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Description

Ling 2.6 represents an independently developed and open-source series of large language models created by Ant Group, utilizing a Mixture of Experts (MoE) architecture to enhance inference efficiency, long context modeling, training methodologies, and collaborative reasoning for AI agents. By employing this MoE architecture, Ling effectively directs each token to engage only the most pertinent expert subnetworks, significantly reducing the computational load while preserving the extensive capabilities of the model. This series makes strides in long-sequence modeling, exemplified by Ling-2.6-1T, which accommodates a native context window of up to 1 million tokens and offers a 256K context window through its official API; additionally, Ling-2.6-flash features a native 256K context window, enabling it to handle around 200,000 characters in lengthy inputs. These models are meticulously crafted to ensure dependable retrieval of long-range information without any discernible loss of quality, regardless of whether the data is located at the start, middle, or end of the context. This innovative approach to long-context processing sets a new benchmark for efficiency and reliability in language model performance.

Description

The MiniMax‑M1 model, introduced by MiniMax AI and licensed under Apache 2.0, represents a significant advancement in hybrid-attention reasoning architecture. With an extraordinary capacity for handling a 1 million-token context window and generating outputs of up to 80,000 tokens, it facilitates in-depth analysis of lengthy texts. Utilizing a cutting-edge CISPO algorithm, MiniMax‑M1 was trained through extensive reinforcement learning, achieving completion on 512 H800 GPUs in approximately three weeks. This model sets a new benchmark in performance across various domains, including mathematics, programming, software development, tool utilization, and understanding of long contexts, either matching or surpassing the capabilities of leading models in the field. Additionally, users can choose between two distinct variants of the model, each with a thinking budget of either 40K or 80K, and access the model's weights and deployment instructions on platforms like GitHub and Hugging Face. Such features make MiniMax‑M1 a versatile tool for developers and researchers alike.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Anuma
Claude Code
GitHub
Hermes Agent
Hugging Face
Kilo Code
OpenAI
OpenClaw
OpenRouter
SiliconFlow

Integrations

Anuma
Claude Code
GitHub
Hermes Agent
Hugging Face
Kilo Code
OpenAI
OpenClaw
OpenRouter
SiliconFlow

Pricing Details

$0.0028 per 1M tokens
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Ant Group

Founded

2014

Country

China

Website

developer.ant-ling.com/en/docs/models/ling/

Vendor Details

Company Name

MiniMax

Founded

2021

Country

Singapore

Website

www.minimax.io/news/minimaxm1

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