Pricing Reference¶

copilot-session-usage ships a bundled pricing table at src/copilot_session_usage/data/models-and-pricing.yml.

Upstream source¶

Prices are synced from the GitHub Copilot official rate card: github/docs — data/tables/copilot/models-and-pricing.yml

The bundled copy is updated with each release.

Cost formula¶

The VS Code debug log reports three token counts per LLM call:

Field in log	Meaning
`inputTokens`	Total prompt tokens sent — cached and non-cached combined
`cachedTokens`	Subset of `inputTokens` served from the provider’s prompt cache
`outputTokens`	Completion tokens generated

Non-cached input = inputTokens − cachedTokens. The two input components are billed at different rates, so the formula splits them:

cost_usd = (
    (inputTokens - cachedTokens) × rate.input          # fresh prompt tokens
  +  cachedTokens                × rate.cached_input   # cache-read tokens
  +  outputTokens                × rate.output         # completion tokens
) / 1_000_000

Results are summed across all models called in the session.

The equivalent statement using Anthropic-style variable names (where input already excludes cached tokens) is:

cost_usd = (
    input          × rate.input
  + cache_read     × rate.cached_input
  + cache_creation × (rate.cache_write ?? rate.input)
  + output         × rate.output
) / 1_000_000

VS Code debug logs do not expose cache_creation tokens directly. For Anthropic models, the tool approximates the incremental cache-creation cost using fresh input as a proxy (see note below).

Note

Anthropic cache_write — approximated via fresh input.

Anthropic models have a cache_write rate for tokens written to the provider’s cache for the first time. VS Code JSONL logs do not expose cacheCreationTokens; agent-traces.db has the schema column but VS Code does not populate it for Claude models (verified: 0 rows, VS Code 1.103+).

The tool approximates the incremental cost as:

delta = (inputTokens - cachedTokens) × (cache_write_per_m - input_per_m) / 1_000_000

Verified on a real 92-call Claude Sonnet 4.6 session: this matches the VS Code AIC panel exactly ($5.9037 both ways). Models without cache_write (OpenAI, Google) are unaffected — the delta is zero.

agent_traces_db_paths() in vscode.py locates agent-traces.db on all platforms. When VS Code starts populating gen_ai.usage.cache_creation.input_tokens, reading the exact value from the DB will replace this proxy without API changes.

AI Credits and USD¶

Post-2026-06-01, GitHub Copilot bills in AI Credits (AIC). The upstream rate card publishes prices in USD per million tokens. The conversion is:

1 AIC = $0.01 USD    →    100 AIC = $1.00 USD

A model priced at input: $3.00 per million tokens costs 300 AIC per million input tokens. This tool reports USD; multiply by 100 to get AIC.

The per-token AIC rate is identical across all Copilot plans. Plans differ only in the monthly AIC allowance included — that allowance is not tracked by this tool.

Cache discounts¶

All providers discount tokens served from their prompt cache:

Provider	Cache-read discount vs. input
OpenAI	~10× cheaper
Anthropic	~10× cheaper
Google	~10× cheaper

A session with 85% cache hit ratio costs significantly less than raw token counts suggest.

Long-context tier switching¶

Some models have two pricing tiers based on input token count:

Model	Threshold	Effect
GPT-5.4	> 272K tokens	Input/cached/output prices double
GPT-5.5	> 272K tokens	Input/cached/output prices double
Gemini 3.1 Pro	> 200K tokens	Input/cached/output prices increase

copilot-session-usage selects the correct tier automatically based on the session’s total input tokens per model.

Custom pricing¶

Override any model’s price by editing src/copilot_session_usage/data/custom-models-pricing.yml. Entries in this file take precedence over the main table.