Mistral Medium (non-reasoning)

Since input tokens are relatively expensive, crafting concise and effective prompts is crucial. Avoid unnecessary preamble or verbose instructions.

• Be direct and specific in your requests.
• Use few-shot examples sparingly, only when necessary for quality.
• Pre-process input text to remove irrelevant information before sending it to the model.

The 33k context window is powerful, but filling it unnecessarily increases input costs. Only include information truly relevant to the task.

• Dynamically manage context to include only the most recent or pertinent conversation history.
• Summarize previous turns in a conversation to reduce token count for ongoing context.
• Employ retrieval-augmented generation (RAG) to fetch only relevant snippets, rather than passing entire documents.

For applications with many small, independent tasks, batching requests can reduce API call overhead and potentially optimize processing.

• Combine multiple summarization tasks or content generation prompts into a single API call.
• Ensure your application logic can handle the aggregated input and parse the combined output effectively.
• Be mindful of potential latency increases for individual items within a batch.

Understand how your specific use case's input-to-output token ratio compares to the 3:1 blended pricing assumption. Adjust strategies accordingly.

• If your tasks are heavily input-bound, focus more on input token reduction.
• If output-heavy, ensure the model's verbosity is controlled to avoid excessive output tokens.
• Regularly analyze your actual token usage patterns to identify areas for improvement.

While output tokens are moderately priced, generating overly verbose responses can still add up. Guide the model to be concise.

• Explicitly instruct the model on desired output length (e.g., "summarize in 3 sentences").
• Use parameters like `max_tokens` to cap response length, though this can sometimes truncate responses.
• Iterate on prompts to find the sweet spot between informative and concise output.

Mistral Medium is best suited for non-reasoning tasks such as summarization, content generation, data extraction, and simple Q&A. Its strengths lie in its speed, moderate cost, and large context window, making it ideal for high-throughput applications where complex analytical capabilities are not required.

It scores 8 on the Artificial Analysis Intelligence Index, placing it among the lower-tier models. This means it's not designed for tasks requiring deep reasoning, complex problem-solving, or intricate logical deductions, unlike more advanced reasoning-focused models.

No, Mistral Medium's lower intelligence score indicates it is not optimized for complex problem-solving, mathematical challenges, or intricate logical tasks. For such applications, you would typically need to consider models with higher intelligence benchmarks.

Mistral Medium features a substantial 33,000 token context window. This allows it to process and generate content based on relatively long documents or extensive conversational histories, making it versatile for tasks requiring broad contextual understanding.

Its input tokens are somewhat expensive at $2.75 per 1 million tokens, while output tokens are moderately priced at $8.10 per 1 million tokens. This results in a blended price of $4.09 per 1 million tokens (based on a 3:1 input-to-output ratio), offering a balanced cost for many common use cases.

Mistral Medium is a proprietary model offered exclusively via Mistral's API. Self-hosting is not an option for this specific model, meaning access and usage are managed through Mistral's cloud infrastructure.

Mistral Medium boasts a median output speed of 76 tokens per second. This performance is faster than the average for benchmarked models, making it an efficient choice for applications that require rapid text generation and high throughput.