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Description

Claude Mythos 5.1 represents Anthropic's latest advancement in the Mythos-class of models, tailored for sophisticated applications in cybersecurity, biology, scientific investigation, programming, and extensive knowledge-oriented tasks. While it shares the same foundational architecture as Claude Fable 5.1, it is differentiated by unique safety measures: Fable 5.1 is widely accessible, whereas Mythos 5.1 is limited to select trusted access programs designed with specific safeguards for cybersecurity and life sciences research. This model establishes a new benchmark in performance for autonomous coding and showcases unparalleled cyber capabilities among all Anthropic models released thus far. In the realm of scientific research, Mythos 5.1 can effectively handle specialized tools and intricate workflows related to molecular design, computational biology, and various technical fields. During testing by Anthropic, it successfully engineered high-affinity protein binders for multiple targets, achieving its highest recorded hit rate to date. Additionally, it demonstrated proficiency in optimizing seven different open-source deep learning models focused on protein and genomics. By pushing the boundaries of what is possible, Mythos 5.1 is positioned to make significant contributions to future research and development endeavors.

Description

The Ling 2.6 Flash represents the newest and most economical addition to the Ling series, utilizing a Mixture of Experts architecture that encompasses a total of 104 billion parameters, with 7.4 billion of those being actively engaged. This model is crafted to strike an ideal balance between inference speed and computational expense, making it an excellent fit for diverse scenarios where reasoning prowess, high throughput, and effective deployment are essential. By employing its MoE structure, Ling ensures that each token activates only the most pertinent expert subnetworks, significantly reducing the actual computational load while preserving the expansive capacity of the model. Offering a native context window of 256K, Ling 2.6 Flash is capable of handling around 200,000 characters of lengthy input, adeptly retrieving critical long-range information regardless of its position in the context. Furthermore, its overall benchmark performance rivals or surpasses that of 40 billion parameter Dense models, highlighting its competitive edge in the field of AI. This blend of efficiency and performance makes Ling 2.6 Flash a noteworthy option for developers seeking advanced capabilities without excessive resource demands.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Claude Code Yes 
Hermes Agent Yes 
OpenClaw Yes 
Augment Code Yes 
Claw Code Yes 
Cody Yes 
GitHub Yes 
Google Antigravity Yes 
Kilo Code No 
LaunchLemonade Yes 
Microsoft Foundry Yes 
Muses Yes 
Perplexity Computer Yes 
PowerShell Yes 
PyCharm Yes 
Ruby Yes 
Snowflake Yes 
Solidity Yes 
Swift Yes 
Trinity-Large-Thinking Yes 

Integrations

Claude Code Yes 
Hermes Agent Yes 
OpenClaw Yes 
Augment Code No 
Claw Code No 
Cody No 
GitHub No 
Google Antigravity No 
Kilo Code Yes 
LaunchLemonade No 
Microsoft Foundry No 
Muses No 
Perplexity Computer No 
PowerShell No 
PyCharm No 
Ruby No 
Snowflake No 
Solidity No 
Swift No 
Trinity-Large-Thinking No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$0.00037 per 1M tokens
Free Trial No 
Free Version 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 

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

Anthropic

Founded

2021

Country

United States

Website

claude.ai

Vendor Details

Company Name

Ant Group

Founded

2014

Country

China

Website

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

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