Mistral Small 24B

Mistral Small 24B is a budget-tier language model from IONOS AI Model Hub. Mistral Small 24B 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 IONOS AI Model Hub'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 Small 24B is priced at $0.1/1M input tokens and $0.3/1M 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 Small 24B costs approximately $0.25 per 1,000 API calls. IONOS AI Model Hub does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.1/1M 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.

Mistral Small 24B is best suited to cost, chat, open-weights. 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 Small 24B compares to similar models across these dimensions.

Mistral Small 24B 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, Mistral Small 24B 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 Mistral Small 24B to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (N/A/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.