DeepSeek V4 Pro vs Mistral Nemo
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 Mistral Nemo.
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,...
Mistral Nemo
A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...
Technical Specifications
| Specification | DeepSeek V4 Pro | Mistral Nemo |
|---|---|---|
| Provider | DeepSeek | Mistral |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 94/100 | N/A |
| Time to First Token (TTFT) | 280 ms | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2024-07-19 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
Mistral Nemo
$0.02
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
Mistral Nemo
$0.03
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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
Mistral Nemo Quirks & Gotchas
No developer gotchas reported.