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Field ID: <code>input_tokens_cache_read</code>

Description: The number of input tokens served from the prompt cache.

Not available yet. GitHub Copilot does not publish this measurement, so it is empty on every record you can query today. It is populated by Anthropic Claude, Cursor and OpenAI Platform, which are coming soon.

The <code>Input tokens (cache read)</code> field counts prompt tokens that were served from cache rather than reprocessed. They are substantially cheaper, so the cache ratio is one of the few AI efficiency levers a team controls.

Cache efficiency: Cache reads as a share of all input tokens shows how well long-running sessions are reusing context. A falling ratio usually means sessions are being restarted more often.

Explaining flat costs under rising usage: A team whose cache ratio is improving can grow usage substantially without a matching rise in consumption.

Only three providers report it: Copilot and Codex publish no cache split.

- Cache efficiency: Cache reads as a share of all input tokens shows how well long-running sessions are reusing context. A falling ratio usually means sessions are being restarted more often.
- Explaining flat costs under rising usage: A team whose cache ratio is improving can grow usage substantially without a matching rise in consumption.
- Only three providers report it: Copilot and Codex publish no cache split.

AI Usage > Input tokens (cache read)

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GitHub Copilot	N/A
Anthropic Claude (coming soon)	`tokens.cache_read` on Claude Code records; `cache_read_input_tokens` on direct API traffic
Cursor (coming soon)	the sum of `tokenUsage.cacheReadTokens` across the record's usage events
OpenAI Platform (coming soon)	`input_cached_tokens`
OpenAI Codex (coming soon)	N/A

AI Usage > Input tokens (cache read)

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