mistralai/Mixtral-8x7B-Instruct-v0.1

mistralai/Mixtral-8x7B-Instruct-v0.1 is a budget-tier language model from Deep Infra with 47B parameters. mistralai/Mixtral-8x7B-Instruct-v0.1 is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through Deep Infra's API. Mixtral is Mistral's mixture-of-experts model family, activating only a subset of parameters per token — delivering near-70B quality at the inference cost of a ~13B dense model.

mistralai/Mixtral-8x7B-Instruct-v0.1 is priced at $0.24/1M input tokens and $0.24/1M output tokens. Output tokens are 1× 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, mistralai/Mixtral-8x7B-Instruct-v0.1 costs approximately $0.36 per 1,000 API calls. Deep Infra does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.24/1M input rate.

Its 33K-token context window is sufficient for most production use cases, handling documents up to ~25,000 words or multi-turn conversations spanning dozens of exchanges. Median latency (P50 time-to-first-token) is approximately 200ms — fast enough for real-time interactive applications. Throughput is approximately 350 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 4K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.

mistralai/Mixtral-8x7B-Instruct-v0.1 is best suited to cost, speed. 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 mistralai/Mixtral-8x7B-Instruct-v0.1 compares to similar models across these dimensions.

mistralai/Mixtral-8x7B-Instruct-v0.1 sits in the budget tier — the most cost-efficient models, best for high-volume simple tasks, classification, extraction, and applications where cost per call is the primary constraint. As an open-weights model, mistralai/Mixtral-8x7B-Instruct-v0.1 can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing mistralai/Mixtral-8x7B-Instruct-v0.1 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (52/100) 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.