An exceptionally fast and concise model from Mistral, offering a massive 256k context window but at a high cost for output and with below-average intelligence.
Devstral Medium emerges from the Mistral family as a model with a distinct and specialized profile. It is engineered for speed and conciseness, delivering responses at a blistering pace of over 105 tokens per second. This performance, combined with a very large 256,000-token context window, positions it as a powerful tool for tasks requiring rapid processing of extensive information. However, this specialization comes with significant trade-offs that potential users must carefully consider. The model's intelligence, as measured by the Artificial Analysis Intelligence Index, is below average, and its pricing structure heavily penalizes generative, output-heavy tasks.
On the Artificial Analysis Intelligence Index, Devstral Medium scores a 28, placing it in the lower half of the 77 models benchmarked. This suggests that for tasks demanding complex reasoning, deep nuance, or creative problem-solving, other models may be more suitable. Where Devstral Medium truly stands out is its brevity. During the intelligence evaluation, it generated only 4.4 million tokens, a stark contrast to the 11 million token average. This makes it the third most concise model in the entire index, a valuable trait for applications where users need quick, to-the-point answers without extraneous detail. This conciseness also has a direct impact on cost, mitigating the high price of its output tokens.
The cost of using Devstral Medium is a critical factor. Its input tokens are priced at $0.40 per million, which is more expensive than the average for comparable models. The real story, however, is the output token price: a steep $2.00 per million tokens. This 5x multiplier between input and output makes it one of the more expensive models for generative workloads. The total cost to run the Intelligence Index benchmark on Devstral Medium was $39.39, a figure that underscores the financial implications of its pricing. Consequently, the model is most cost-effective for input-heavy tasks like summarization, classification, and data extraction, where the volume of generated text is low relative to the amount of text processed.
In essence, Devstral Medium is not a general-purpose workhorse. It is a specialist's tool. Developers who need to build real-time applications, process large documents for specific insights, or power systems where response speed is paramount will find immense value here. Its low latency (time to first token) of 0.44 seconds further enhances its suitability for interactive use cases like advanced chatbots or coding assistants that need to feel responsive. However, those building applications centered around creative writing, complex instruction-following, or open-ended generation must weigh the model's speed against its lower intelligence and high output costs. Choosing Devstral Medium is a strategic decision to prioritize throughput and brevity over reasoning capability and budget-friendliness for generative tasks.
28 (39 / 77)
105.6 tokens/s
$0.40 / 1M tokens
$2.00 / 1M tokens
4.4M tokens
0.44 seconds
| Spec | Details |
|---|---|
| Owner | Mistral |
| License | Proprietary |
| Context Window | 256,000 tokens |
| Input Modalities | Text |
| Output Modalities | Text |
| Blended Price (3:1) | $0.80 / 1M tokens |
| Input Token Price | $0.40 / 1M tokens |
| Output Token Price | $2.00 / 1M tokens |
| Intelligence Index Score | 28 |
| Intelligence Rank | #39 / 77 |
| Speed Rank | #29 / 77 |
| Verbosity Rank | #3 / 77 |
Devstral Medium is offered directly by its creator, Mistral, via their official API. This simplifies the choice of provider to a single, canonical source. While this eliminates provider competition on price or features, it guarantees that you are using the definitive version of the model as intended by its developers. Our analysis, therefore, focuses on how to best leverage the model based on your priorities when using the Mistral API.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Maximum Speed | Mistral API | Direct API access to the model's origin ensures the lowest possible network latency and highest potential throughput. | You are subject to Mistral's pricing with no alternative options. |
| Lowest Cost | Mistral API (Input-Heavy) | Focus on workloads like summarization or classification to take advantage of the much cheaper $0.40 input token price. | Avoid generative tasks, as the $2.00 output token price will quickly escalate costs. |
| Large Context Use | Mistral API | The official API provides full, unfettered access to the entire 256k context window for large-scale document analysis. | Processing full context can be costly and may increase latency, negating some of the model's speed advantage. |
| Operational Simplicity | Mistral API | Using the official API means clear documentation, direct support channels, and no third-party layers to debug. | You miss out on potential value-adds from other platforms, such as unified APIs, advanced logging, or different pricing models. |
Performance metrics such as latency and throughput are based on benchmarks conducted by Artificial Analysis on the specified provider. Your actual real-world performance may vary depending on your specific workload, geographic region, and concurrent API traffic.
The true cost of operating Devstral Medium is dictated entirely by your application's workload. The five-fold difference between its input and output token prices ($0.40 vs. $2.00 per million) means that the ratio of input-to-output is the single most important variable in your monthly bill. A slight shift in this ratio can lead to a dramatic change in cost. The following scenarios break down the estimated cost for common tasks, illustrating how this price disparity plays out in the real world.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Chatbot Session | 1,500 tokens | 500 tokens | A typical back-and-forth user conversation. | $0.0016 |
| Long Document Summary | 20,000 tokens | 1,000 tokens | Summarizing a lengthy report or academic paper. | $0.0100 |
| Code Generation | 500 tokens | 2,000 tokens | Generating a functional code block from a description. | $0.0042 |
| RAG Query | 8,000 tokens | 400 tokens | Answering a question using provided context. | $0.0040 |
| Marketing Copy Generation | 200 tokens | 1,500 tokens | Creating a few paragraphs of ad copy from a brief. | $0.0031 |
| Data Extraction | 15,000 tokens | 300 tokens | Pulling structured data from unstructured text. | $0.0066 |
The data clearly shows that input-heavy tasks like summarization and RAG are where Devstral Medium is most cost-effective. A task that summarizes a 20k-token document costs just over twice as much as one that generates a 2k-token code block, despite processing ten times the number of total tokens. Applications that are generative by nature will find their costs dominated by the expensive output tokens.
Managing costs for Devstral Medium requires a deliberate strategy centered on its asymmetric pricing. To use this model effectively without incurring excessive fees, you must architect your application to minimize expensive output tokens and maximize the value of cheaper input tokens. This isn't just about prompt engineering; it's about designing workflows with cost in mind from the start. Here are several practical strategies to control your spending.
Since every output token costs 5x more than an input token, prompt engineering for brevity is crucial. Instruct the model directly to be concise.
Design your application around tasks where the model's cost structure is an advantage, not a liability. This means focusing on use cases that involve more reading than writing.
Never make an API call without setting the max_tokens parameter. This is your primary safety net against runaway generation and unexpected costs. It acts as a hard cap on the cost of any single API call.
max_tokens to a value slightly above this expected length to allow for variance, but low enough to prevent excessive generation.You cannot manage what you do not measure. Implement logging to track the number of input and output tokens for every API call. This data is vital for understanding your true costs and identifying areas for optimization.
Devstral Medium is a large language model from Mistral. It is characterized by its very high output speed, extreme conciseness, and a large 256,000-token context window. These strengths are balanced by below-average performance on complex reasoning tasks and a high price for output tokens, making it a specialized tool rather than a general-purpose model.
Developers and businesses that prioritize speed and throughput above all else are the ideal users. It excels in real-time applications, high-volume batch processing of documents, and interactive systems where low latency is key. If your use case is input-heavy (like summarization or RAG) and can tolerate moderate intelligence, Devstral Medium offers a compelling performance profile.
Devstral Medium occupies a specific niche. It is likely faster but less intelligent than flagship models like Mistral Large. Compared to open-weight models like Mixtral, it offers a much larger context window and potentially higher hosted speed, but at the cost of being a proprietary, paid API. It is designed for a different purpose: maximum throughput on large-context, low-generation tasks.
A 256,000-token context window is exceptionally large. It can hold approximately 190,000 words, equivalent to a 400-500 page book like 'The Great Gatsby' or a very large codebase. This allows the model to reason about vast amounts of information in a single pass, making it ideal for 'needle in a haystack' problems, comprehensive document analysis, or maintaining long-running conversations without losing context.
The pricing strategy, with output tokens costing 5x more than input tokens, reflects the underlying computational costs. Processing input (ingestion) is generally less computationally intensive than generating new tokens (inference). The high output price likely subsidizes the model's high speed and large context capabilities, while also encouraging users to adopt it for the input-heavy tasks it is best suited for.
Generally, no. While it can generate text very quickly, its two main weaknesses work against it for creative tasks. First, its below-average intelligence score suggests it may lack the nuance, creativity, and coherence needed for high-quality storytelling or marketing copy. Second, creative writing is an output-heavy task, which would make it very expensive to use Devstral Medium compared to other models that are both more capable and have a more balanced pricing structure.