MiniMax M3

MiniMax M3

MiniMax
SubQ Snap

SubQ Snap

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 Snap is a long-context decision model for enterprise and agentic workflows, built to make structured decisions quickly over large amounts of information. It handles tasks such as choosing which files are relevant, selecting the next tool, checking whether a result passes, deciding whether a workflow should continue, and determining when a frontier model is needed. Instead of generating open-ended text, Snap takes context, a question, and a finite set of allowed answers, then returns a probability distribution over those answers. It supports up to 384K tokens of mixed text and image context, allowing it to reason over codebases, contracts, interaction histories, logs, screenshots, charts, diagrams, and scanned pages in a single call. Powered by Subquadratic Sparse Attention, Snap reduces the cost of reading long inputs while a decision head maps contextual representations directly to admissible answers without autoregressive generation.

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 and enterprise AI teams interested in a tool to make fast, structured decisions across long documents, codebases, multimodal inputs, and agentic 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

$0.05 per million input tokens
Free Version Not Supported
Free Trial 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: 2024
United States
subq.ai/introducing-subq-snap

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Categories

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

Categories

AI Decision Models Supported

Integrations

APIFree Supported
Alibaba AI Coding Plan Supported
BLACKBOX AI Supported
Claude Code Supported
Cline Supported
ClinePass Supported
Factory Droid Supported
Hermes Agent Supported
MiniMax Agent Supported
MiniMax Code Supported
Ollama Supported
OpenClaw Supported
OpenCode Go Supported
OpenCode Zen Supported
OpenTag Supported
Pi Agent Supported
Roo Code Supported
Tabbit Browser Supported
Together AI Supported
Vision Agents Supported

Integrations

APIFree Not Supported
Alibaba AI Coding Plan Not Supported
BLACKBOX AI Not Supported
Claude Code Not Supported
Cline Not Supported
ClinePass Not Supported
Factory Droid Not Supported
Hermes Agent Not Supported
MiniMax Agent Not Supported
MiniMax Code Not Supported
Ollama Not Supported
OpenClaw Not Supported
OpenCode Go Not Supported
OpenCode Zen Not Supported
OpenTag Not Supported
Pi Agent Not Supported
Roo Code Not Supported
Tabbit Browser Not 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
Claim SubQ Snap and update features and information
Claim SubQ Snap and update features and information