[HN]score: 0.57

GLM-5.3 Open-Weights Outperforms Anthropic and OpenAI on Real-World Tasks

August 23, 2026

GLM-5.3 achieves higher pass rates on a 28-task real-world benchmark compared to leading proprietary models while maintaining 80% lower costs per task. Evaluation focused on single-task execution as a proxy for agentic unit tests, measuring pass rate, rubric quality, and time to first token.

HOW THIS AFFECTS YOU

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builderYou can replace expensive proprietary APIs with this open-weight model for agentic workflows without sacrificing task success rates.

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founderThis provides a pathway to significantly higher margins on AI-agent products by reducing inference costs by 5x.

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investorOpen-weight models are increasingly challenging the pricing power and performance moats of closed-source incumbents.

read original ↗reinvently.co.uk

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