Mistral Small (Sep) offers high speed and a decent context window, but its intelligence score and pricing position it as a premium option for straightforward, non-reasoning tasks.
The Mistral Small (Sep) model, released by Mistral, presents itself as a compact and swift solution for various language processing tasks. Benchmarked across key performance indicators, this model demonstrates a notable emphasis on speed, achieving a median output speed of 115 tokens per second, with a peak performance observed at 131 tokens per second. This places it significantly above the average for comparable models, making it an attractive choice for applications where rapid text generation is paramount.
However, its positioning in the market is nuanced. While excelling in speed, Mistral Small (Sep) registers an intelligence score of 13 on the Artificial Analysis Intelligence Index, which is below the average of 20 for its class. This suggests that while it can process and generate text quickly, its capabilities for complex reasoning or nuanced understanding are limited. It is explicitly categorized as a 'non-reasoning' model, indicating its suitability for tasks that do not require deep analytical thought or intricate problem-solving.
A critical consideration for Mistral Small (Sep) is its pricing structure. With an input token price of $0.20 per 1M tokens and an output token price of $0.60 per 1M tokens, it is notably more expensive than the average for its category ($0.10 for input, $0.20 for output). This premium pricing, coupled with its below-average intelligence, means that users must carefully weigh the benefits of its speed against the higher operational costs, especially for large-scale deployments or tasks that could potentially be handled by more cost-effective, albeit slower, alternatives.
The model offers a substantial 33k token context window, which is a significant advantage for handling longer inputs and maintaining conversational coherence over extended interactions. This generous context allows for more complex prompts and detailed responses without losing track of earlier information. Despite its 'open' license, the primary access is through the Mistral API, which simplifies integration but also ties users directly to Mistral's pricing and infrastructure.
In summary, Mistral Small (Sep) is a specialized tool. It shines in scenarios demanding high-speed text generation and a large context window, particularly for non-reasoning tasks. Its performance profile makes it a strong contender for applications like content summarization, rapid response generation, or data extraction where the speed-to-cost ratio aligns with specific project requirements, provided the intelligence limitations are understood and accounted for.
13 (39 / 55 / 2 out of 4 units)
131 tokens/s
$0.20 per 1M tokens
$0.60 per 1M tokens
N/A tokens
0.33 seconds
| Spec | Details |
|---|---|
| Owner | Mistral |
| License | Open |
| Context Window | 33k tokens |
| Model Type | Non-Reasoning |
| Intelligence Index Score | 13 |
| Output Speed (Median) | 115 tokens/s |
| Output Speed (Peak) | 131 tokens/s |
| Latency (TTFT) | 0.33 seconds |
| Input Token Price | $0.20 / 1M tokens |
| Output Token Price | $0.60 / 1M tokens |
| Blended Price (3:1) | $0.30 / 1M tokens |
| API Provider | Mistral |
Given Mistral Small (Sep)'s unique profile of high speed, decent context, but premium pricing and limited reasoning, the choice of provider is straightforward as it's exclusively offered by Mistral. However, understanding how to best leverage this model within the Mistral ecosystem is key.
The primary consideration is aligning its strengths with your application's needs, particularly for tasks that prioritize speed over deep intelligence and where the cost can be justified by the performance gains.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Primary | Mistral API | Direct access to the model, optimized performance, and official support. | Higher cost per token compared to market averages. |
| Alternative (for reasoning) | N/A (Consider other models) | Mistral Small (Sep) is not designed for complex reasoning. | Requires integrating a different model for reasoning tasks, increasing complexity. |
| Alternative (for cost) | N/A (Consider other models) | For budget-sensitive projects, other models might offer better price-to-performance for non-reasoning tasks. | May sacrifice speed or context window size. |
| Integration | Mistral API SDKs | Seamless integration with existing Mistral tools and libraries. | Vendor lock-in to the Mistral ecosystem. |
Note: Mistral Small (Sep) is exclusively available via the Mistral API. Provider choices are thus focused on how to best utilize this specific offering.
Understanding the real-world cost implications of Mistral Small (Sep) requires examining typical usage scenarios. Its high token prices mean that even with its speed, costs can escalate quickly for verbose applications. Below are estimated costs for common workloads, assuming a 3:1 input-to-output token ratio for blended price calculations where applicable, or separate input/output pricing.
These estimates highlight where the model's premium pricing becomes most apparent and where its speed might justify the investment.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Short Email Generation | 200 tokens | 500 tokens | Drafting a concise email response. | $0.00034 |
| Article Summarization | 5,000 tokens | 1,000 tokens | Condensing a news article into key points. | $0.00160 |
| Customer Support Chatbot (10 turns) | 1,500 tokens | 1,500 tokens | A brief, interactive customer service exchange. | $0.00240 |
| Long-Form Content Creation | 1,000 tokens | 4,000 tokens | Generating a detailed blog post or report section. | $0.00260 |
| Data Extraction (Large Document) | 10,000 tokens | 500 tokens | Extracting specific entities from a legal document. | $0.00230 |
| Code Generation (Small Function) | 300 tokens | 200 tokens | Generating a simple utility function. | $0.00018 |
The estimated costs reveal that while individual requests are inexpensive, the premium pricing of Mistral Small (Sep) means that high-volume or highly verbose applications will incur significant costs rapidly. Its speed must genuinely translate into business value to justify these expenses, especially for tasks that could be handled by more budget-friendly alternatives.
Optimizing costs when using Mistral Small (Sep) is crucial due to its higher-than-average token prices. The focus should be on maximizing the value derived from each token while leveraging its speed where it matters most.
Here are strategies to keep your expenses in check without sacrificing performance where Mistral Small (Sep) truly shines:
Crafting concise and effective prompts can significantly reduce input token usage, directly impacting costs.
Since output tokens are more expensive, controlling the length of the model's responses is vital.
max_tokens parameter to prevent unnecessarily long outputs.Leverage Mistral Small (Sep) for tasks where its speed provides a clear advantage, and offload others.
Regularly review your API usage and costs to identify areas for optimization.
Mistral Small (Sep) is best suited for high-speed text generation tasks that do not require complex reasoning. This includes applications like rapid content summarization, quick response generation in chatbots, data extraction from structured or semi-structured text, and generating short-form creative content where speed is a priority.
Mistral Small (Sep) scores 13 on the Artificial Analysis Intelligence Index, which is below the average of 20 for comparable models. It is classified as a 'non-reasoning' model, meaning it excels at pattern recognition and text generation but struggles with tasks requiring deep analytical thought, problem-solving, or nuanced understanding.
Compared to the average, Mistral Small (Sep) is on the more expensive side, with input tokens at $0.20/1M and output tokens at $0.60/1M. While its speed can offer value in specific high-throughput scenarios, its premium pricing means that for many standard or budget-sensitive tasks, more cost-effective alternatives might exist, even if they are slightly slower.
Mistral Small (Sep) features a substantial 33k token context window. This allows the model to process and generate text based on a relatively large amount of preceding information, making it suitable for tasks that require maintaining context over longer conversations or documents.
No, Mistral Small (Sep) is currently available exclusively through the Mistral API. It is not an open-weight model that can be downloaded and self-hosted. This means users are reliant on Mistral's infrastructure and pricing for its usage.
With an output speed of 131 tokens per second, Mistral Small (Sep) can significantly reduce response times in applications where quick turnaround is critical. This is particularly beneficial for user-facing interfaces like chatbots, real-time content generation tools, or any system where latency directly impacts user experience or operational efficiency.