Unverified70% confidenceFactExact time
Thinking models with Chain-of-Thought reasoning capabilities may consume 5–20 times the tokens of ordinary conversations in practice, due to the need to generate large numbers of intermediate reasoning tokens.
2
Sources
70%
Confidence
Long-term
Relevance
8/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedChain-of-Thought reasoning significantly increases generation length: the final answer to a simple math problem may require only 20 tokens, but the intermediate reasoning process may require 200–500 tokens.78% similarUnverifiedMore complex models and longer reasoning chains raise the billing weight of a single conversation.76% similarUnverifiedLarge model inference is evolving from a conversation-first to an agent-first direction.76% similarUnverifiedReasoning models perform extended reasoning within thinking blocks wrapped in special tokens (such as <think> and </think>); this content is typically not shown directly to users but consumes a large number of tokens.76% similarVerifiedThe internal chain of thought in reasoning models generates a large number of output tokens, making the actual cost of completing complex tasks often far higher than what a per-token price comparison would suggest.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/805264API
curl https://kongchang.com/api/v1/knowledge/claims/805264MCP
get_claim(id=805264)