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What is MiniMax M2.7?

MiniMax M2.7 is a cutting-edge AI model engineered to deliver high-performance productivity across coding, search, and professional office workflows. It is trained using reinforcement learning across extensive real-world environments, allowing it to handle complex, multi-step tasks with accuracy and adaptability. The model excels at structured problem-solving, breaking down challenges into logical steps before generating solutions across a wide range of programming languages. It offers high-speed processing with rapid token generation, enabling faster execution of tasks and improved workflow efficiency. Its optimized reasoning reduces unnecessary token usage, improving both performance and cost efficiency compared to earlier models. M2.7 achieves state-of-the-art results in software engineering benchmarks, demonstrating strong capabilities in debugging, development, and incident resolution. It also significantly reduces intervention time during system issues, improving operational reliability. The model is equipped with advanced agentic capabilities, enabling it to collaborate with tools and execute complex workflows with high precision. It supports multi-agent environments and maintains strong adherence to complex task requirements. Additionally, it excels in professional knowledge tasks, including high-quality office document editing and multi-turn interactions. Its ability to handle structured business workflows makes it suitable for enterprise use cases. With its balance of speed, intelligence, and affordability, it stands out among frontier AI models. Overall, MiniMax M2.7 provides a scalable and efficient solution for modern AI-driven productivity and automation.

What is Ling 2.6 Flash?

The Ling 2.6 Flash is the latest and most cost-effective member of the Ling series, featuring a Mixture of Experts architecture that boasts 104 billion parameters, with 7.4 billion of these actively utilized. Designed to achieve an optimal balance between inference speed and resource costs, this model excels in various applications that require robust reasoning, high throughput, and efficient deployment. Its MoE framework allows the model to engage only the most relevant expert subnetworks for each token, thereby significantly lowering the computational burden while still leveraging the model's extensive capacity. With a native context window of 256K, Ling 2.6 Flash can process approximately 200,000 characters of lengthy input, effectively retrieving essential long-range information no matter where it appears in the context. Additionally, its benchmark performance competes with or even surpasses that of dense models with 40 billion parameters, showcasing its strong position within the AI landscape. This combination of efficiency and high performance positions the Ling 2.6 Flash as a compelling choice for developers who desire sophisticated capabilities without placing undue strain on their resources. As technology continues to evolve, the Ling 2.6 Flash stands out as a prime candidate for future innovations in artificial intelligence.

Media

Media

Integrations Supported

Claude Code
Hermes Agent
Kilo Code
OpenClaw
APIFree
Alibaba AI Coding Plan
BLACKBOX AI
Clawd.run
Cline
Factory Droid
Fireworks AI
MiniMax Agent
Ollama
OpenRouter
Pi Agent
Roo Code
Tabbit Browser
Together AI
ZenMux

Integrations Supported

Claude Code
Hermes Agent
Kilo Code
OpenClaw
APIFree
Alibaba AI Coding Plan
BLACKBOX AI
Clawd.run
Cline
Factory Droid
Fireworks AI
MiniMax Agent
Ollama
OpenRouter
Pi Agent
Roo Code
Tabbit Browser
Together AI
ZenMux

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

$0.00037 per 1M tokens
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

MiniMax

Date Founded

2021

Company Location

Singapore

Company Website

www.minimax.io

Company Facts

Organization Name

Ant Group

Date Founded

2014

Company Location

China

Company Website

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

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