DeepSeek V4 Pro 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 V4 Pro and MiniMax M2.7.
DeepSeek V4 Pro
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
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 V4 Pro | MiniMax M2.7 |
|---|---|---|
| Provider | DeepSeek | MiniMax |
| Context Window | 1,048,576 tokens | 204,800 tokens |
| Agent Suitability | 94/100 | N/A |
| Time to First Token (TTFT) | 280 ms | N/A |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2026-03-18 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
MiniMax M2.7
$0.18
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
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 V4 Pro Quirks & Gotchas
- ▸MoE architecture — cold-start latency on first request, use keep-alive
- ▸Best cost-performance ratio of any frontier model — strong tool calling for agentic use
MiniMax M2.7 Quirks & Gotchas
No developer gotchas reported.