Mistral Small 3 (non-reasoning)

The most impactful decision for cost optimization is choosing the right API provider. As seen in the benchmarks, prices vary wildly.

• Prioritize Deepinfra for Cost: If budget is your absolute top priority, Deepinfra offers the lowest blended price and input token price.
• Balance with Mistral: For a good balance of speed and cost, Mistral's own API provides competitive rates and excellent output speed.
• Avoid High-Cost Providers for Bulk: For high-volume tasks, steer clear of providers with significantly higher blended prices, even if they offer marginal latency improvements.

The way you construct your prompts directly impacts input token count and, consequently, cost.

• Be Concise: Remove unnecessary words, examples, or instructions from your prompts. Every token counts.
• Leverage Context Window: While 32k is generous, only include necessary context. Summarize or extract relevant sections from longer documents before passing them to the model.
• Few-Shot Learning: Use minimal, high-quality examples instead of many redundant ones to guide the model.

Managing the length and content of the model's output is critical, especially given that output tokens can be more expensive.

• Specify Max Tokens: Always set a max_tokens parameter to prevent the model from generating excessively long responses.
• Clear Output Instructions: Guide the model to produce only the essential information. For example, instruct it to 'Summarize in 3 sentences' or 'Extract only the name and email'.
• Post-Processing: If the model occasionally generates extra content, consider client-side truncation or filtering to ensure you're only using (and paying for) what's needed.

For certain workloads, batching requests and caching common responses can significantly reduce API calls and costs.

• Batch Similar Requests: If you have multiple independent prompts that can be processed simultaneously, check if your chosen provider supports batching to optimize throughput.
• Cache Common Responses: For frequently asked questions or repetitive content generation, store and reuse previous model outputs instead of re-querying the API.
• Pre-computation: For static or slowly changing content, pre-compute responses and store them in a database or CDN.

Mistral Small 3 is a highly efficient, general-purpose language model developed by Mistral AI. It is known for its exceptional speed and above-average intelligence, making it suitable for a wide range of text-based tasks.

Mistral Small 3 scores 21 on the Artificial Analysis Intelligence Index, placing it above average among comparable models. This indicates strong performance across various language understanding and generation tasks.

Its primary strengths include outstanding output speed (222.7 tokens/s), above-average intelligence, a generous 32k token context window, and the availability of highly cost-effective provider options like Deepinfra.

While intelligent, it is classified as a non-reasoning model, meaning it may not excel at complex logical deduction or multi-step problem-solving. Also, its average output token price can be high if not optimized through provider selection.

For raw output speed, Mistral's own API is the fastest at 190 tokens/second. For the lowest time to first token (latency), Together.ai leads with 0.23 seconds.

Deepinfra offers the most cost-effective solution for Mistral Small 3, with a blended price of $0.06 per 1M tokens, and the lowest input token price at $0.05 per 1M tokens.

Mistral Small 3 features a 32k token context window, allowing it to process and generate substantial amounts of text in a single interaction.

While it has above-average intelligence, Mistral Small 3 is generally considered a non-reasoning model. For highly complex logical reasoning or multi-step problem-solving, more specialized or larger reasoning models might be more appropriate.