Mathstral

Mathstral

Mistral AI
MiniMax M3

MiniMax M3

MiniMax
+
+

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About

As a tribute to Archimedes, whose 2311th anniversary we’re celebrating this year, we are proud to release our first Mathstral model, a specific 7B model designed for math reasoning and scientific discovery. The model has a 32k context window published under the Apache 2.0 license. We’re contributing Mathstral to the science community to bolster efforts in advanced mathematical problems requiring complex, multi-step logical reasoning. The Mathstral release is part of our broader effort to support academic projects, it was produced in the context of our collaboration with Project Numina. Akin to Isaac Newton in his time, Mathstral stands on the shoulders of Mistral 7B and specializes in STEM subjects. It achieves state-of-the-art reasoning capacities in its size category across various industry-standard benchmarks. In particular, it achieves 56.6% on MATH and 63.47% on MMLU, with the following MMLU performance difference by subject between Mathstral 7B and Mistral 7B.

About

MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Individuals focused on mathematical research looking for an AI tool to enhance their problem-solving capabilities

Audience

MiniMax M3 is best suited for developers, AI engineers, coding assistant builders, enterprise automation teams, research teams, data teams, agent developers, and organizations that need open-weight AI for coding, long-context reasoning, multimodal understanding, tool use, repository analysis, and autonomous workflows

Support

Phone Support Not Supported
24/7 Live Support Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version Supported
Free Trial Not Supported

Pricing

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 5.0 / 5
ease 4.0 / 5
features 4.0 / 5

Pros & Cons from Real Users

Pros

  • The multimodal side also makes it more flexible than a code-only model. Being able to work across text, images, video-style understanding, and code gives it more room to support real workflows instead of being boxed into one narrow use case. For agent builders, the best part is that M3 is clearly designed around tool use and multi-step execution. MiniMax specifically calls out autonomous task decomposition and tool invocation, which is exactly what matters when a model is powering coding assistants, workflow agents, or automated dev tools.

Cons

  • The main catch is infrastructure and trust. Even with sparse attention and MoE efficiency, this is still a large model, so deployment is not casual. I would also want to benchmark it on my own repos before depending on it for production work.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Mistral AI
Founded: 2023
France
mistral.ai/news/mathstral/

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io

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Categories

AI Models Supported
Foundation Models Supported

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported
Multimodal Models Supported

Integrations

Arize Phoenix Supported
BLACKBOX AI Not Supported
Claude Code Not Supported
Cline Not Supported
ClinePass Not Supported
EvalsOne Supported
GMTech Supported
Hugging Face Supported
Literal AI Supported
Mammouth AI Supported
Microsoft Foundry Agent Service Supported
MiniMax Mavis Not Supported
Mistral NeMo Supported
Msty Supported
NexalAI Supported
Overseer AI Supported
PI Prompts Supported
Respan Supported
Roo Code Not Supported
Tabbit Browser Not Supported

Integrations

Arize Phoenix Not Supported
BLACKBOX AI Supported
Claude Code Supported
Cline Supported
ClinePass Supported
EvalsOne Not Supported
GMTech Not Supported
Hugging Face Not Supported
Literal AI Not Supported
Mammouth AI Not Supported
Microsoft Foundry Agent Service Not Supported
MiniMax Mavis Supported
Mistral NeMo Not Supported
Msty Not Supported
NexalAI Not Supported
Overseer AI Not Supported
PI Prompts Not Supported
Respan Not Supported
Roo Code Supported
Tabbit Browser Supported
Claim Mathstral and update features and information
Claim Mathstral and update features and information
Claim MiniMax M3 and update features and information
Claim MiniMax M3 and update features and information