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Model details

Devstral Small 2505

Devstral Small 2505 is an open-weights language model positioned as an agentic coding assistant, jointly developed by Mistral AI and All Hands AI and fine-tuned from Mistral Small 3.1. It carries roughly 24 billion parameters and the cataloged API limit context window, giving it room to read substantial codebases, multi-file diffs, and long execution traces in a single pass. The model's lineage from a general-purpose small model means it inherits broad reasoning ability before being specialized toward software engineering workflows such as repository navigation, bug fixing, and tool-mediated editing.

In qualitative terms, Devstral Small 2505 debuted with the claim of being the top open-source entry on SWE-bench, signaling strong real-world coding competence rather than just leaderboard-style puzzle performance. Independent evaluation providers such as Artificial Analysis have published benchmark context for the model, which helps users gauge its coding and reasoning behavior outside of Mistral's own marketing. Because the weights are openly available, community variants like a 4-bit MLX quantization for Apple Silicon extend the model's reach to lightweight local inference, making it a flexible choice for both server-side agent pipelines and on-device development helpers.

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Quick Info

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Provider
Mistral
Model key
devstral-small-2505
Release date
May 7, 2025
Last updated
May 7, 2025
Knowledge cutoff
2025-05
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.10
Output token cost
$0.30

Limits

Output tokens
128,000 tokens
Context window
128,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Devstral Small 2505

Mistral

Coverage

The official Hugging Face model card for mistralai/Devstral-Small-2505 confirms the exact variant is a 24-billion-parameter fine-tune of Mistral-Small-3.1 (with the vision encoder removed before fine-tuning) and is text-only, optimized for coding-agent use cases such as exploring codebases and editing multiple files. I The model card reproduces the 46.8% SWE-Bench Verified score under the OpenHands scaffold and provides a comparator table showing GPT-4.1-mini at 23.6%, Claude 3.5 Haiku at 40.6%, and SWE-smith-LM 32B at 40.2%, positioning Devstral as the top open-source model on the benchmark. It notes that under the same scaffold Dev

Mistral

CoverageBenchmark

Devstral-Small-2505 is a 24B parameter agentic LLM fine-tuned from Mistral-Small-3.1, jointly developed by Mistral AI and All Hands AI for advanced software engineering tasks. 131,072 token context window. Includes independent benchmarks from Artificial Analysis.

Mistral

Coverage

Devstral by MistralAI is based on Mistral Small 3.1. Debuts as the #1 open source model on SWE-bench.

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