Károly Zsolnai-Fehér presents Kimi K3 as a large open-weights model that approaches frontier coding capability while remaining downloadable and available through a lower-cost API. The model has 2.8 trillion parameters, so most people cannot run the full version locally, but its pricing and future distilled variants could still affect the wider market.
Károly Zsolnai-Fehér highlights two technical changes. Kimi Delta Attention maintains a compact, gradually updated memory instead of repeatedly processing all prior context, while attention residuals preserve useful intermediate representations across model layers.
The developers report about 2.5 times more scaling efficiency than Kimi K2, meaning greater learning progress from the same training computation rather than a direct 2.5 times speed or cost improvement. Károly Zsolnai-Fehér concludes that open weights, published techniques and lower inference prices can spread improvements across other open models while increasing price pressure on closed providers.
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