Antares-1B
Efficient open-weight small language model for finding known vulnerabilities in codebases
Context window
Not announced
Input / 1M tokens
Free
Output / 1M tokens
Free
Provider
Cisco
Data verified 2026-07-23
Antares-1B is a specialized security small language model (SLM) designed to pinpoint where known vulnerabilities exist within codebases. Built by Cisco Foundation AI, it specializes in vulnerability localization—connecting external vulnerability knowledge to internal source code—and runs locally without requiring source code to be sent to external providers. The model significantly outperforms larger closed- and open-weight models on vulnerability detection benchmarks while running at a fraction of the cost.
Capability index
Relative estimates (0-100) to place this model against its peers, grounded in published benchmarks.
How to access it
Download open-weight model from Hugging Face. Cisco vets access on a case-by-case basis. Models run locally on-premises.
Strengths
- ✓172x cheaper than GPT-5.5 on vulnerability localization benchmark
- ✓15.2x cheaper than GLM-5.2 open-weight model
- ✓Faster execution: 15 minutes for 500-task benchmark vs 5 hours for frontier models
- ✓Runs locally, maintaining data sovereignty
- ✓Outperforms Gemini 3 Pro on vulnerability localization
- ✓Specialized focus reduces false positives
Best for developers who...
When to choose it (and when not to)
Reach for Antares-1B when...
- →When cost efficiency is critical for repeated vulnerability scanning
- →When source code must remain on-premises or private
- →For initial triage phase of vulnerability investigation
- →For organizations with constrained security budgets
Look elsewhere if...
- ✕Antares is not a replacement for full application security stack
- ✕Should not replace dependency analysis, SAST, or human review
- ✕Not designed as a general-purpose coding assistant
- ✕Does not generate code or answer programming questions
How to use it
- ›Provide CWE (Common Weakness Enumeration) identifiers or vulnerability descriptions
- ›Use in CI/CD workflows triggered by security advisories or dependency alerts
- ›Leverage ranked file list output as starting point for security analyst triage
Quickstart
Pythonfrom transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'Cisco/antares-1b'
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)Access requires Hugging Face verification from Cisco. Model runs locally with standard transformer inference.
API model id: antares-1b
Benchmarks
| Benchmark | Score | Notes |
|---|---|---|
| Vulnerability Localization Benchmark (VLoc Bench) | Beats Gemini 3 Pro; matches GLM-5.2 | 500-task dataset evaluating vulnerability localization. Antares-1B demonstrates near-frontier accuracy at fraction of cost. |
| Cost efficiency | $0.71 per 500-task benchmark run | Compared to $12.50 for GLM-5.2 and $141 for GPT-5.5 |
| Speed | 15 minutes on single Nvidia H100 GPU | vs 5 hours for GPT-5.5 on same 500-repository evaluation |
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