MiniMax M2.7 vs R1
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 MiniMax M2.7 and R1.
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...
R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....
Technical Specifications
| Specification | MiniMax M2.7 | R1 |
|---|---|---|
| Provider | MiniMax | DeepSeek |
| Context Window | 204,800 tokens | 163,840 tokens |
| Agent Suitability | N/A | N/A |
| Time to First Token (TTFT) | N/A | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-03-18 | 2025-01-20 |
API Pricing Comparison
Input Price per Million Tokens
MiniMax M2.7
$0.18
R1
$0.70
Output Price per Million Tokens
MiniMax M2.7
$0.72
R1
$2.50
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.
MiniMax M2.7 Quirks & Gotchas
No developer gotchas reported.
R1 Quirks & Gotchas
No developer gotchas reported.