DeepSeek R1 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 R1 and Mistral Nemo.
DeepSeek R1
A premier reasoning model employing large-scale reinforcement learning. Displays specialized math, coding, and logical validation capabilities comparable to OpenAI's o1.
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 R1 | Mistral Nemo |
|---|---|---|
| Provider | DeepSeek | Mistral |
| Context Window | 163,840 tokens | 131,072 tokens |
| Agent Suitability | 78/100 | N/A |
| Time to First Token (TTFT) | 1800 ms | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-01-20 | 2024-07-19 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek R1
$0.70
Mistral Nemo
$0.02
Output Price per Million Tokens
DeepSeek R1
$2.50
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 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
Mistral Nemo Quirks & Gotchas
No developer gotchas reported.