Comment by VulgarExigency
8 days ago
The model that is most optimized around token cost is, in fact, Chinese. DeepSeek is astoundingly cheap by default, but if you use it from Reasonix (the harness optimized around its cache), it becomes even cheaper.
I keep seeing mention of the cache, what's special about it? All frontier llms have prefix caching, what is special about deepseek's approach?
Their kv cache is smaller so they can use less vram and also keep your prefix cached for longer. https://deepseek.ai/blog/deepseek-v4-compressed-attention
This also comes with significant capability reduction. deepseek-v4-flash is very good in the < 250K range, then degrades between 250-500K, and is practically unusable after 500K.
[edit]
This is my observation from using it without an specific context engineering to optimize for Deepseek's cache compression and sparse attention mechanisms. I am pretty sure that if you specifically structure your context to align to the cache compression boundaries you can significantly improve performance in the full 1M context, but there is not much reason to do this, because if you design your outer loop to work with shorter contexts that solution is portable and more efficient, so I haven't bothered with a optimizing for DS at this point.
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