Mistral AI API: models, pricing and limits
A practical guide to mistral, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
The Mistral AI lineup
Mistral AI has 35 models in the index. European model lab focused on open and commercial LLMs. The lineup spans 3 categories, from multimodal to small open-weight.
The strongest model by average score is mistral-large-3. The cheapest by input price is Mixtral 1.5B.
Pricing
Across the lineup, the average input price is $2.71 per million tokens and the average output price is $12.15. That average hides a wide range: individual models run from $0.36 to $5.41 on input.
Use the average as a starting point, then check the specific model you plan to deploy. Output price and cached input pricing can change the ranking for long or repetitive workloads.
Strengths and limits
35 of the 35 models are open weights. That gives you a self-hosting path for part of the lineup, while the proprietary models remain API-only.
The best benchmark result in the lineup is 81.7 on the index average. Compare that with the best model from other providers before treating Mistral AI as the default.
How to choose
- Start with the task, not the brand.
- Pick the cheapest model that clears your evaluation set.
- Keep a second provider as a fallback for outages and price changes.
- Check licence and data-handling terms for every model you shortlist.
Mistral AI models at a glance
| Model | Input / 1M | Context | Avg score |
|---|---|---|---|
| Mistral Large 3 | $5.41 | 128,000 | 81.7 |
| Mistral Large 3 Instruct | $5.31 | 400,000 | 81.1 |
| Magistral | $4.75 | 200,000 | 79.8 |
| Mistral Large 3 Chat | $4.90 | 1,000,000 | 79.1 |
| Magistral Instruct | $4.48 | 65,536 | 78.4 |
| Mistral Large 3 Base | $4.47 | 65,536 | 78.2 |
| Codestral | $4.19 | 65,536 | 77.6 |
| Magistral Chat | $4.33 | 128,000 | 76.8 |
| Codestral Chat | $3.89 | 32,768 | 75.8 |
| Magistral Base | $3.86 | 65,536 | 75.6 |
Related
FAQ
Which Mistral AI model should I start with?
Start with mistral-large-3 for quality-sensitive work and Mixtral 1.5B for high-volume tasks.
What is the most important factor in mistral?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.