Cisco’s Tiny Open-Weight AI Hunts Bugs, and It Says It Beats Gemini and GPT
The networking giant’s Antares models run on your own machines and, it claims, localise vulnerabilities faster and cheaper than frontier systems. It is gating access, and withholding its strongest model, because a bug-finder is also a bug-exploiter.
July 22, 2026 – 5:00 pm
Image by: Cisco
Cisco’s new Antares models are small enough to run on your own machines. And, it claims, they beat Google’s Gemini and rival GPT-class systems at finding bugs. It is releasing them open-weight, though it is vetting who gets access.
The Pitch from Cisco
A quiet rebuke to the frontier-model arms race. You do not need a giant, pricey AI to hunt for software bugs. And you should not have to ship your source code to OpenAI or Google to do it.
Cisco’s Foundation AI team has released two open-weight security models, Antares-350M and Antares-1B, built to find known vulnerabilities buried in existing codebases. They sit on Hugging Face for the community, though Cisco is approving access case by case. It is holding a larger 3-billion-parameter model back from a full public release.
What Are These Models?
These are not chatbots. Antares works a repository like a human investigator. It starts from a vulnerability description, searches the code, and reads candidate files. It backtracks when a path leads nowhere, then returns a ranked list of the files most likely to hide a flaw. The name comes from the red supergiant star, roughly 1,000 times the size of the sun.
Small Yet Mighty
Cisco says the little models punch far above their weight. On its own 500-task benchmark for vulnerability localisation, Antares-1B beats Google’s Gemini 3 Pro and matches Z.ai’s GLM-5.2. The unreleased 3B model, it claims, tops both GLM-5.2 and OpenAI’s GPT-5.5.
The real advantage shows in speed and cost. Chief AI scientist Amin Karbasi told The Register that Antares clears 500 code repositories in 15 minutes, against five hours for frontier models. That works out at less than $1, he said, versus $100 to $150 for the big systems.
Local Running
Running locally is the other selling point. Being small and open-weight, the models keep proprietary code on a company’s own machines. Cloud-based systems, by contrast, send that code to an AI provider’s servers for scanning. For firms in regulated industries, keeping it in-house is the whole point.
The Catch
There is a reason Cisco is holding back. A tool that finds flaws is also one that helps attackers exploit them. As Reza Shokri, a computer scientist at the National University of Singapore, put it, AI agents now “write more of the code, and are growing capable of exploiting it.”
So Cisco gates access to academic, nonprofit, and smaller public-sector security teams, and keeps its strongest model back. The aim, it says, is to stop advanced AI defense becoming a luxury good.