mistral-large

mistral-large is a advanced-tier language model from Mistral. mistral-large is a proprietary model available exclusively through Mistral's API. Mistral's model family is known for strong performance per parameter, with the 7B model punching well above its weight class and the Mixtral MoE variants delivering near-frontier quality at efficient inference cost.

mistral-large is priced at $2/1M input tokens and $6/1M output tokens. Output tokens are 3× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, mistral-large costs approximately $5.00 per 1,000 API calls. Mistral does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $2/1M input rate.

Its 128K-token context window handles most long-document tasks — roughly 75,000 words or a full novel — without requiring chunking or retrieval augmentation. Median latency (P50 time-to-first-token) is approximately 600ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 100 tokens/second — adequate for streaming at comfortable reading speed. Maximum output length is 16K tokens — sufficient for long-form content generation, detailed code files, and extended reports.

mistral-large is best suited to chat, coding. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how mistral-large compares to similar models across these dimensions.

mistral-large sits in the advanced tier — strong general-purpose models balancing capability and cost, suitable for most production applications. Unlike open-weights alternatives, mistral-large is only available via Mistral's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing mistral-large to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (76/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.