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AI CLUSTERED EVENT · 10/14/2026

Artificial Analysis Launches Cache Hit Rate Metrics: GLM-5.3 Cost Per Task Varies 9x Across Providers

1 reports archived1 independent sourcesupdated 10/14/2026, 03:17:42
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Artificial Analysis has introduced new metrics for 'Cache Hit Rates' and 'Cost per Task by Provider,' highlighting the significant impact of caching on agentic workload costs. Data reveals that the cost per task for identical models like GLM-5.3 can vary up to 9x across different inference providers, driven primarily by differences in cache hit rates and pricing structures. By measuring actual cache hits during multi-turn coding tasks, this tool provides developers with quantitative data to optimize AI application expenses.

LATEST/Artificial Analysis has introduced new metrics for 'Cache Hit Rates' and 'Cost per Task by Provider,' highlighting the significant impact of caching on agentic workload costs. Data reveals that the cost per task for identical models like GLM-5.3 can vary up to 9x across different inference providers, driven primarily by differences in cache hit rates and pricing structures. By measuring actual cache hits during multi-turn coding tasks, this tool provides developers with quantitative data to optimize AI application expenses.

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Total 1 reports · Latest first
  1. Artificial Analysis (@ArtificialAnlys)T1·68 pts
    • Agentic workloads resend full history each turn, making most input tokens cacheable; thus, cache hit rate is a primary driver of final cost.
    • Real-world tests show a 9x cost variance for GLM-5.3 across providers, with endpoints like Z AI and Together AI achieving near or 100% cache hit rates.
    • New metrics are calculated using standardized Intelligence Index runs, isolating infrastructure-level cache performance and pricing from model capability differences.
Artificial Analysis Launches Cache Hit Rate Metrics: GLM-5.3 Cost Per Task Varies 9x Across Providers | Today for AI