An open-weight model from Mistral that balances high intelligence and exceptional speed with a very competitive price point.
Devstral Small 2 emerges as a formidable contender in the open-weight model space, developed and optimized by its creator, Mistral. It carves out a distinct niche by delivering a powerful trifecta of high-end intelligence, blistering speed, and an exceptionally low cost structure. This combination makes it an attractive option for a wide range of applications, from real-time conversational agents to complex document analysis, without the typical performance or budget compromises.
On the Artificial Analysis Intelligence Index, Devstral Small 2 achieves a score of 32, placing it firmly in the upper echelon of its class and significantly outperforming the average score of 20 for comparable models. This high score reflects its ability to generate nuanced, accurate, and coherent text across a variety of tasks. This intelligence does come with a minor caveat: the model is somewhat verbose, generating 15 million tokens during the index evaluation compared to the 13 million average. While this can lead to more detailed outputs, it's a factor to manage in token-sensitive applications.
Performance is where Devstral Small 2 truly shines. Clocking in at a median output speed of over 205 tokens per second, it ranks among the fastest models available, making it ideal for interactive use cases where responsiveness is critical. This speed is complemented by a low latency (time to first token) of just 0.36 seconds, ensuring that users receive an immediate response. This performance profile is particularly impressive given its high intelligence score, as speed and quality are often at odds.
Perhaps its most disruptive feature is its price. On the benchmarked provider, Mistral, Devstral Small 2 is priced at an unbeatable $0.00 for both input and output tokens. This is a dramatic departure from the class averages of $0.10 per million input tokens and $0.20 per million output tokens. This pricing effectively removes the cost barrier for experimentation and deployment, allowing developers to leverage its capabilities without budgetary constraints. Further enhancing its versatility, the model supports a massive 256k token context window and can process both text and image inputs, opening up a vast landscape of potential use cases.
32 (6 / 55)
205.2 tokens/s
$0.00 / 1M tokens
$0.00 / 1M tokens
15M tokens
0.36 seconds
| Spec | Details |
|---|---|
| Model Owner | Mistral |
| License | Open |
| Context Window | 256,000 tokens |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Architecture | Transformer-based |
| Model Size | Small |
| Fine-tuning Support | Yes (as an open-weight model) |
| Primary API Provider | Mistral |
| Intelligence Index Score | 32 |
| Speed Index Rank | #5 / 55 |
| Verbosity Index Rank | #22 / 55 |
Choosing a provider for Devstral Small 2 is currently a simple decision, as benchmarks highlight a single, dominant option. The model's creator, Mistral, offers a highly optimized environment that delivers the impressive performance and cost metrics detailed in this analysis. For developers looking to get the most out of the model, the official API is the clear starting point.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Best Performance | Mistral | Offers the highest benchmarked speed (205 t/s) and lowest latency (0.36s), as the platform is optimized by the model's creators. | Tied to the model creator's ecosystem; less provider diversity compared to models on larger platforms. |
| Lowest Cost | Mistral | The only benchmarked provider offering a $0.00 price point for both input and output tokens, making it effectively free to use. | This pricing may be promotional and is subject to change. Future costs are uncertain. |
| Simplicity & Support | Mistral | The official API is well-documented, stable, and directly supported by the team that built the model, ensuring a smooth integration experience. | Fewer third-party tools or platform-specific abstractions compared to multi-model marketplaces. |
| Largest Context | Mistral | Guarantees reliable access to the full 256k context window as intended by the model's design. | None; it is the reference implementation for the model's capabilities. |
Provider benchmarks are based on available data at the time of analysis. Performance and pricing are subject to change. 'Pick' reflects the best option for the stated priority based on our data, not a universal endorsement.
To understand the practical cost implications of using Devstral Small 2, let's examine a few common scenarios. The following table estimates costs based on the benchmarked price of $0.00 per million input and output tokens. While the costs are currently zero, this table is useful for illustrating the token usage required for each task, which is critical for future budget planning.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Email Summarization | 1,500 tokens | 200 tokens | Summarizing a long email thread for a daily brief. | $0.00 |
| Customer Support Chatbot | 3,000 tokens | 1,000 tokens | A 10-turn conversation with a user, including conversation history. | $0.00 |
| Code Generation | 500 tokens | 1,500 tokens | Generating a Python function based on a detailed docstring. | $0.00 |
| Document Q&A | 50,000 tokens | 500 tokens | Asking a question about a large PDF report loaded into the context window. | $0.00 |
| Blog Post Draft | 100 tokens | 2,000 tokens | Generating a first draft of a blog post from a short prompt. | $0.00 |
At its current price point, Devstral Small 2 makes even token-intensive tasks like large-document analysis virtually free. The key takeaway for developers is the freedom to build complex, high-volume applications without immediate cost concerns. However, it remains wise to monitor token usage closely in anticipation of future pricing adjustments and to build efficient, token-aware applications from the start.
While Devstral Small 2 is currently cost-free on its native platform, building good cost-management habits is crucial for long-term project viability and preparing for potential future pricing. The following strategies will help you optimize token usage and ensure your application remains efficient regardless of the underlying cost structure.
Devstral Small 2 is slightly more verbose than average. You can guide it to produce more concise outputs through careful prompt engineering. This practice reduces output token counts and improves response speed.
The 256k context window is a powerful feature, but using it unnecessarily can increase processing time and would be costly on a priced model. Only provide the context that is absolutely necessary for the task at hand.
Many applications receive identical or highly similar user queries. Implementing a cache saves you from making redundant API calls for requests the model has already processed.
A $0.00 price point is unlikely to last forever. Build your application with cost visibility from day one to avoid being caught off guard by future pricing adjustments.
Devstral Small 2 is an open-weight, multimodal large language model from Mistral. It is engineered to provide a best-in-class balance of high intelligence, extremely fast generation speed, and a large 256,000-token context window, all while being offered at a highly competitive price.
Compared to other models in the 'small' category, Devstral Small 2 is a top performer. It ranks in the top tier for both intelligence (score of 32 vs. 20 average) and speed (205 tokens/s), making it faster and smarter than many of its direct competitors.
Based on the benchmarked data from the official Mistral API, the price is currently $0.00 per million tokens for both input and output. It's important to treat this as a potentially promotional or introductory rate that could change in the future. Self-hosting the open-weight model would incur infrastructure costs.
It means the model can process more than one type of data as input. Specifically, Devstral Small 2 can accept both text and images. This allows it to perform tasks like describing a picture, answering questions about a diagram, or interpreting visual information in conjunction with a text prompt. The model's output is limited to text.
A 256,000-token context window is exceptionally large and enables powerful use cases. It allows the model to:
The primary trade-offs are minor but important to consider. First, it is slightly more verbose than the average model, which could impact costs if its price increases. Second, its peak performance and current pricing are tied to the Mistral platform, offering less provider choice than more widely distributed models. Finally, the $0.00 price point carries inherent uncertainty about future costs.