Cantina Security and Yeta Labs released apex-flash-1 on October 4: a 321-billion-parameter open-weights model fine-tuned for vulnerability research, MIT-licensed on Hugging Face.
AspireAI community · huggingface.co
Cantina Security and Yeta Labs released apex-flash-1 on October 4: a 321-billion-parameter open-weights model fine-tuned for vulnerability research, MIT-licensed on Hugging Face.
It is a reinforcement learning fine-tune of Z.ai's GLM-5.3-Flash, trained on production-like software and protocol environments with the Codex agent harness. The pitch is cheap security work: on Cantina's own 60 held-out bug tasks from 20 cases, apex-flash-1 solved 40 of 60 (66.7%) at an estimated $2.38 per 60-task sweep. Claude Opus 5 High solved 43, but cost $74.68 on the same set.
An "abliterated" variant with refusals stripped was released alongside.
The honest limit: the benchmark is Cantina's own. BF16 needs ~640 GB of GPU memory, so most readers will wait for a quantized build.
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