Whilst I agree with the premise, I think you are actually underselling them.

Claude becomes near lobotomized at beyond 500,000 tokens. I don't believe much quality code gets outputted at such high token counts, not to mentioned drastically increased cost.

270k isn't massive, but its very usable with compaction. Not every task needs the full context history.

Quantized models do have a quality / accuracy impact, although it is not as drastic as you suggest. There is some good data on this [0].

"These findings confirm that quantization offers large benefits in terms of cost, energy, and performance without sacrificing the integrity of the models. "

One thing that is worth mentioning is quant models are not created equally, they are not always scaling at the same rate. [1] For example not all tensors contribute equally to model accuracy. In practice, the most sensitive parts (such as key attention projections) are often quantized less aggressively to preserve the quality of the inference.

[0] - https://developers.redhat.com/articles/2024/10/17/we-ran-ove...

[1]- https://medium.com/@paul.ilvez/demystifying-llm-quantization...