Mistral: Devstral Small 1.1

Text input Text output
Author's Description

Devstral Small 1.1 is a 24B parameter open-weight language model for software engineering agents, developed by Mistral AI in collaboration with All Hands AI. Finetuned from Mistral Small 3.1 and released under the Apache 2.0 license, it features a 128k token context window and supports both Mistral-style function calling and XML output formats. Designed for agentic coding workflows, Devstral Small 1.1 is optimized for tasks such as codebase exploration, multi-file edits, and integration into autonomous development agents like OpenHands and Cline. It achieves 53.6% on SWE-Bench Verified, surpassing all other open models on this benchmark, while remaining lightweight enough to run on a single 4090 GPU or Apple silicon machine. The model uses a Tekken tokenizer with a 131k vocabulary and is deployable via vLLM, Transformers, Ollama, LM Studio, and other OpenAI-compatible runtimes.

Key Specifications
Cost
$$
Context
131K
Parameters
24B (Rumoured)
Released
Jul 10, 2025
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Tools Structured Outputs Tool Choice Response Format Stop Seed Top P Max Tokens Frequency Penalty Temperature Presence Penalty
Features

This model supports the following features:

Tools Response Format Structured Outputs
Performance Summary

Devstral Small 1.1, a 24B parameter model from Mistral AI, demonstrates strong performance tailored for software engineering agents. It performs among the fastest models, ranking in the 78th percentile for speed, and consistently offers competitive pricing, placing in the 82nd percentile. Notably, its reliability is exceptional, achieving a 100% success rate across all benchmarks, indicating minimal technical failures. The model exhibits particular strength in coding-related tasks, achieving 85.0% accuracy in the Coding benchmark and a remarkable 53.6% on SWE-Bench Verified, surpassing all other open models. It also performs well in Email Classification (98.0% accuracy) and shows solid general knowledge (97.5%). While its Mathematics accuracy (85.0%) is notable for its speed, its Instruction Following (51.0%) and Reasoning (64.0%) scores are more moderate. Hallucination rates are relatively low at 92.0% accuracy. Its optimization for agentic coding workflows, combined with its efficiency and deployability, makes it a compelling choice for software development applications.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.1
Completion $0.3

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
Mistral
Mistral | mistralai/devstral-small-2507 131K $0.1 / 1M tokens $0.3 / 1M tokens
Parasail
Parasail | mistralai/devstral-small-2507 131K $0.07 / 1M tokens $0.28 / 1M tokens
NextBit
NextBit | mistralai/devstral-small-2507 131K $0.07 / 1M tokens $0.28 / 1M tokens
DeepInfra
DeepInfra | mistralai/devstral-small-2507 128K $0.07 / 1M tokens $0.28 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Strategy Free Executions Accuracy Cost Duration
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