Unverified50% confidenceFactExact time
Full chain-of-thought reasoning requires generating a large number of tokens, which is extremely demanding on computing resources.
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50%
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Long-term
Relevance
9/21/2026
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VerifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.78% similarUnverifiedReasoning mode performs internal chain-of-thought reasoning before the final answer, and the thinking tokens generated are billed; in complex programming tasks, thinking tokens may account for 60%–70% of total consumption, and costs may be 3–5 times that of normal mode.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% similarUnverifiedWhen handling long contexts, multi-turn dialogues, and complex reasoning chains in inference scenarios, models need to frequently read and write large amounts of intermediate states (such as KV Cache), placing extremely high demands on memory capacity and bandwidth.74% similarUnverifiedReasoning tasks require a strong logical chain while summarization tasks demand concise language ability, and using the same model for both not only wastes computing power but may also not yield the best results.72% similar
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