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Average Ratings 0 Ratings
Description
Antares represents a suite of open-weight security small language models specifically designed to identify existing vulnerabilities within extensive codebases. With models like Antares-350M and Antares-1B, organizations can operate them locally or on-site, allowing for the protection of proprietary source code while also minimizing both inference costs and runtime. The process begins with a description of the vulnerability, an advisory, or a CWE category, where the model engages in a step-by-step investigation akin to that of a human analyst, systematically searching for pertinent code patterns, examining potential files, assimilating new information, and altering its approach when certain avenues prove unfruitful. This strategy enables the model to focus its efforts on the files that are most likely to harbor the identified weaknesses. Ultimately, Antares generates a prioritized list of potentially vulnerable source files along with the detailed exploration trail that led to these findings, facilitating easier review and prioritization for teams. Moreover, this capability not only streamlines the vulnerability assessment process but also enhances the overall security posture of the development environment.
Description
Qwen3-Coder-Next is a language model with open weights, crafted for coding agents and local development, which excels in advanced coding reasoning, adept tool usage, and effective handling of long-term programming challenges with remarkable efficiency, utilizing a mixture-of-experts framework that harmonizes robust capabilities with a resource-efficient approach. This model enhances the coding prowess of software developers, AI system architects, and automated coding processes, allowing them to generate, debug, and comprehend code with a profound contextual grasp while adeptly recovering from execution errors, rendering it ideal for autonomous coding agents and applications focused on development. Furthermore, Qwen3-Coder-Next achieves impressive performance on par with larger parameter models, but does so while consuming fewer active parameters, thus facilitating economical deployment for intricate and evolving programming tasks in both research and production settings, ultimately contributing to a more streamlined development process.
API Access
Has API
No
API Access
Has API
Yes
Integrations
OpenClaw
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
Yes
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
Yes
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
Yes
iPad App
Yes
Android App
Yes
Windows
Yes
Mac
Yes
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
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)
Yes
In Person
No
Vendor Details
Company Name
Cisco
Founded
1984
Country
United States
Website
blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization
Vendor Details
Company Name
Alibaba
Founded
1999
Country
China
Website
qwen.ai/blog