Arena has published an analysis of more than 26,000 agent sessions showing that caching sharply lowers the cost of running AI agents, and that without it, the same workloads would cost 260% more.
Sep 12 – Oct 23, 2026 · Agent Arena analysis
Caching cuts the cost of running AI agents by roughly 70%
Across more than 26,000 agent sessions, the same workloads would cost 260% more without caching. Agents reuse large blocks of context on every step, so caching has an outsized effect on their economics.
26,000+
agent sessions analyzed
+260%
cost without caching
Relative cost of the same workloads
Column height is proportional to total cost. Uncached is 3.6× the cached cost (+260%).
Without caching
3.6× (+260%)
Takeaway: the cached setup costs about a quarter of the uncached one, implying savings on the order of 70%.
Why agents benefit more
Each step in an agent loop reuses the same large context.
1. Model call
Agent sends a step to the model.
→
2. Reuse context
System instructions, tool definitions and earlier turns are served from cache.
→
3. Repeat
More steps mean more reused tokens, so savings grow with task length.
Limits to keep in mind
The 260% figure reflects sampled agent workloads, not every task.
Data comes from a single platform, with no independent verification in the available notes.
No breakdown by individual agent or lab, and no detail on how caching was configured.
Short single-turn interactions reuse little context and may see much smaller gains.
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