Unverified50% confidenceFactExact time
In agentic tasks, the model repeatedly feeds context back in, causing input token consumption to balloon sharply, which is the key reason that estimating total cost by simply doubling the unit price becomes inaccurate.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLow-confidence requests first fail on the small model and then fall back to the large model with the same context for a second generation, causing such requests to incur the cost of two generations and resulting in double payment.74% similarUnverifiedIn model selection, pursuing the last few percentage points of performance often requires paying several times or even dozens of times more in cost.72% similarUnverifiedToken pricing varies enormously across different models, and choosing the wrong model can result in several times the cost for batch tasks.71% similarUnverifiedReasoning models may consume several times or even more than ten times the output tokens, directly driving up API call costs.70% similarUnverifiedThe newer the model, the better the results, but the more token quota it consumes.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1007852API
curl https://kongchang.com/api/v1/knowledge/claims/1007852MCP
get_claim(id=1007852)