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Description

MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.

Description

Qwen3.8-Flash-Next represents an open-weight multimodal Mixture-of-Experts architecture and serves as an initial glimpse into the design intended for Qwen4. This model strategically enhances attention mechanisms, residual pathways, embeddings, and optimization techniques to boost its capabilities, improve computational efficiency, expand model capacity, and ensure training stability. Its innovative hybrid architecture merges Gated DeltaNet, which adeptly compresses past information, with Qwen Sparse Attention, enabling the selection of significant context at a micro-block level to lessen both attention and indexing costs associated with lengthy sequences. The Gated Residual feature broadens the residual pathway into four streams, dynamically managing the flow of information across different layers. Additionally, the N-gram Embedding integrates large-scale local-pattern memory with minimal added computation per token, and it can be transferred to host memory for further efficiency. The model is structured around a 125B-parameter main network supplemented by 51B parameters dedicated to N-gram embeddings, activating only 6B parameters for each token processed. This sophisticated framework highlights the ongoing advancements in machine learning architectures, setting a promising stage for future developments.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Cline Yes 
ClinePass Yes 
Hermes Agent Yes 
Ollama Yes 
OpenClaw Yes 
APIFree Yes 
Alibaba AI Coding Plan Yes 
Clawd.run Yes 
Factory Droid Yes 
Happy Shrimp 1.0 No 
Hugging Face No 
MiniMax Agent Yes 
MiniMax Code Yes 
Model Context Protocol (MCP) No 
Novita AI No 
Odysseus No 
OfoxAI No 
OpenCode Go Yes 
Qwen Code No 
Vision Agents Yes 

Integrations

Cline Yes 
ClinePass Yes 
Hermes Agent Yes 
Ollama Yes 
OpenClaw Yes 
APIFree No 
Alibaba AI Coding Plan No 
Clawd.run No 
Factory Droid No 
Happy Shrimp 1.0 Yes 
Hugging Face Yes 
MiniMax Agent No 
MiniMax Code No 
Model Context Protocol (MCP) Yes 
Novita AI Yes 
Odysseus Yes 
OfoxAI Yes 
OpenCode Go No 
Qwen Code Yes 
Vision Agents No 

Pricing Details

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Trial No 
Free Version Yes 

Pricing Details

$2 per 1M (input)
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

MiniMax

Founded

2021

Country

Singapore

Website

www.minimax.io

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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