Mistral Large 2 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 Mistral Large 2 and Mistral Nemo.
Mistral Large 2
Mistral Large 2 is Mistral AI's second-generation flagship commercial model, delivering substantial improvements over the original Mistral Large in reasoning, tool use, multilingual capabilities, and instruction following. With a 1-million-token context window, it processes entire codebases and lengthy documents in a single pass. Mistral Large 2 excels at complex agentic workflows, structured JSON output, and native function calling — making it one of the top choices for European enterprises requiring GDPR-compliant AI infrastructure.
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 | Mistral Large 2 | Mistral Nemo |
|---|---|---|
| Provider | Mistral | Mistral |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 93/100 | N/A |
| Time to First Token (TTFT) | 270 ms | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-07-24 | 2024-07-19 |
API Pricing Comparison
Input Price per Million Tokens
Mistral Large 2
$0.60
Mistral Nemo
$0.02
Output Price per Million Tokens
Mistral Large 2
$1.80
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.
Mistral Large 2 Quirks & Gotchas
- ▸Excellent European data sovereignty — GDPR-compliant infrastructure
- ▸1M context window enables full codebase analysis in a single pass
Mistral Nemo Quirks & Gotchas
No developer gotchas reported.