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 is 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.

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

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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.

The trick is that Cisco trained the models to search, not to chat. Because each one is tiny and nimble, it can run many searches at once and switch tactics when a line of attack goes nowhere. Karbasi likened it to a bicycle weaving past a truck on a busy London street.

Keeping the code at home

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 defence becoming a luxury good, just as attackers start wielding AI of their own. Cisco’s bet is that the edge is no longer the size of the model. It is who gets to aim it.