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Model details

Devstral 2

Devstral the listed price is a 123B-parameter dense transformer released by Mistral AI as an open-weights Instruct model fine-tuned for software engineering agents. The model is engineered for agentic coding workflows: it explores codebases using tools, orchestrates edits across multiple files while preserving architectural context, tracks framework dependencies, and detects failures with retry-and-correct behavior. That orientation makes it well suited to AI code assistants and automation pipelines that need to modernize legacy systems, refactor across files, or drive multi-step engineering tasks end to end. The model is distributed under a modified MIT license, allowing self-hosting and downstream fine-tuning for specialized languages or large enterprise codebases.

Devstral the listed price ships in FP8 with the cataloged API limit context window, giving it room to ingest substantial repositories and long interaction histories during agentic runs. On the SWE-bench Verified and SWE-bench Multilingual benchmarks, the Hugging Face model card reports scores of 72.2% and 61.3% respectively, with a 32.6% result on Terminal Bench the listed price, placing it ahead of similarly sized open coding models and competitive with much larger closed systems on agentic software engineering tasks. Compared with its predecessors in the Devstral line, the release emphasizes improved generalization across diverse coding environments and a stronger step-up in tool-driven task performance, positioning it as a practical foundation for organizations building production software engineering agents.

Opperdevstral-2512devstral

Quick Info

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Provider
Opper
Model key
devstral-2512
Release date
Dec 9, 2025
Last updated
Dec 9, 2025
Knowledge cutoff
2025-12
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.40
Output token cost
$2.00

Limits

Output tokens
8,192 tokens
Context window
256,000 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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