Token Compression, achieving more with less

edgee.ai

8 pointssachamorard3 comments

From a theoretical perspective, token compression is about removing low-utility tokens while retaining enough structure to trigger the desired model behavior. Prompt compression and embedding compression each operate along a precision–efficiency spectrum, and the balance of semantic retention vs. token reduction is what makes it effective. Because API billing scales with tokens processed, this balance is directly tied to cost optimization

That's exactly the trade-off we're pointing at.

One nuance we've been seeing in practice is that the "utility" of a token isn't purely semantic: some tokens carry behavioral constraints (negations, numeric bounds, formatting rules, safety instructions) and their removal can cause discrete failures rather than smooth degradation.

And yes, since cost scales linearly with input tokens, reducing prompt size (I mean context size) can improve both spend and latency.

very interesting

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