Antares-1B
Efficient open-weight small language model for finding known vulnerabilities in codebases
Context window
128000
Input / 1M tokens
Free
Output / 1M tokens
Free
Provider
Cisco
Data verified 2026-07-26
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
- ›Use 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: fdtn-ai/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 |
| VLoc Bench (Vulnerability Localization) | 0.209 | File F1 score; highest recall at 0.224; outperforms GLM-5.2 (753B params) at 0.186 |
Compare Antares-1B
Compare Antares-1B with any other model
Build a comparison →All model comparisons →