Mistral
Mistral Large
model
mistral-largeBudgetStableVisionStreamingVisionToolsLong contextEurope
Context
262K
Input / 1M tokens
$0.5000
Output / 1M tokens
$1.50
Cached input / 1M tokens
$0.1175
Cache write / 1M tokens
— (bills at input price)
About this model
Mistral Large
Mistral Large — Mistral AI's flagship dense model. Strong general-purpose quality with particularly good multilingual performance for European languages.
Key facts
- Maker
- Mistral
- Context window
- 262K tokens
- Max output
- 262K tokens
- Lane
- Guarded: the provider has confirmed it does not train on your data
- Modality
- Text and image input
- Input price
- $0.5000 per 1M tokens
- Output price
- $1.50 per 1M tokens
- Processing region(s)
- eu
- Route
- Via Mistral's API
- Cheapest plan
- Available from the Pro plan
- Price comparison
- Priced below 144 of 234 pro-band guarded models on Anoman (blended price: 1 part input to 3 parts output).
Released
2024-07
Training cutoff
2024-07
Parameters
123B (dense)
Ways to call
Ways to call Mistral Large
Each model ID below is callable through the Anoman AI API. Price and processing region depend on the route.
| Model ID | Route | Input / output per 1M tokens | Region(s) | Lane | Context |
|---|---|---|---|---|---|
| mistral-large | Via Mistral's API | $0.5000 / $1.50 | eu | Guarded | 262K |
| mistral-large-latest | Via Mistral's API | $0.5000 / $1.50 | eu | Guarded | 262K |
Best for
Use cases
- European-language customer support
- GDPR-resident workloads (data processed in EU)
- Code generation
- Structured output + function calling
Strengths
What it does well
- EU data residency by default
- Native function calling + JSON mode
- Strong on French, German, Spanish, Italian benchmarks
- Code quality matches GPT-4-class on common tasks
Limitations
Know the trade-offs
- Trails frontier on the hardest reasoning tasks
- Knowledge cutoff trails newer models
- Premium price relative to open-weights at similar quality
Benchmarks
Published scores
Scores from official model cards and public leaderboards. Higher is better unless noted.
| Benchmark | Score | Measures |
|---|---|---|
| MMLU | 84.0 | General knowledge across 57 subjects |
| HumanEval | 92.0 | Python code generation, pass@1 |
| MATH | 60.4 | Mathematics, mixed difficulty |
Code example
Chat completion with Mistral Large
from openai import OpenAI
client = OpenAI(
base_url="https://api.anoman.io/v1",
api_key="anm-sk-..."
)
response = client.chat.completions.create(
model="mistral-large",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)Alternatives
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