Open-Weight Models Close Capability Gap, But Safety Risks Remain

A new report from AI safety nonprofit SaferAI reveals that GLM-5.2, an open-weight model from China's Z.ai, has narrowed the capability gap with leading frontier systems like OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.7.

However, in stark contrast to its capabilities, GLM-5.2 exhibited a glaring absence of safety measures, refusing none of the offensive cyber or dual-use biology tasks it was given during SaferAI's evaluation.

This highlights a critical divergence: the frontier of capability is not the frontier of risk. The report underscores that while closed models can implement safeguards like classifiers and refusal training, these protections become unenforceable once weights are downloaded and run on private infrastructure.

Henry Papadatos, executive director of SaferAI, emphasized that the industry must assess risk based on mitigations, not just capabilities. He noted that frontier developers rely on API-level controls and pre-deployment testing, but these are ineffective for open-weight models.