Mistral
European frontier AI — Mistral Large, Codestral, and open models
Mistral AI is a Paris-based lab that trains both proprietary and open-weight models. Mistral Large competes with GPT-4 class models at lower prices, while Codestral is purpose-built for code generation with a 262K context window. Several Mistral models are open-weight and available for self-hosting.
The only frontier lab offering open-weight models alongside proprietary ones — giving teams the flexibility to self-host or use the API.
- Several open-weight models available for self-hosting
- Codestral purpose-built for code with 262K context
- European data sovereignty — GDPR-native
- Competitive pricing vs. GPT-4 class models
- Mistral Small is one of the cheapest capable models at $0.10/1M
- Intelligence scores trail OpenAI and Anthropic at frontier tier
- Smaller ecosystem than OpenAI
- No vision support on smaller models
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Full comparison tableMistral LLM pricing overview
Mistral is a proprietary AI lab that develops and hosts its own large language models. Mistral AI is a Paris-based lab that trains both proprietary and open-weight models. Mistral Large competes with GPT-4 class models at lower prices, while Codestral is purpose-built for code generation with a 262K context window. Several Mistral models are open-weight and available for self-hosting. Common use cases include Coding, European compliance, Open-source, Cost-efficiency. Headquartered in Paris, France, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Mistral vs other LLM providers
Mistral competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. The only frontier lab offering open-weight models alongside proprietary ones — giving teams the flexibility to self-host or use the API. Use the LLM provider comparison tool to see Mistral token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Mistral?
Mistral's key strengths are: Several open-weight models available for self-hosting; Codestral purpose-built for code with 262K context; European data sovereignty — GDPR-native. Limitations to consider: Intelligence scores trail OpenAI and Anthropic at frontier tier; Smaller ecosystem than OpenAI. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Mistral token pricing
Mistral charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. Prices shown are sourced from Mistral's public pricing page and updated daily. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Mistral context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Mistral makes sense
Managed inference APIs like Mistral eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Mistral API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Mistral token price data is available in the LLM price history charts.