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

Muse Spark 1.3 is an AI model with improved performance across agentic and coding tasks, designed to be smarter and more practical for real-world work. It sustains longer-horizon tasks by collaborating with users and managing multiple workflows in a single, long thread. Given an open-ended objective, it uses tools to build context across messy or conflicting sources, correct gaps in its plan, track what it has learned, and produce a final deliverable. It asks clarifying questions when prompts are ambiguous, requests help when stuck, and confirms before taking consequential actions. The model follows complex, long-form instructions more reliably, preserving detailed requirements across multi-step tasks without dropping constraints or drifting from the requested workflow. Improved multitasking allows it to map incoming prompts to the correct task even when users interrupt or redirect previous requests.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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 professional users seeking an AI model for complex agentic workflows, long-running tasks, coding, and multi-step work requiring reliable instruction following

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

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
Free Trial

Pricing

$1.25 per 1M tokens (input)
Free Version
Free Trial

Reviews/Ratings

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

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 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.

Pros & Cons from Real Users

Pros

  • This feels like the version where Muse Spark becomes much more interesting for developers. The focus is not just “better chat,” but better coding, better agentic workflows, and more reliable long-running tasks. The biggest win is efficiency. Meta says Muse Spark 1.3 uses fewer tokens and fewer tool calls on coding tasks, which matters a lot if you are running agents repeatedly instead of asking one-off questions. I also like that it is available in both Muse Code and the Meta Model API. That gives developers a practical path whether they want a coding-agent experience or want to plug the model into their own workflows.

Cons

  • Have not found any downsides thus far. This model is a big leap for Meta

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

MiniMax
Founded: 2021
Singapore
www.minimax.io

Company Information

Meta
Founded: 2004
United States
meta.ai/

Alternatives

Alternatives

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GPT-5.6 Sol

OpenAI
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
MiniMax

MiniMax

MiniMax AI
MiniMax M1

MiniMax M1

MiniMax

Categories

Categories

Integrations

Claude Code
Hermes Agent
OpenClaw
BLACKBOX AI
Bash
Codex CLI
Fireworks AI
Instagram
Kilo Code
Meta AI
MiniMax Code
Muse Video
Objective-C
OpenAI Agents SDK
OpenAI Codex
OpenTag
Python
Scala
Tabbit Browser
Vercel AI SDK

Integrations

Claude Code
Hermes Agent
OpenClaw
BLACKBOX AI
Bash
Codex CLI
Fireworks AI
Instagram
Kilo Code
Meta AI
MiniMax Code
Muse Video
Objective-C
OpenAI Agents SDK
OpenAI Codex
OpenTag
Python
Scala
Tabbit Browser
Vercel AI SDK
Claim MiniMax M3 and update features and information
Claim MiniMax M3 and update features and information
Claim Muse Spark 1.3 and update features and information
Claim Muse Spark 1.3 and update features and information