Unverified50% confidenceFactExact time
Under the reference workload (1,100 tasks per month, each with 12K fresh input, 48K cached input, and 6K output tokens), enabling caching saves about $190 per month compared with not enabling it.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/18/2026
First Seen
Valid until: 1/16/2027
Sources
Related Entities
Related Claims
UnverifiedThe task produced approximately 716,000 output tokens, approximately 43 million cache input reads, and approximately 1.5 million cache writes.79% similarUnverifiedIn a mid-tier model scenario of 100,000 queries per month (baseline cost about $1369 per month), switching to a small model saves about $980 per month (72%); retrieving 3 chunks instead of 5 saves 1000 input tokens per query, saving $200.75% similarUnverifiedA single 10-turn task cumulatively consumes 445850 input tokens, and the cost per ticket is between $5.35 and $8.90; under abnormal loops the cost of a single task can exceed $18; calculated at 10000 tickets per month, re-reading dead JSON alone burns about $80000.71% similarUnverifiedExplicitly setting max_tokens for each task type and using JSON mode or structured output constraints: on flagship models, the cost difference between a 200-token and a 2,000-token output is about 10x, and this alone can save $20 to $30 per month.71% similarUnverifiedAt one poll every 10 seconds over an 8-hour workday, each user generates 2,880 multimodal API calls per day; at closed-source API pricing, a heavy user's token cost is $20 to $40 or more per month.70% similar
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