MiniMax-M2
MiniMax-M2 is an AI model published by MiniMaxAI, released on 2025-10-27, for Chat model, with 230B parameters, and 205K context length, requiring about 239.99 GB storage, with a 87.00 score on τ²-Bench - Telecom.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Model basics
Open source & experience
Official resources
API details
| Type | Condition | Input | Output |
|---|---|---|---|
| Text | - | ¥2.10/ 1M | ¥8.40/ 1M |
| Type | TTL | Write | Read |
|---|---|---|---|
| Text | - | ¥2.63/ 1M | ¥0.210/ 1M |
| Text | - | ¥2.63/ 1M Cache = write | - |
| Text | - | - | ¥0.210/ 1M Cache = hit |
Benchmark Results
MiniMax M2 currently shows benchmark results led by LiveCodeBench (25 / 126, score 83), IF Bench (12 / 33, score 72.30), MMLU Pro (54 / 133, score 82). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.
Coding and Software Engineer
2 evaluationsAgent Level Benchmark
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Publisher
Model Overview
MiniMax-M2 is an AI model published by MiniMaxAI, released on 2025-10-27, for Chat model, with 230B parameters, and 205K context length, requiring about 239.99 GB storage, with a 87.00 score on τ²-Bench - Telecom.
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