MiniMax M2.7 Thinking
MiniMax M2.7 Thinking is MiniMax's reasoning-focused model variant that extends the M2.7 base with explicit chain-of-thought capabilities. MiniMax is a Shanghai-based AI lab known for building capable models optimized for Chinese and multilingual tasks, and the Thinking variant specifically targets scenarios where deliberate, stepwise reasoning improves output accuracy over direct generation.
The model is suited for logic-heavy tasks such as math, coding, and structured analysis, where the extended thinking process allows it to catch errors and explore solution paths before committing to a final answer. It represents MiniMax's positioning in the emerging class of inference-time scaling models that trade latency for improved reliability on difficult problems.
Key Features
Extended chain-of-thought reasoning for multi-step problem solving
Strong performance on mathematical and logical inference tasks
Effective for structured analysis requiring intermediate reasoning steps
Bilingual competence in Chinese and English
Inference-time compute scaling improves accuracy on hard problems
Suitable for code debugging and technical problem decomposition
Ideal Use Cases
Step-by-step math tutoring and homework assistance
Logic puzzle and constraint-satisfaction problem solving
Code review and systematic debugging workflows
Technical specification analysis and requirement decomposition
Scientific reasoning tasks requiring traceable inference chains
Example Prompts for MiniMax M2.7 Thinking
Technical Specifications
| Provider | MiniMax |
| Category | Reasoning |
| Modality | Text -> Text (reasoning) |
Frequently Asked Questions
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