DeepSeek V4 Pro vs MiniMax M1
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 M1.
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 M1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
Technical Specifications
| Specification | DeepSeek V4 Pro | MiniMax M1 |
|---|---|---|
| Provider | DeepSeek | MiniMax |
| Context Window | 1,048,576 tokens | 1,000,000 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 | beta |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2025-06-17 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
MiniMax M1
$0.40
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
MiniMax M1
$2.20
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.
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 M1 Quirks & Gotchas
No developer gotchas reported.