---
id: coding-agent-cache-oct26
slug: coding-agent-cache-hit-rate-oct-2026
title: "Cache hit rate by coding agent, October 2026"
topic: agentic
period: Oct 2026
updated: 2026-11-11
license: CC BY 4.0
canonical: https://www.requesty.ai/data/coding-agent-cache-hit-rate-oct-2026
---

# Cache hit rate by coding agent, October 2026

> Which coding agents get the most from prompt caching? In October 2026 Cline led at 96% of input tokens from cache. Qwen Code was lowest at 82%. Most agents sat above 90%.

*Topic: Agentic workloads. Period: Oct 2026. Last updated 2026-11-11.*

## Why it matters

Coding agents resend large contexts on every turn, so cache hit rate drives their cost. A few points of cache hit rate is a large share of the input bill.

## Questions this answers

- Which coding agent has the best cache hit rate?
- How much do coding agents rely on prompt caching?
- Why is Claude Code cache hit rate lower than other agents?

## Key findings

1. Cline: 96%.
2. Roo Code: 96%.
3. GitHub Copilot: 95%.
4. Cursor: 95%.
5. Pi: 95%.

## Data

| Agent | Cache hit rate (percent) |
| --- | --- |
| Cline | 96.00% |
| Roo Code | 95.57% |
| GitHub Copilot | 95.45% |
| Cursor | 95.42% |
| Pi | 94.58% |
| Kilo Code | 93.82% |
| OpenCode | 93.75% |
| Hermes Agent | 92.78% |
| Zed | 91.98% |
| Zoo Code | 89.66% |
| Claude Code | 85.22% |
| Qwen Code | 81.87% |

## Caveats

- Agents identified from client user agent and app title, with at least 1,000 calls and 5 organizations.

## Cite as

**APA.** Requesty (2026). Cache hit rate by coding agent, October 2026. Requesty Data. https://www.requesty.ai/data/coding-agent-cache-hit-rate-oct-2026

```bibtex
@misc{requesty_coding_agent_cache_hit_rate_oct_2026,
  author       = {{Requesty}},
  title        = {Cache hit rate by coding agent, October 2026},
  year         = {2026},
  howpublished = {\url{https://www.requesty.ai/data/coding-agent-cache-hit-rate-oct-2026}},
  note         = {Requesty Data}
}
```

---

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