This model is deprecated. We still benchmark its performance, but less often: every 18 hours instead of every 6.

Mistral has launched a newer release, Devstral 2. We suggest considering it instead.

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Proprietary model

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Released July 2025

Devstral Medium: Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out

Intelligence

N/A

Artificial Analysis Intelligence Index

Performance

N/A

Time per Intelligence Index task (minutes)

Cost

In -Out -

N/A

Cost per Intelligence Index task

Verbosity

N/A

Output tokens from Intelligence Index

Devstral Medium supports text input, outputs text, and has a 256k tokens context window.

Reasoning	No
Input modality	Supports: text
Output modality	Supports: text
Context window	256k ~384 A4 pages of size 12 Arial font

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

No publicly available API

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Further details

								Weights
Devstral Medium	Mistral	-	-	256k	-	-	-	Not available	-

Frequently Asked Questions

Common questions about Devstral Medium

Devstral Medium was released on July 10, 2025.

Devstral Medium was created by Mistral.

No, Devstral Medium is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.

Devstral Medium supports text input.

Devstral Medium supports text output.

No, Devstral Medium does not support image input. It can only process text.

No, Devstral Medium is not multimodal. It only supports text input.

Devstral Medium has a context window of 256k tokens. This determines how much text and conversation history the model can process in a single request.

No, Devstral Medium is proprietary. The model weights are not publicly available.

Devstral Medium is a proprietary model and Mistral has not disclosed the model size or parameter count.

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Devstral Medium: Intelligence, Performance & Price Analysis

Model summary

Intelligence

Performance

Cost

Verbosity

Technical specifications

302 models in this class

Intelligence

Time per Task

Cost per Task

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Further details

More from Mistral

Frequently Asked Questions

When was Devstral Medium released?

Who created Devstral Medium?

Is Devstral Medium a reasoning model?

What input modalities does Devstral Medium support?

What output modalities does Devstral Medium support?

Can Devstral Medium process images?

Is Devstral Medium multimodal?

What is the context window of Devstral Medium?

Is Devstral Medium open source?

How many parameters does Devstral Medium have?

Is Devstral Medium available via API?

Where can I use Devstral Medium?