MiniMax M3

MiniMax M3

MiniMax
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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

Twigg is a context-management tool designed for long-term interactions with large language models that replaces linear chat threads with a visual, branching tree of conversation nodes, giving users control over what context is sent to the model. You can explore alternate directions by instantly creating new branches from any point, cutting or copying nodes, deleting or moving content to optimize the prompt context, and thus improve performance while managing complexity. The interface provides a dashboard tracking token usage per model and shows credit consumption transparently. By controlling context at the node level, Twigg helps reduce unnecessary tokens and supports the concurrency of ideas without cluttering your main workflow. Compatible with major models (via BYOK support) and designed for extended projects, Twigg essentially acts as “Git for LLMs”, enabling versioning, branching, and precise context management for conversational AI workflows.

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, researchers and AI teams interested in a solution to manage context, maintain clarity, and reduce token-waste

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

$6.66 per month
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

Twigg
Founded: 2025
United States
twigg.ai/

Alternatives

Alternatives

MiniMax

MiniMax

MiniMax AI

Categories

Categories

AI Tools Supported

Integrations

Alibaba AI Coding Plan Supported
Claude Code Supported
Claude Fable 5.1 Not Supported
Claude Fable 5.5 Not Supported
Claude Mythos 5.1 Not Supported
Claude Opus 4.1 Not Supported
Claude Opus 4.5 Not Supported
Clawd.run Supported
Cline Supported
ClinePass Supported
GPT-5 mini Not Supported
Gemini 2.0 Flash Not Supported
MaxHermes Supported
MiniMax Agent Supported
MiniMax Mavis Supported
Ollama Supported
OpenClaw Supported
Tabbit Browser Supported
Together AI Supported
Vision Agents Supported

Integrations

Alibaba AI Coding Plan Not Supported
Claude Code Not Supported
Claude Fable 5.1 Supported
Claude Fable 5.5 Supported
Claude Mythos 5.1 Supported
Claude Opus 4.1 Supported
Claude Opus 4.5 Supported
Clawd.run Not Supported
Cline Not Supported
ClinePass Not Supported
GPT-5 mini Supported
Gemini 2.0 Flash Supported
MaxHermes Not Supported
MiniMax Agent Not Supported
MiniMax Mavis Not Supported
Ollama Not Supported
OpenClaw 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 Twigg and update features and information
Claim Twigg and update features and information