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Gemini 3.1 Flash vs MiniMax M2.7

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 Flash and MiniMax M2.7.

Google

Gemini 3.1 Flash

Gemini 3.1 Flash is Google's high-speed, cost-efficient multimodal model in the 3.1 generation, purpose-built for high-volume content synthesis, classification, and intelligent routing at scale. Featuring a 1-million-token context window, it can process large batches of documents, customer data, or multimedia content in a single inference pass, dramatically reducing pipeline complexity. At just $0.25/MTok for input, it is one of the most affordable routes to Google-caliber multimodal AI, making it an ideal backbone for production pipelines, data enrichment workflows, and high-frequency API integrations.

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MiniMax

MiniMax M2.7

MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...

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Technical Specifications

SpecificationGemini 3.1 FlashMiniMax M2.7
ProviderGoogleMiniMax
Context Window1,000,000 tokens204,800 tokens
Agent Suitability86/100N/A
Time to First Token (TTFT)150 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-202026-03-18

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.1 Flash

$0.25

MiniMax M2.7

$0.18

Output Price per Million Tokens

Gemini 3.1 Flash

$1.50

MiniMax M2.7

$0.72

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
8680.0%vs8250.0%
Gemini 3.1 Flash
MiniMax M2.7
HumanEvalPython coding & logic synthesis
8850.0%vs8000.0%
Gemini 3.1 Flash
MiniMax M2.7
MATHComplex mathematical problem solving
7820.0%vs5400.0%
Gemini 3.1 Flash
MiniMax M2.7
GPQAGraduate-level expert reasoning
6050.0%vs3900.0%
Gemini 3.1 Flash
MiniMax M2.7
HellaSwagCommonsense reasoning and inference
9520.0%vs8400.0%
Gemini 3.1 Flash
MiniMax M2.7
MT-BenchMulti-turn conversation flow quality
900.0%vs870.0%
Gemini 3.1 Flash
MiniMax M2.7

Gemini 3.1 Flash Quirks & Gotchas

  • โ–ธMost cost-effective Google model โ€” ideal for high-volume pipelines
  • โ–ธContext caching available via Vertex AI for repeated document processing

MiniMax M2.7 Quirks & Gotchas

No developer gotchas reported.