Cache Hit Rate
The proportion of repeated or similar prefix tokens read directly from cache during large model inference. It affects the practical cost of long-context and agent scenarios.
Timeline (last 90 days)
If the cache hit rate stays below 30%, it usually means the context changes frequently, the cache is not taking effect or the configuration is wrong, in which case the per-conversation cost rises significantly.
Bai Lu suggested that when observing Agent usage costs, one need not fixate on the amount spent but should look at the cache hit rate, and only if it is below 30% is further investigation needed.
In the probe test, all six seeded defects were explicitly identified, along with an additional valid finding outside the key: the reviewer pointed out that under an 87% cache hit rate, the metric definition that counts the full 19ms origin fetch into the per-request average contradicts the hit rate.
Cache hit rate refers to the proportion, in continuous conversations or repeated requests, in which the model provider caches and reuses context with the same prefix and bills it at a discounted price; in programming agent workflows, the higher the cache hit rate, the less actual token billing and the faster the response.
The actual extent of cache savings depends on the hit rate; if 80% of input hits the cache, the actual input cost is approximately 0.8 × cache price + 0.2 × regular price.
The target for repetitive workloads is for more than 80% of input tokens to hit the cache; if it is only 20%, that means there is still dynamic content changing in the prefix.
When a new model launches and user input patterns are dispersed, the cache hit rate often drops, further amplifying load pressure, which explains why a cluster of the same scale appears strained in the early days of a new model.
When using DeepSeek Harness, the cache hit rate is higher than other Agent solutions
A key technical aspect of OpenAI's new model is a higher cache hit rate, which allows reuse of existing results when processing repeated or similar content to reduce token consumption.
Cache Hit Rate refers to the proportion of requests in which an AI model is able to reuse already-cached context (prompt prefixes) when processing requests; the higher the hit rate, the lower the API cost for the same workload.
All Facts (9)
If the cache hit rate stays below 30%, it usually means the context changes frequently, the cache is not taking effect or the configuration is wrong, in which case the per-conversation cost rises significantly.
50%UnverifiedBai Lu suggested that when observing Agent usage costs, one need not fixate on the amount spent but should look at the cache hit rate, and only if it is below 30% is further investigation needed.
50%UnverifiedIn the probe test, all six seeded defects were explicitly identified, along with an additional valid finding outside the key: the reviewer pointed out that under an 87% cache hit rate, the metric definition that counts the full 19ms origin fetch into the per-request average contradicts the hit rate.
50%UnverifiedCache hit rate refers to the proportion, in continuous conversations or repeated requests, in which the model provider caches and reuses context with the same prefix and bills it at a discounted price; in programming agent workflows, the higher the cache hit rate, the less actual token billing and the faster the response.
50%UnverifiedThe actual extent of cache savings depends on the hit rate; if 80% of input hits the cache, the actual input cost is approximately 0.8 × cache price + 0.2 × regular price.
50%UnverifiedThe target for repetitive workloads is for more than 80% of input tokens to hit the cache; if it is only 20%, that means there is still dynamic content changing in the prefix.
50%UnverifiedWhen a new model launches and user input patterns are dispersed, the cache hit rate often drops, further amplifying load pressure, which explains why a cluster of the same scale appears strained in the early days of a new model.
50%UnverifiedA key technical aspect of OpenAI's new model is a higher cache hit rate, which allows reuse of existing results when processing repeated or similar content to reduce token consumption.
50%UnverifiedCache Hit Rate refers to the proportion of requests in which an AI model is able to reuse already-cached context (prompt prefixes) when processing requests; the higher the hit rate, the lower the API cost for the same workload.
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