Mistral Small 3.1 (non-reasoning)

While Mistral Small 3.1 has a large context window, every input token costs money. Design prompts to be as concise and effective as possible without sacrificing necessary context.

• Be Direct: Avoid verbose instructions; get straight to the point.
• Few-Shot Examples: Use minimal, high-quality examples instead of many redundant ones.
• Iterative Refinement: Break down complex tasks into smaller, sequential prompts if a single, massive prompt becomes too expensive or inefficient.

As shown in our provider analysis, the choice of API provider dramatically impacts both cost and performance. Match your provider to your primary objective.

• Cost-First: For budget-sensitive applications, prioritize providers like CompactifAI for their lower blended and output token prices.
• Performance-First: For applications demanding the lowest latency and highest speed, Google Vertex is often the superior choice.
• Balanced Approach: Mistral's own API offers a strong balance of performance and cost, making it a reliable default.

Mistral Small 3.1 is inherently concise, but you can further control output to manage costs, especially given its slightly higher output token price.

• Specify Max Tokens: Always set a reasonable max_tokens parameter in your API calls to prevent unnecessarily long generations.
• Prompt for Brevity: Include instructions like "be concise," "summarize briefly," or "provide only the answer" in your prompts.
• Post-Processing: If outputs are consistently too long, consider client-side truncation or further summarization if acceptable for your use case.

For non-real-time applications, batching multiple independent requests into a single API call can sometimes reduce overhead and improve efficiency, though this depends on the provider's API design.

• Consolidate Tasks: If you have multiple small, unrelated tasks, check if your chosen provider supports batch processing to send them together.
• Asynchronous Processing: For tasks that don't require immediate responses, queue them up and process them in larger batches during off-peak hours.

If your application frequently asks the same or very similar questions, implementing a caching layer can eliminate redundant API calls and save significant costs.

• Identify Common Queries: Analyze your application's usage patterns to find frequently asked questions or generated content.
• Implement a Cache: Store responses for these common queries and serve them directly from your cache instead of calling the API.
• Set Expiration: Ensure your cache has an appropriate expiration policy to keep content fresh while maximizing cost savings.

Mistral Small 3.1 is a general-purpose language model developed by Mistral AI. It is designed for a wide range of text generation and analysis tasks, offering a strong balance of intelligence, speed, and cost-efficiency. It also supports multimodal input, meaning it can process both text and images.

Mistral Small 3.1 scores 25 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (average of 20). This indicates robust comprehension and generation capabilities, making it highly effective for many complex tasks, though it's not specifically categorized as a 'reasoning' model.

Its primary strengths include exceptional output speed (119 tokens/s), above-average intelligence, highly concise outputs (reducing token usage), competitive input token pricing, a large 128k token context window, and multimodal input capabilities (text and image).

While input tokens are competitively priced, output tokens are slightly above average ($0.30/M). This means applications with very high output generation might see higher costs. However, its conciseness often mitigates this by reducing the total number of output tokens needed.

Yes, Mistral Small 3.1 supports multimodal input, allowing it to process both text and image data. This expands its utility for applications that require understanding or generating content based on visual information.

Mistral Small 3.1 features a substantial 128k token context window. This allows it to process and maintain context over very long documents or extended conversational histories, making it suitable for complex tasks requiring broad contextual understanding.

The best provider depends on your priority. Google Vertex offers the lowest latency and highest output speed. CompactifAI provides the best blended and lowest output token prices. Mistral's own API offers a strong, balanced performance across speed, latency, and cost. It's recommended to evaluate providers based on your specific application's needs.

Yes, its high output speed (119 tokens/s) and excellent time-to-first-token (as low as 0.16s with top providers) make Mistral Small 3.1 highly suitable for real-time applications such as chatbots, interactive assistants, and dynamic content generation where quick responses are critical.