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Devstral Small 1.1
Devstral Small 1.1
Upgraded Speed and Accuracy for Everyday AI
What is Devstral Small 1.1?
Devstral Small 1.1 is the improved version of Devstral Small 1, built for users who want lightweight AI performance with better accuracy and faster responses. It’s designed for startups, small businesses, and individual developers who need a dependable AI without the complexity or cost of larger models.
Compared to version 1.0, Devstral Small 1.1 offers improved text clarity, smarter context handling, and slightly stronger coding support, while still being resource-efficient and budget-friendly.
Key Features of Devstral Small 1.1
Use Cases of Devstral Small 1.1
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What are the Risks & Limitations of Devstral Small 1.1
Limitations
- Context Window Limits: Hard limits on token input can lead to lost data in long-form coding tasks.
- Reduced Logic Depth: Smaller parameter counts often struggle with highly nested architectural logic.
- Knowledge Recency: Lacks awareness of software libraries released after its specific training cutoff.
- Multilingual Gaps: Performance fluctuates significantly when prompting in less common programming languages.
- Inference Speed Trades: While fast, it may prioritize speed over the exhaustive validation of complex code.
Risks
- Vulnerability Injection: May inadvertently suggest deprecated functions that contain known security flaws.
- Hallucinated Libraries: Risks generating references to non-existent packages that could mask malware.
- Bias in Logic: Potential for ingrained biases to influence algorithmic decision-making and fairness.
- Data Privacy Leakage: Small models might mirror sensitive patterns found within their massive training sets.
- Over-reliance Hazard: Users might skip manual code reviews, leading to the deployment of silent logical bugs.
Parameter
- Quality (MMLU Score)
- Inference Latency (TTFT)
- Cost per 1M Tokens
- Hallucination Rate
- HumanEval (0-shot)
Devstral Small 1.1
Create or Sign In to an Account
Register on the platform providing Devstral models and complete any required verification steps.
Locate Devstral Small 1.1
Navigate to the AI or language model section and select Devstral Small 1.1 from the list of available models.
Choose an Access Method
Decide whether to use hosted API access for immediate usage or local deployment if self-hosting is supported.
Enable API or Download Model Files
Generate an API key for hosted usage, or download the model weights, tokenizer, and configuration files for local deployment.
Configure and Test the Model
Adjust inference parameters such as maximum tokens and temperature, then run test prompts to ensure proper functionality.
Integrate and Monitor Usage
Embed Devstral Small 1.1 into applications or workflows, monitor performance and resource consumption, and optimize prompts for consistent results.
Pricing of the Devstral Small 1.1
Devstral Small 1.1 uses a usage‑based pricing model, where costs are tied to the number of tokens processed both the text you send (input tokens) and the text the model generates (output tokens). Rather than paying a fixed subscription, you pay only for the compute your application actually consumes, making this structure flexible and scalable from small prototype tests to large‑scale production environments. This approach lets teams forecast budgets based on expected prompt lengths, typical response size, and overall usage volume, helping avoid paying for unused capacity.
In common API pricing tiers, input tokens are billed at a lower rate than output tokens because generating responses generally requires more compute effort. For example, Devstral Small 1.1 might be priced at around $1.75 per million input tokens and $7 per million output tokens under standard usage plans. Larger contexts or longer responses naturally increase total spend, so refining prompt structure and managing response verbosity can help optimize costs. Because output tokens generally make up the majority of billing, controlling how much text the model generates is key to effective cost control.
To further manage expenses, developers often use prompt caching, batching, and context reuse, which reduce redundant processing and lower effective token counts. These optimization techniques are especially valuable in high‑volume environments such as automated chatbots, content generation pipelines, and data interpretation tools. With transparent usage‑based pricing and smart cost‑management strategies, Devstral Small 1.1 provides a predictable, scalable pricing structure suitable for a wide range of AI‑driven applications.
Future versions will continue to refine accuracy, context retention, and industry-specific capabilities, keeping Devstral models a strong choice for lightweight AI applications.
Get Started with Devstral Small 1.1
Frequently Asked Questions
Version 1.1 (the "2507" release) provides a critical upgrade in Generalization. While version 1.0 was tightly coupled with the OpenHands scaffold, 1.1 is more robust when used with other prompts, IDE plugins (like Cline or Roo Code), and custom bash-scripts. It also introduces native support for Mistral’s structured function calling, making tool orchestration significantly more reliable than text-pattern parsing.
Codestral is optimized for FIM (Fill-In-the-Middle) and code completion. In contrast, Devstral Small 1.1 is an Agentic Specialist. It is trained on "Action Trajectories," meaning it understands how to navigate a file system, read README files to build a mental map of the project, and execute tests to verify its own work. It treats the 128k context as a workspace rather than just a code-buffer.
Devstral Small 1.1 is the recommended "brain" for the OpenHands (formerly OpenDevin) platform. To deploy, use the CodeAct interaction strategy. Developers should launch an OpenAI-compatible server (like vLLM) and point OpenHands to the v1/chat/completions endpoint. This allows the model to interact with a secure Docker sandbox where it can safely execute pip install or npm test commands.
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