Károly Zsolnai-Fehér examines a major DeepSeek V4 Flash update released only months after the original model. Several evaluation results more than doubled, and the revised Flash model sometimes surpasses the Pro version despite being roughly one-fifth its size.
The base architecture, parameter count and underlying learned knowledge remain unchanged. The improvement comes from post-training that teaches the model how to select abilities, plan sequences of work, check intermediate results and recover from mistakes.
Károly Zsolnai-Fehér describes the distinction as giving the same capable builder a better playbook. The model already has the required information, but improved strategy helps it assemble solutions in a more reliable order, test components earlier and correct errors before they compound.
Because the weights are available, users can keep and run the model without subscription limits, although local deployment still requires substantial hardware. Károly Zsolnai-Fehér treats the result as evidence that post-training can rapidly compress frontier-like behavior into smaller, cheaper open systems.
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