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Model details

Magistral Medium (latest)

Magistral Medium sits as the larger sibling in Mistral AI's Magistral family, which marks the company's first deliberate move into reasoning-focused models rather than general-purpose chat. It is positioned above the 24B Apache-2-licensed Magistral Small variant, offering higher capacity for deliberation while remaining available only through a managed API rather than as downloadable open weights. The family is organized around explicit step-by-step inference, making the Medium tier a natural fit for workflows where traceable logic matters more than free-form generation.

In practice, Magistral Medium is most useful for multi-step problem solving, code reasoning, and analytical tasks that benefit from visible chains of thought rather than quick single-shot answers. For teams already building on Mistral models, it acts as a specialized reasoning component that can be slotted into pipelines alongside general chat models, adding deliberation where needed without replacing conversational behavior elsewhere. Its API-only delivery simplifies adoption for organizations that prefer managed endpoints over self-hosting larger reasoning weights.

Pioneermagistral-mediummagistral-medium

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Provider
Pioneer
Model key
magistral-medium
Release date
Mar 17, 2025
Last updated
Mar 20, 2025
Knowledge cutoff
2025-06
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$2.00
Output token cost
$5.00

Limits

Output tokens
64,000 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 Magistral Medium (latest)

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CoverageBenchmark

OpenOCR published a benchmark evidence page for "magistral-medium-2509" on 2026-08-22, reporting an average character-LCS similarity score of 97.4% across 11 documents (grocery and café receipts, handwritten letters and notes), with an average latency of about 1.9 seconds per document. The page explicitly states the pr The benchmark is narrow — it measures OCR transcription on a fixed 11-document synthetic corpus rather than general reasoning, coding, or agentic behavior, so it should be read as capability evidence on a specific OCR slice rather than as an overall model evaluation. Useful signal from the page: concrete per-document s

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