Verified65% confidenceFactTime unknown
In large language models, the cost of generating code (output tokens) is far higher than the cost of analyzing problems (chain-of-thought reasoning).
3
Sources
65%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe per-task cost of Codex is far higher than that of an ordinary ChatGPT conversation, because it requires maintaining a full runtime environment and performing large language model inference computation.80% similarUnverifiedThe inference cost of large language models is far higher than that of traditional web services; each conversation request may trigger computation involving billions of parameters, with large VRAM usage, narrow batching windows, and complex GPU scheduling, so increasing the number of servers does not always linearly increase service capacity.79% similarUnverifiedOutput token prices in API pricing are generally higher than input token prices, because generating text consumes more computing power than processing input.75% similarUnverifiedTraditional diffusion language models often require dozens or even hundreds of denoising steps, making their actual inference cost higher than that of autoregressive models.74% similarVerifiedIn large language models, output tokens are typically 2–3 times more expensive than input tokens.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/12057API
curl https://kongchang.com/api/v1/knowledge/claims/12057MCP
get_claim(id=12057)