Unverified60% confidenceFactExact time
Output tokens are typically more expensive than input tokens because outputs require autoregressive token-by-token generation, which has higher computational intensity and lower GPU utilization.
2
Sources
60%
Confidence
Long-term
Relevance
8/6/2026
First Seen
Sources
Kimi K3为何被催更？一条Reddit吐槽揭示国产大模型竞争真相
Related Claims
UnverifiedThe price of output tokens is typically 4–6 times that of input tokens, stemming from the computational differences between the prefill and decode phases, with GPU utilization during the decode phase potentially being only 10–20% of that during prefill72% similarUnverifiedThe marginal cost of AI models decreases as scale expands; the more users there are, the lower the average cost per inference after optimization through batching and GPU utilization.71% similarUnverifiedGPU utilization can rise from 20%–30% during low traffic to 70%–80% during high traffic, because larger request volumes make it easier to aggregate into larger compute batches71% similarUnverifiedThe distributed output strategy (multiple models each responsible for one stage) has the highest quality compared to direct output and step-by-step output, but is the slowest and most costly.69% similarUnverified每个中间推理token与输出token消耗等量的GPU算力，Extra High档位单次请求FLOPs可能是普通生成模式的数十倍69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/699232API
curl https://kongchang.com/api/v1/knowledge/claims/699232MCP
get_claim(id=699232)