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Can you take the cheapest open model from a frontier lab & beat closed models on the hardest long range tasks? Yep Our open adaptive intelligence system Zenith ramps DeepSeek v4.1 Flash past GPT 5.6 Sol to the frontier 🚀 h/t Opus 5.5 for making the video in like... show more
30,687 Aufrufe • vor 3 Tagen •via X (Twitter)
9 Kommentare

The @bespokelabsai blog post on Autoresearch exam is worth reading, shows minimal difference in performance with standard harnesses Much more to come, reliable frontier is within reach for all

@01Singularity01 Wow so brilliant and thank you for the credits and bonus credits

the 1/49th cost part is what gets me, open models catching up on long tasks changes a lot

Zenith likely uses sparse attention to cut the noise. Fewer tokens processed means less memory pressure on those deep context windows. Smart work shaving off compute waste while keeping track of every detail. Efficient is sexy. 🤖

Interesting, how did you train it?

Interesting. Systems > Models for generating Intelligence. Orchestration is effectively creating a system of intelligence rather than relying on a single Model node to carry the entire workload.

open models catching up fast, love to see it

True leverage shifts from raw training compute to adaptive systems orchestration. The economic compression of reasoning is where open models truly win.

how do you handle KV cache thrashing during high-depth state orchestration?




