AntaresCisco
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LFM3Liquid AI
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Related Products
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About
Antares is a family of open-weight security small language models purpose-built to localize known vulnerabilities inside large codebases. Antares-350M and Antares-1B are compact enough to run locally or on premises, helping teams keep proprietary source code inside their environment while reducing inference cost and runtime. Starting from a vulnerability description, advisory, or CWE category, the model follows an iterative investigation process similar to a human analyst, it searches for relevant code patterns, reads candidate files, incorporates new evidence, changes direction when a path is unproductive, and narrows the search to the files most likely to contain the weakness. Antares returns a ranked list of potentially vulnerable source files together with the terminal exploration trace that produced the result, making findings easier to review and prioritize.
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About
LFM3 is Liquid AI’s family of compact, open-weight foundation models for text, vision, audio, and multimodal tasks. The release includes text-generation, vision-language, audio, omni, and decision-model variants. Developers can download the model weights, adapt supported models through fine-tuning, and run them locally on edge hardware or access them through Liquid AI’s hosted API. Use cases include conversational assistants, tool calling, speech transcription and generation, image and video understanding, document extraction, classification, routing, and robotics applications. LFM3 is intended for developers, researchers, and organizations building specialized AI applications that need flexible deployment choices, including local inference. Open-weight models are available under Liquid AI’s LFM Open License; commercial eligibility and terms depend on an organization’s annual revenue.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Security researchers and developers that need efficient open-weight AI models to locate known software vulnerabilities within codebases
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Audience
Developers, AI researchers, and organizations building specialized AI applications, including applications using local or edge inference.
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Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Pricing
$0.08/MTok
LFM3-1.7B API usage is $0.08 per million input tokens, $0.02 per million cached input tokens, and $0.20 per million output tokens. New organizations receive $2 in API credit. Open-weight models can be used in production free by developers, researchers, nonprofits, and businesses with less than $10 million in annual revenue; businesses above that threshold require a paid license.
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Company InformationCisco
Founded: 1984
United States
blogs.cisco.com/ai/introducing-antares-the-most-efficient-open-weight-ai-models-for-vulnerability-localization
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Company InformationLiquid AI
Founded: 2023
United States
www.liquid.ai/blog/lfm3
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Alternatives |
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Categories |
Categories |
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Integrations
Hugging Face
Not Supported
Hugging Face Transformers
Not Supported
LM Studio
Not Supported
Ollama
Not Supported
OpenRouter
Not Supported
Unsloth
Not Supported
Vercel AI Gateway
Not Supported
vLLM
Not Supported
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Integrations
Hugging Face
Supported
Hugging Face Transformers
Supported
LM Studio
Supported
Ollama
Supported
OpenRouter
Supported
Unsloth
Supported
Vercel AI Gateway
Supported
vLLM
Supported
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