MiniMax-M2.7
MiniMax-M2.7 is an AI model published by MiniMaxAI, released on 2026-03-18, for Reasoning model, with 2290.0B parameters, and 200K tokens context length, requiring about 未知 storage, under the MiniMax-Modified MIT license.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
| Type | Condition | Input | Output |
|---|---|---|---|
| Text | - | $0.300/ 1M | $1.20/ 1M |
| Type | TTL | Write | Read |
|---|---|---|---|
| Text | 5m | $0.375/ 1M | $0.060/ 1M |
MiniMax-M2.7 currently shows benchmark results led by IF Bench (4 / 27, score 76), Claw Bench (5 / 29, score 91.70), GPQA Diamond (35 / 175, score 87). This page also consolidates core specs, context limits, and API pricing so you can evaluate the model from benchmark results and deployment constraints together.
MiniMax-M2.7 is an AI model published by MiniMaxAI, released on 2026-03-18, for Reasoning model, with 2290.0B parameters, and 200K tokens context length, requiring about 未知 storage, under the MiniMax-Modified MIT license.
Follow DataLearner on WeChat for AI model updates and research notes.
