 3 14B 2512.

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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Mistral

Ministral 3 14B 2512

The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language...

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Technical Specifications

SpecificationGemini 3.1 ProMinistral 3 14B 2512
ProviderGoogleMistral
Context Window2,000,000 tokens262,144 tokens
Agent Suitability93/100N/A
Time to First Token (TTFT)420 msN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-202025-12-02

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.1 Pro

$2.00

Ministral 3 14B 2512

$0.20

Output Price per Million Tokens

Gemini 3.1 Pro

$12.00

Ministral 3 14B 2512

$0.20

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%vsN/A
Gemini 3.1 Pro
Ministral 3 14B 2512
HumanEvalPython coding & logic synthesis
9460.0%vsN/A
Gemini 3.1 Pro
Ministral 3 14B 2512
MATHComplex mathematical problem solving
8800.0%vsN/A
Gemini 3.1 Pro
Ministral 3 14B 2512
GPQAGraduate-level expert reasoning
8130.0%vsN/A
Gemini 3.1 Pro
Ministral 3 14B 2512
HellaSwagCommonsense reasoning and inference
9840.0%vsN/A
Gemini 3.1 Pro
Ministral 3 14B 2512
MT-BenchMulti-turn conversation flow quality
950.0%vsN/A
Gemini 3.1 Pro
Ministral 3 14B 2512

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

Ministral 3 14B 2512 Quirks & Gotchas

No developer gotchas reported.