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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
Ling 3.0 Tiny is a reasoning model featuring open weights, comprising 7.9 billion total parameters and 1.3 billion active parameters, alongside a substantial context window of 262,000 tokens. Leveraging a mixture-of-experts architecture, it pushes the boundaries of the open-weights Pareto frontier in terms of intelligence relative to active parameters, while being compact enough for local deployment in various environments. Scoring 25 on the Artificial Analysis Intelligence Index, it stands on par with gpt-oss-120b, which scores 24, despite utilizing 15 times fewer total parameters and 4 times fewer active parameters. This impressive parameter efficiency does come with a trade-off, as it requires a significant 213 million output tokens to complete the Intelligence Index evaluation. In addition, Ling 3.0 Tiny exhibits noteworthy advancements in reducing hallucination tendencies compared to Ling-mini-2.0; it enhances its AA-Omniscience score by 59 points while keeping accuracy levels consistent. Notably, rather than making random guesses in uncertain situations, the model chose to attempt only 37% of the questions during evaluation, leading to a markedly reduced hallucination rate of 30%, a significant improvement over the previous generation's 96%. This strategic approach not only demonstrates the model's improved reasoning capabilities but also highlights its potential for more reliable real-world applications.
API Access
Has API
No
API Access
Has API
No
Integrations
Claude Code
No
Hermes Agent
No
Kilo Code
No
OpenClaw
No
OpenRouter
No
ZenMux
No
Integrations
Claude Code
Yes
Hermes Agent
Yes
Kilo Code
Yes
OpenClaw
Yes
OpenRouter
Yes
ZenMux
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
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
No
iPad App
No
Android App
No
Windows
No
Mac
No
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)
No
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
Ant Group
Founded
2014
Country
China
Website
ant-ling.com