GitHub Copilot Usage Reports Add Per-Model Token Detail Behind AI Credits
The August 11 report now exposes input, output, cache-read, and cache-write tokens, but cost attribution still needs task and quality evidence.
Key takeaways
GitHub added a per-model token breakdown to AI usage reports on August 11, 2026. For each model, users can now see input, output, cache-read, and cache-write tokens beside the AI Credits consumed. The detail is available to Copilot Business and Enterprise administrators and to individual Copilot users through a downloadable report on the AI usage page in billing settings. It can reveal whether long context, verbose output, cache behavior, or retries explain a charge. However, token detail explains consumption rather than quality or return on investment. Use a fixed billing window and task labels, then compare failures, rework, accepted output, and human review alongside tokens and credits before changing a model, prompt, or workflow policy.
# GitHub Copilot Usage Reports Add Per-Model Token Detail Behind AI Credits
August 13, 2026
Direct answer
Download the AI usage report, aggregate every token type and AI Credits by model, and compare them within one task and time window. Keep quality, failures, rework, and human acceptance beside the cost data.
Fact sources
GitHub announced per-model input, output, cache-read, and cache-write token detail on August 11, 2026.
The new fields sit beside AI Credits, exposing the usage components behind a charge that previously lacked token detail.
Copilot Business and Enterprise administrators and individual Copilot users can obtain the breakdown from a downloaded billing report.
Five steps to investigate AI cost with token detail
- Download a report for one billing cycle using a fixed time zone.
- Aggregate input, output, cache-read, cache-write tokens, and AI Credits by model.
- Label major work by repository, task, user, and successful or failed outcome.
- Inspect long inputs, repeated context, verbose output, cache misses, and retries.
- Retest after a model or prompt change and compare quality, rework, and human acceptance too.
Why it matters
AI Credits show the billing result, while token detail gives a closer view of causes. Without task and outcome data, high use can be mislabeled as waste and low use as efficiency.
Impact for ordinary AI users
Individuals can explain charges more precisely, and administrators can locate model-level cost. They also need policies for report access, user data, time zones, and retention.
Related tools and tutorials
Start with one reversible task, verify version, permissions, cost, and logs, then record the result in the team runbook.
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FAQ
Where is the token breakdown?
GitHub says it is in the downloadable AI usage report from billing settings, not only in the on-screen total.
Are more cache-read tokens always better?
No. Interpret cache behavior with price, latency, task success, and output quality.
Is the model with fewer tokens automatically cheaper overall?
Not if it creates more failures, retries, rework, or human correction.
Source links
- GitHub Changelog: Per-model token breakdown (2026-08-11)
- GitHub Docs: Billing reports reference
- GitHub Docs: Models and pricing for Copilot
What this means for everyday users
Record billing window, time zone, model, token type, AI Credits, task, failure, rework, acceptance, and report access.
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Summary
Per-model token detail makes AI Credits explainable, not automatically efficient. Join billing, task, quality, and human-cost evidence before changing model policy.
