MiniMax M3

MiniMax M3

MiniMax
SubQ

SubQ

Subquadratic
+
+

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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.

About

SubQ is a large language model developed by Subquadratic, designed specifically for long-context reasoning tasks. It can process up to 12 million tokens in a single prompt, allowing it to analyze entire codebases, long histories, and complex datasets at once. The model uses a sub-quadratic sparse-attention architecture that improves efficiency by focusing only on the most relevant relationships in the data. This approach reduces computational overhead while maintaining strong performance on large-scale tasks. SubQ is optimized for use cases such as software engineering, coding agents, and long-context retrieval. It delivers fast processing speeds and operates at a lower cost compared to many traditional models. Developers can access SubQ through APIs or integrate it into coding tools for enhanced workflows. Its architecture enables scalable AI reasoning without the limitations of standard transformer models.

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

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

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

Audience

Developers, AI engineers, and enterprises that need large-context language models for coding, data analysis, and advanced AI workflows

Support

Phone Support Not Supported
24/7 Live Support Not 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

$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

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

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

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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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

MiniMax
Founded: 2021
Singapore
www.minimax.io

Company Information

Subquadratic
Founded: 2026
United States
subq.ai/

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SubQ 1.1 Small

Subquadratic
MiniMax M1

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MiniMax M3

MiniMax M3

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Categories

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

Categories

AI Coding Models Supported
AI Models Supported

Integrations

Claude Code Supported
APIFree Supported
Alibaba AI Coding Plan Supported
Clawd.run Supported
Cline Supported
ClinePass Supported
Factory Droid Supported
Kilo Code Supported
MiniMax Agent Supported
MiniMax Mavis Supported
Ollama Supported
OpenAI Not Supported
OpenAI Codex Not Supported
OpenClaw Supported
OpenCode Go Supported
OpenTag Supported
Roo Code Supported
SubQ 1.1 Small Not Supported
Together AI Supported
Vision Agents Supported

Integrations

Claude Code Supported
APIFree Not Supported
Alibaba AI Coding Plan Not Supported
Clawd.run Not Supported
Cline Not Supported
ClinePass Not Supported
Factory Droid Not Supported
Kilo Code Not Supported
MiniMax Agent Not Supported
MiniMax Mavis Not Supported
Ollama Not Supported
OpenAI Supported
OpenAI Codex Supported
OpenClaw Not Supported
OpenCode Go Not Supported
OpenTag Not Supported
Roo Code Not Supported
SubQ 1.1 Small Supported
Together AI Not Supported
Vision Agents Not Supported
Claim MiniMax M3 and update features and information
Claim MiniMax M3 and update features and information
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Claim SubQ and update features and information