Mistral Small (Feb) (non-reasoning)

Since input tokens contribute to cost, crafting concise yet clear prompts is crucial. Avoid unnecessary preamble or overly verbose instructions. Get straight to the point, providing just enough context for the model to understand the task without wasting tokens.

• **Be Direct:** Use imperative verbs and clear instructions.
• **Provide Examples:** For specific formats, a few short examples are more effective than long descriptions.
• **Iterate & Refine:** Test different prompt variations to find the shortest one that yields acceptable results.

Mistral Small's exceptional speed and low cost make it ideal for tasks that need to be performed at scale. Think about scenarios where you need to process thousands or millions of simple text operations.

• **Batch Processing:** Group multiple requests into single API calls if your application allows, reducing overhead.
• **Automated Workflows:** Integrate it into automated pipelines for tasks like content moderation, tag generation, or data normalization.
• **Initial Filtering:** Use it as a first pass for large datasets, then route more complex cases to larger, more expensive models.

For tasks requiring some level of reasoning or creativity, consider a multi-model approach. Use Mistral Small for the initial, low-complexity steps, and then pass the output to a more capable (and expensive) model for refinement or complex analysis.

• **Drafting & Editing:** Generate a first draft with Mistral Small, then use a larger model to edit, enhance, or fact-check.
• **Summarization & Analysis:** Summarize a long document with Mistral Small, then feed the summary to a reasoning model for deeper insights.
• **Categorization & Refinement:** Use Small for initial broad categorization, then a more intelligent model for fine-grained classification.

Implement robust logging and monitoring for both input and output token counts. This allows you to identify unexpected token consumption patterns and optimize your application's interaction with the model.

• **Set Usage Alerts:** Configure alerts for exceeding certain token thresholds.
• **Analyze Output Lengths:** Ensure the model isn't generating excessively long outputs when shorter ones would suffice.
• **Cost Attribution:** Track token usage per feature or user to understand where costs are accumulating.

Explicitly specify `max_tokens` in your API requests to prevent the model from generating unnecessarily long responses, which directly impacts your output token costs. Tailor this parameter to the minimum required length for your specific use case.

• **Define Clear Limits:** Always set a reasonable `max_tokens` value.
• **Test Output Lengths:** Experiment to find the optimal `max_tokens` that balances completeness and cost.
• **Post-Processing:** If the model occasionally exceeds desired length, implement client-side truncation as a fallback.

Mistral Small (Feb '24) is a proprietary, non-reasoning language model developed by Mistral. It is designed for high-speed, cost-effective text generation and processing tasks, featuring a 33,000-token context window.

Mistral Small (Feb '24) scores 8 on the Artificial Analysis Intelligence Index, placing it among the lower-tier models in terms of reasoning capabilities. It is explicitly a 'non-reasoning' model, meaning it is not suited for tasks requiring complex logic, problem-solving, or deep understanding.

Its main strengths are exceptional output speed (136 tokens/s) and highly competitive pricing ($1.00/1M input tokens, $3.00/1M output tokens). It also offers a generous 33k context window for its price point, making it ideal for high-volume, cost-sensitive applications.

It excels at tasks like generating simple email drafts, product descriptions, content rephrasing, summarization of non-complex texts, and initial content drafts. Essentially, any task that requires fast, affordable text output without demanding advanced cognitive functions.

Mistral Small (Feb '24) features a 33,000-token context window. This allows it to process and generate text based on a substantial amount of input, supporting longer conversations or document-based tasks.

No, it is not. Its low intelligence score indicates it will struggle with complex reasoning, logical inference, and highly creative or nuanced writing tasks. For such applications, more capable (and typically more expensive) models would be a better choice.

Mistral Small (Feb '24) offers highly competitive pricing. Its input token price of $1.00 per 1M tokens is significantly below the average of $2.00, and its output token price of $3.00 per 1M tokens is remarkably lower than the average of $10.00 for comparable models, making it one of the most cost-effective options available.