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[ARTICLE · art-40267] src=pub.towardsai.net ↗ pub= topic=large-language-models verified=true sentiment=↑ positive

I Shrank a 428B Model From 855GB to 128GB and It Still Beats GPT-5.5 at Coding

A 428-billion-parameter open-weight model achieved 59.0% on SWE-Bench Pro, outperforming GPT-5.5's 58.6%, after being compressed from 855GB to 128GB. The model's efficiency and performance mark a significant advance in AI model optimization.

read1 min views1 publishedJun 26, 2026

A 428-billion-parameter open-weight model scores 59.0% on SWE-Bench Pro, edging out GPT-5.5’s 58.6% — and the community just finished… Continue reading on Towards AI »

source & further reading
pub.towardsai.net — original article Building Production-Ready Agentic AI Systems with Docker and FastAPI Why Every Weight in a Neural Network Is Born Divided by the Square Root of n. Stop Letting LLMs Hallucinate Your Codebase: A Graph-First Way to Summarize Repos
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