# Show HN: Claude Code's $200 plan is a 17× subsidy on the raw API

> Source: <https://github.com/Coral-Bricks-AI/coral-ai/tree/main/claude-code-token-xray>
> Published: 2026-05-27 17:25:53+00:00

Reverse-engineer a month of your own local Claude Code logs
(`~/.claude/projects/*/*.jsonl`

) into where the **tokens, time, and cost**
actually go — and run it on yours. Reads **only local logs**; nothing is sent anywhere.

What it found(one month of my own logs — 181 sessions, 25,564 model calls):

You don't pay to generate, you pay to re-read.~29M unique tokens →4.35B billed (~150×), because every turn re-sends the whole ~173K-token context.- The bill is
84% input / 16% output— and re-reading the same context is64%of it.- The biggest line is the one you never see:
hidden reasoningis 84% of outputand~60% of everything re-read.~$3,371for the month at Opus 4.7 list rates. Caching already serves 98% of input — and re-reading isstill64% of the bill.Full write-up (all the tables, the why, the main-thread-vs-subagent split) →

[coralbricks.ai/blog/claude-code-token-xray]

```
pip install -r requirements.txt   # just tiktoken
python3 token_time_breakdown.py
python3 cost.py
python3 main_vs_sidecar.py
python3 reread_breakdown.py
```

tiktoken is OpenAI's tokenizer, not Claude's, so token

proportionsare reliable to ~±15%, not Claude-exact. The billed-token counts in`cost.py`

come straight from the API`usage`

blocks and are exact.

From `cost.py`

on my logs, priced at Opus 4.7 list rates:

| Line item | Cost | Share |
|---|---|---|
| Input — re-reading context (cache reads) | $2,176 | 64% |
| Input — cache writes | $682 | 20% |
| Input — fresh (uncached) | $2 | 0% |
| Output — reasoning | $429 | 13% |
| Output — tool calls + summaries | $82 | 2% |
Total |
$3,371 |
100% |

Caching is the only thing keeping it sane — without it the same work lists at
**~$22,630** (~7×). Your numbers will differ; that's the point. Run it on yours.

— the headline table: tokens (marked input/output)`token_time_breakdown.py`

**and** wall-clock time per activity (reasoning, running commands, writing tool calls, subagents, summaries, reading/searching, editing) plus the passive-context rows (system prompt + tools, attachments, the typed prompt, injected reminders). One pass, so tokens and time stay consistent. Reasoning isn't stored in plaintext (only an encrypted signature), so it's recovered by subtraction:`output − tool_calls − summaries`

. Time is reconstructed from event timestamps.— billed token totals (cache reads / cache writes by TTL / fresh input / output) priced at Opus 4.7 list rates, plus the no-caching counterfactual.`cost.py`

— splits the human-driven main thread from spawned subagents (logged under nested`main_vs_sidecar.py`

`*/subagents/*.jsonl`

); reports billed tokens, per-model mix, cache-hit rate, turns per agent (per session for the main thread, per subagent for the sidecar), and cost for each, plus the combined total.— per-activity`reread_breakdown.py`

*cumulative*input: replays each session's context growth to show what each kind of context costs once it's re-read every turn. Reports`unique`

vs`re-read`

tokens per activity (reasoning is the biggest re-read line). The replay is scaled to the measured billed input (exact); the per-activity split is a model.

- One person's month on one machine — directional, not a benchmark. Claude Code is dynamic, so your split will differ. That's the point: run it on yours.
- A generation-time gap also includes the model reading its context before it writes; Bash time is real execution (commands auto-approved), but code run in the background or a separate terminal isn't counted.
- The system-prompt row is estimated from each session's first cache write.

If this helped you see where your Claude Code tokens, time, and cost actually go,
please ⭐ [the repo](https://github.com/Coral-Bricks-AI/coral-ai) — it helps others
find it. Curious what your re-read share comes out to.

Apache 2.0 — see the repository [LICENSE](/Coral-Bricks-AI/coral-ai/blob/main/LICENSE).