DeepSeek R1 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 DeepSeek R1 and MiniMax M2.7.
DeepSeek R1
A premier reasoning model employing large-scale reinforcement learning. Displays specialized math, coding, and logical validation capabilities comparable to OpenAI's o1.
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...
Technical Specifications
| Specification | DeepSeek R1 | MiniMax M2.7 |
|---|---|---|
| Provider | DeepSeek | MiniMax |
| Context Window | 163,840 tokens | 204,800 tokens |
| Agent Suitability | 78/100 | N/A |
| Time to First Token (TTFT) | 1800 ms | N/A |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-01-20 | 2026-03-18 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek R1
$0.70
MiniMax M2.7
$0.18
Output Price per Million Tokens
DeepSeek R1
$2.50
MiniMax M2.7
$0.72
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
DeepSeek R1 Quirks & Gotchas
- ▸Reasoning model — not designed for high-frequency tool calling
- ▸Pair with a smaller model (V4 Flash) for routing and use R1 for complex reasoning only
MiniMax M2.7 Quirks & Gotchas
No developer gotchas reported.