Mistral Medium 3
A versatile AI model designed for professional applications with particular strengths in coding and multimodal understanding. The model can be deployed across various environments, making it ideal for enhancing customer service, personalizing business processes, and analyzing complex datasets across industries.
Mistral Versatile text generation for professional and enterprise use
Mistral Medium 3 is a text generation model released on May 7, 2025 by Mistral, a French AI company. It is designed to balance performance with cost efficiency, priced at $0.40 per million input tokens and $2.00 per million output tokens. The model supports a 128,000-token context window and was trained on data through early 2025. It is available through Mistral La Plateforme and Amazon SageMaker, with additional platform support planned.
Mistral Medium 3 is built with enterprise deployment in mind, supporting self-hosted setups with a minimum of four GPUs as well as any cloud environment. It can be customized through continuous pre-training, fine-tuning, and integration with enterprise knowledge bases, making it applicable to domain-specific workflows in sectors such as financial services, energy, and healthcare. The model is noted for its strengths in coding tasks and multimodal understanding, and is suited for use cases including customer service automation, business process personalization, and complex dataset analysis.
What Mistral Medium 3 supports
Long Context Window
Processes up to 128,000 tokens in a single request, enabling analysis of long documents, codebases, or extended conversations without truncation.
Code Generation
Generates, explains, and debugs code across common programming languages, with coding identified as one of the model's primary strengths.
Multimodal Understanding
Handles tasks requiring multimodal comprehension, supporting analysis that goes beyond plain text inputs as noted in the model's official overview.
Fine-Tuning Support
Supports continuous pre-training and comprehensive fine-tuning, allowing organizations to adapt the model to domain-specific datasets and workflows.
Enterprise Deployment
Can be deployed on any cloud environment or self-hosted on a minimum of four GPUs, with integration options for enterprise knowledge bases.
Cost-Efficient Pricing
Priced at $0.40 per million input tokens and $2.00 per million output tokens, positioning it as an accessible option for organizations managing AI inference costs.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 76.0% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 57.8% |
| MATH-500 | Undergraduate and competition-level math problems | 90.7% |
| AIME 2024 | American math olympiad problems | 44.0% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 40.0% |
| HLE | Questions that challenge frontier models across many domains | 4.3% |
| SciCode | Scientific research coding and numerical methods | 33.1% |
Common questions about Mistral Medium 3
What is the context window size for Mistral Medium 3?
Mistral Medium 3 supports a context window of 128,000 tokens, allowing it to process long documents, extended conversations, or large codebases in a single request.
How is Mistral Medium 3 priced?
The model is priced at $0.40 per million input tokens and $2.00 per million output tokens when accessed via API.
What is the training data cutoff for Mistral Medium 3?
According to the available metadata, Mistral Medium 3 was trained on data through early 2025.
Where can Mistral Medium 3 be deployed?
Mistral Medium 3 is available through Mistral La Plateforme and Amazon SageMaker. It also supports self-hosted deployment on a minimum of four GPUs and can run on any cloud environment.
Can Mistral Medium 3 be fine-tuned for specific use cases?
Yes. The model supports continuous pre-training, comprehensive fine-tuning, and integration with enterprise knowledge bases, making it adaptable for domain-specific applications in industries such as healthcare, finance, and energy.
What people think about Mistral Medium 3
Community reception to Mistral Medium 3 on Reddit has been generally positive, with users in r/LocalLLaMA and r/singularity noting its coding capabilities and cost-to-performance ratio as notable attributes. Discussions around a subsequent version, Mistral Medium 3.1, generated significant engagement with over 500 upvotes on r/singularity.
A recurring concern at launch was the lack of local model support, which was explicitly noted in thread titles on r/LocalLLaMA. Some users expressed interest in running the model locally but found that option unavailable at the time of release.
Mistral Medium 3.1 LMArena
Did anyone try out Mistral Medium 3?
Mistral-Medium 3 (unfortunately no local support so far)
ClockBench: A visual AI benchmark focused on reading analog clocks
Documentation & links
Parameters & options
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