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Model details

Mistral Medium 3

Mistral Medium 3 was introduced by Mistral AI on May 7, 2025 as a new mid-tier class of language model designed to balance frontier performance with a substantially lower cost profile and easier enterprise deployment. According to the official Mistral announcement, the model is positioned to deliver state-of-the-art results at roughly an order of magnitude less expense than comparable frontier systems, with hybrid, on-premises, and in-VPC deployment supported alongside custom post-training and integration with existing enterprise tooling. The release also emphasizes simpler deployability as a core differentiator, aiming to make advanced language capabilities more accessible for organizations that need to run models inside their own infrastructure rather than relying solely on external APIs.

Mistral AI highlights Mistral Medium 3 as particularly strong in professional use cases, with the announcement calling out coding and multimodal understanding as leading capabilities. The model is described as performing at or above the level of significantly larger and more expensive competitors on broad benchmarks while remaining more economical to operate, and it is framed as surpassing leading open and enterprise alternatives on a cost-versus-performance basis. In practice, this makes Mistral Medium 3 a fit for teams that want capable code assistance, document and image understanding, and flexible deployment options without stepping up to the largest frontier models, and it is already offered through multiple serving platforms including IBM watsonx alongside the Mistral ecosystem.

Vertexmistral-medium-3mistral-medium

Quick Info

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Provider
Vertex
Model key
mistral-medium-3
Release date
May 7, 2025
Last updated
May 7, 2025
Knowledge cutoff
2025-05
AI SDK package
@ai-sdk/mistral
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.40
Output token cost
$2.00

Limits

Output tokens
131,072 tokens
Context window
131,072 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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