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Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
New research reveals that low-bit post-training quantization causes reasoning models to generate longer chains of thought even when answers remain correct, adding hidden inference costs.
Quantization is widely used to reduce the inference cost of large language models, but its effect on reasoning models is not fully captured by final-answer accuracy or per-token latency. A paper posted on arXiv on June 25 uncovers a hidden cost: low-bit post-training quantization can cause quantized reasoning models to generate substantially longer chains of thought, even when they still answer correctly. This phenomenon was observed across mathematical reasoning, code generation, and scientific question answering, meaning users may pay hidden extra compute costs for correct but inefficient reasoning. The paper is listed under arXiv ID 2606.25519 in the cs.AI category and introduces a new consideration for quantization decisions in model deployment.
Why it matters
This finding challenges the common assumption that quantization only affects accuracy and latency, alerting the industry to hidden token inflation costs when quantizing reasoning models.