@ChrisGPT: Correct. People often say open models are six months behind the frontier. But “open” is too broad, - a model that needs…
Summary
The article discusses the pattern where open-source AI models like Qwen match frontier capabilities about two quarters later, exemplified by GPT-5 and Qwen3.5-27B, and questions if this trend will continue with models like Astra and Fable 5.1.
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Cached at: 09/22/26, 07:47 AM
Correct. People often say open models are six months behind the frontier. But “open” is too broad, - a model that needs a cluster is downloadable, not but not local to the everyday person.
I want a scaling law for the best model that fits on one 24GB GPU.
On Artificial Analysis’ current index, GPT-5 scored 23 in August 2025. Qwen3.5-27B reached 23 roughly 6.6 months later. GPT-5.3 Codex scored 33 in February 2026; Qwen3.8-27B reached 34 about 6.3 months later.
A composite score is not task by task equivalence of course. But the pattern has now repeated across two Qwen generations.
im seeing people post about ‘Qwen Law’ - basically matching frontier capabilities about two quarters later. Now will this hold for Astra and Fable 5.1? Thats yet to be seen.
Ahmad (@TheAhmadOsman): Just a reminder that GLM 5.3 Flash, DeepSeek V4.1 Flash, Qwen 3.8 Next Flash, and even Qwen 3.8 27B are all outperforming (in both intelligence and capabilities) every model that was considered “frontier intelligence” in Xmas 2025 (just 10 months ago)
Opensource AI is on fire
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