DeepSeek V3 vs Mistral Large
Pricing, benchmarks, and use case comparison
Quick take
- •DeepSeek V3 is meaningfully stronger at cost efficiency (92 vs 66 on our capability index).
- •Mistral Large is meaningfully stronger at multimodal (65 vs 10).
- •DeepSeek V3 is open-weights (free to self-host); Mistral Large is paid API only.
Specs comparison
| DeepSeek V3 | Mistral Large | |
|---|---|---|
| Provider | DeepSeek | Mistral AI |
| Type | Open source | Closed source |
| Context window | 128K tokens | 128000 |
| Input / 1M tokens | ✓Free (self-host) | 2.00 |
| Output / 1M tokens | Free (self-host) | 6.00 |
| Release date | 2024-12 | 2024-02 |
Benchmarks
| Benchmark | DeepSeek V3 | Mistral Large |
|---|---|---|
| Pre-training scale | ~15T tokens | - |
| MMLU | - | 84.0% |
| HumanEval | - | 92.0% |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose DeepSeek V3 if...
- →You want a proven, stable open model with broad ecosystem support
- →You need to self-host or fine-tune without licensing friction
- →Cost is critical and you don't need V4's 1M context or top scores
- →You want reproducible open-weight behavior pinned to a known version
Choose Mistral Large if...
- →You need a strong European-built flagship with open weights
- →Your work is multilingual or requires nuanced reasoning
- →You want structured/JSON output and solid coding ability
- →You need the option to self-host for data sovereignty