Unverified50% confidenceFactExact time
Retry rate refers to the proportion of cases where the model's first output is not adopted and the developer needs to ask again or adjust the prompt and try again; it is ubiquitous in real workflows because the model's one-shot success rate is far below the ideal value assumed by benchmarks.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedBenchmarks typically count only the cost of one successful call and completely ignore the repeated trial and error in real workflows; counting only the cost of one successful call is equivalent to assuming a retry rate of zero, which is severely disconnected from actual bills.74% similarUnverifiedAs benchmark scores of leading models grow increasingly close, the perceived difference that parameter numbers make for ordinary users is growing smaller, and what determines user retention is whether a model is useful, reliable, and time-saving in everyday scenarios.73% similarUnverifiedIf the highest score after reranking is below a threshold validated against a test set, the model should be told that no relevant content was found.73% similarUnverifiedWhen the success rate is extremely low, almost all sample rewards are identical, resulting in extremely high policy gradient variance and very low signal-to-noise ratio, preventing the model from obtaining effective update directions.72% similarUnverifiedWhen researchers run different testing frameworks using the same model, scores often fluctuate noticeably.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1049238API
curl https://kongchang.com/api/v1/knowledge/claims/1049238MCP
get_claim(id=1049238)