# pxpipe
**Cut Claude Code's input tokens by rendering bulky context as images — the same system prompt, tool docs, and history, in a fraction of the tokens.**
An image's token cost is fixed by its pixel dimensions, not by how much
text is inside it. Dense content (code, JSON, tool output) packs ~3.1 chars per
image-token vs ~1 char per text-token on real Claude Code traffic. pxpipe is a
local proxy that exploits that gap: it rewrites the bulky parts of your
request (system prompt, tool docs, older history) into compact PNGs before
the request leaves your machine.
Savings are **workload-dependent** — pxpipe wins on token-dense content and
leaves sparse/small requests untouched — so these are measured snapshots, not
constants. The primary, durable result is **input-token reduction**: dense
system prompts, tool docs, and history go in as compact images instead of text
(the example above is ≈25k text tokens rendered as ≈2.7k image tokens), every
request measured against its own `count_tokens` counterfactual. **Dollars are
downstream of that** — at current Fable list prices the token cut lands as a
**~59–70% lower end-to-end bill** (~72–74% on compressed requests; full pricing
math in the FAQ). But list prices can change tomorrow and the token count
won't, so tokens — not dollars — are the number to watch. Reproduce both from
`~/.pxpipe/events.jsonl`.
This is what the model sees instead of text:
*~48k characters of system prompt + tool docs (this repo's own README,
FINDINGS, and source), ≈25k tokens as text, ≈2.7k image tokens as this page.
Produced by the real `transformRequest` pipeline: whitespace-minified, reflowed
into full rows with ↵ marking original newlines, OCR instruction banner
co-rendered on top. The model reads renders like this at 100/100 on a clean
eval (see benchmarks).*
## Demo
[](https://drive.google.com/file/d/1yZYEjr9765aswomG8mopmp3GlRdSLWM1/view?usp=sharing)
*Side-by-side — plain Claude (left) vs pxpipe (right), both on **Opus 4.8** (opt-in; pxpipe is tuned for Fable — see the Fable clip below). Click the image to watch (Google Drive).*
- **Demo 1 — fix a failing test suite:** both pass; the dashboard shows pxpipe cut the request to a fraction of the tokens (real, server-measured **context/token reduction**).
- **Demo 2 — a big file-context (40 files, ~382k tokens) plus a math question and a "count this phrase" task:** the math answer (a small **text** needle) reads on both. The phrase-count needs reading the **imaged** filler — so pxpipe-on-Opus can't read it and **honestly surfaces that it won't fabricate a number** (the documented lossy limit: exact values stay text). Plain, meanwhile, bogs down counting file-by-file.
**Fable 5 demo (the default, 100/100 reader):**
[](https://drive.google.com/file/d/1pmI3quwv7uuNQ2Z7KMW-78OUOE30AzmU/view?usp=sharing)
*Same two demos re-run with both panes on **Fable 5** (plain left, pxpipe right). Click to watch (Google Drive).*
- **Fable reads what Opus can't.** The imaged phrase-count that Opus refused: the pxpipe arm counts the exact token **10/10** across 39 imaged filler files (matches `grep` ground truth line-for-line) and gets the multi-step ledger arithmetic right (8037 → … → 15,021).
- **Same answers, ~9× cheaper.** Session totals after both demos: plain **$42.21**, context **96% full** (964.5k/1M — one task away from forced compaction) vs pxpipe **$4.51** with context to spare (21.7M cache-read tokens vs 451.8K).
- **Honest caveat, visible in the clip:** the pxpipe arm answered the count first and needed one follow-up nudge to also print the ledger balance in the requested one-line format; the plain arm followed the format on the first try. Legibility is solved on Fable — single-reply format compliance is the remaining rough edge.
## Try it (30 seconds)
```bash
npx pxpipe-proxy # proxy on 127.0.0.1:47821
ANTHROPIC_BASE_URL=http://localhost:47821 claude # point Claude Code at it
```
Open for a live dashboard: tokens saved, per-session
stats, every text→image conversion side by side, a global kill switch, and
runtime model chips including GPT 5.6 and GPT 5.5.
Nothing else changes. Responses stream normally; pxpipe only compresses the
*request* (your context going up), never the model's output. Recent turns stay
text; the system prompt, tool docs, and older bulk history are imaged.
## The honest part, read before relying on it
**It is lossy.** pxpipe is a *gist* tier, not a lossless store. In a
needle-in-haystack eval, exact 12-char hex strings inside dense imaged content
came back **0/15** on Opus and 13/15 on Fable 5, and the failure mode is
*silent confabulation*: a plausible wrong value, not an error. Anything you
need back byte-exact (IDs, hashes, secrets, exact numbers) must stay text.
Recent turns do; a dedicated verbatim-risk guard is not built yet.
**Exact-recall escape hatch.** pxpipe only images Fable requests
(`PXPIPE_MODELS=claude-fable-5`), so any subagent on a non-Fable model passes
through as text. Route work that needs byte-exact values to one — globally with
`CLAUDE_CODE_SUBAGENT_MODEL=claude-sonnet-4-6`, or per-agent with `model: sonnet`
in the agent frontmatter. It reads from source (file/JSONL), not the imaged
history. This covers exact-recall you route on purpose; it does **not** catch a
silent misread you did not expect — that is the unbuilt guard above.
**Does it break real work?** Parity in what we measured: a 10-instance
SWE-bench Lite pilot (the easy subset) resolved **10/10 on both arms**,
pxpipe ON at $27 vs OFF at $54 token-equivalent, and 19 SWE-bench Pro
pairs (harder, long-horizon) resolved **14/19 ON vs 15/19 OFF** at
**-60% per-request**: verdicts agree on 18/19, and the single split
(one ON fail) re-resolved 3/3 when replicated, i.e. run-to-run agentic
variance, not compression. Small n, details and caveats below.
**Savings are workload-dependent.** It wins on token-dense content
(~1 char/token: code, JSON, hashes) and *loses money* on sparse English prose
(~3.5 chars/token). The built-in gate only images content where the math wins,
calibrated against N=391 production rows.
**Model scope:** one `PXPIPE_MODELS` CSV controls which model bases get imaged
across both families — default `claude-fable-5,gpt-5.6` (GPT 5.5 is opt-in;
it degrades on imaged context). Set
`PXPIPE_MODELS=off` to disable imaging entirely, or use
`~/.config/pxpipe/config.json` with `{ "models": "off" }` (or a list). For GPT,
pxpipe keeps tool definitions in native JSON (only verbose schema prose moves
into the image) so tool-calling stays reliable; unlike the Claude path, the GPT
path does not add or depend on Anthropic `cache_control` prompt-cache markers.
The dashboard chips can flip any model live without changing client configs.
Opus 4.7/4.8 was the original Claude scope but misread ~7% of renders
(`10200`→`9400`), so it was turned off by default once Fable 5 hit 100/100 with
identical image billing — opt it back in at your own risk via `PXPIPE_MODELS` or
the dashboard chips. Everything else passes through untouched.
## Benchmarks (reproducible)
Measured with novel random-number problems the model cannot have memorized:
| test | N | text | pxpipe (image) | tokens |
|---|---:|---:|---:|---|
| novel arithmetic, `claude-fable-5` | 100 | 100% | **100%** | **−38%** |
| novel arithmetic, `claude-opus-4-8` | 100 | 100% | 93% | −38% |
| gist recall A/B (decisions, values, paths, names, negations; with distractors; 15k-45k char sessions), Fable 5 | 98/arm | 98/98 | **98/98** | - |
| state tracking (value mutated 3x, final/first/count), Fable 5 | 18/arm | 18/18 | **18/18** | - |
| confabulation on never-stated facts (lower is better), Fable 5 | 16/arm | 0/16 | **0/16** | - |
| verbatim 12-char hex recall, dense render, Opus | 15 | 15/15 | **0/15** | - |
| verbatim 12-char hex recall, dense render, Fable 5 | 15 | - | **13/15** | - |
### SWE-bench Lite pilot (end-to-end task quality)
10 SWE-bench Lite instances, Claude Code + Fable 5, paired runs through
pxpipe ON vs OFF, graded with the official `swebench` Docker harness:
| | pxpipe ON | OFF |
|---|---:|---:|
| resolved | **10/10** | 10/10 |
| request size vs own uncompressed body | **−65%** | ±0 |
The −65% is per-request (`count_tokens` probe of each body before
compression), so it has no turn-count confound. n=10/arm, Lite skews easy.
Run totals, receipts, caveats: [`eval/swe-bench/`](eval/swe-bench/).
### SWE-bench Pro bench (harder, long-horizon)
19 completed pairs across two runs (2 dropped: checkout failed both
arms), same setup, official `SWE-bench_Pro-os` Docker harness:
| | pxpipe ON | OFF |
|---|---:|---:|
| resolved | 14/19 | 15/19 |
| request size vs own uncompressed body | **−60%** | ±0 |
Verdicts agree on 18/19 (three instances failed both arms, one with
byte-identical patches across arms). The single split (navidrome, ON
fail) was replicated 3x on the ON arm: all three runs produced an
identical patch and **resolved**, so the original loss was run-to-run
agentic variance, not compression. Receipts:
[`eval/swe-bench-pro/`](eval/swe-bench-pro/).
We also ran GSM8K: 96% imaged. But GSM8K is in training data, so the model
recalls memorized answers through its own misreads, inflating the score, so we
lead with the clean novel-number eval instead. Reproduce:
[`eval/gsm8k/`](eval/gsm8k/) · [`eval/needle-haystack/`](eval/needle-haystack/) ·
[`eval/gist-recall/`](eval/gist-recall/) ·
full analysis in [`FINDINGS.md`](FINDINGS.md).
## FAQ
**Is the headline end-to-end, or only on the requests you touched?**
End-to-end, the whole bill. Most compression tools report savings only on
the input slice they touched, which flatters the number. The end-to-end
denominator is *every* production request: the small ones pxpipe correctly
left untouched, all cache writes and reads, and all output tokens (which the
proxy never compresses). On a 13,709-request snapshot that was 59% ($100 →
~$41); a later 8,904-compressed-request trace measured ~70%. Compressed-only
runs higher (~72–74%) and is quoted separately, never as the headline. The
exact figure is workload-dependent — reproduce it on your own log.
**How is the math measured?**
Both sides of the same request, at the same moment. For every `/v1/messages`
POST the proxy fires a free `count_tokens` probe on the original uncompressed
body (the counterfactual) in parallel with the real forward, and reads
Anthropic's actually-billed usage block off the response. Both land in the
same row of `~/.pxpipe/events.jsonl`, so there is no turn-count or
run-to-run confound. Dollar conversion uses Fable 5 list ratios: input ×1.0,
cache write ×1.25, cache read ×0.1, output ×5. Cache pricing is applied
identically to both sides, so the caching discount cancels and cannot be
double-counted as "savings". Re-derive it yourself from the events log: the
formula and field names are documented in `src/core/baseline.ts`.
**What does it actually compress?**
Three kinds of *input* blocks, each behind a profitability gate:
1. large `tool_result` bodies (file reads, command output, logs) above
~6k chars of token-dense content
2. older collapsed history: turns behind the live tail get re-rendered as
image pages, recent turns always stay text
3. the static system prompt + tool docs slab
Everything else passes through byte-identical: your messages, recent turns,
the model's output (it is the response, the proxy never touches it), sparse
prose, and anything too small to win. Non-Fable models pass through entirely.
**Has it ever failed for real, outside the benchmarks?**
Yes, once in weeks of daily use: the model recalled a person's name from
imaged chat history and got it confidently wrong. No error, just a
plausible wrong name. That is the documented failure mode: exact strings
in imaged content are not byte-safe. Coding sessions tolerate this because
the agent re-reads files before editing; pure chat recall has no such check.
## How it works
```
tool_result string ──► wrap at 1928px-wide columns ──► pack ~92,000 chars/page ──► PNG[]
```
The proxy intercepts `/v1/messages`, rewrites eligible bulk history into image
blocks, splices them back cache-friendly (static prefix preserved, so prompt
caching keeps working), and forwards. Per-request events log to
`~/.pxpipe/events.jsonl`.
The economics: a 1928×1928 image costs ≈4,761 vision tokens and holds up to
≈92,000 chars (≈48,000 text tokens at the observed density), so plain text is
cheaper *only* when it runs denser than ~19 chars/token. Claude Code transcripts
are far below that (observed 1.91 chars/token, N=391). The runtime estimator (`estimateImageCount`) plus a chars/token gate
decides per-request; sparse prose is left as text.
## Library use (no proxy)
Same engine, no proxy. Render text → PNGs, or run the full cache-safe transform:
```ts
import { renderTextToPngs, transformAnthropicMessages } from "pxpipe";
const imgs = await renderTextToPngs(toolResultText); // RenderedImage[]
const { body, applied, info } = await transformAnthropicMessages({
body: requestBytes,
model: "claude-fable-5",
});
```
`options.keepSharp(block)` pins blocks as text (override the heuristic for IDs,
hashes, paths); `options.emitRecoverable` returns the originals of imaged blocks
so a stateful caller can recover them — the two halves of the fidelity contract
for the lossy limitation below. Runtime is pure-JS (Node and edge/Workers);
`@napi-rs/canvas` is build-time only. Full API, types, and constants:
`src/core/index.ts`.
## Development
```bash
pnpm install && pnpm test # 376 tests
pnpm run build # regenerates dist/
```
## Limitations
* **Lossy**: see "the honest part" above. Verbatim recall from images is unreliable.
* Render latency: encoding PNGs adds time to large requests before they leave
(partly offset by the model ingesting fewer tokens). Responses stream normally.
* ASCII/Latin-1 well tested; CJK works but conservatively.
* Runtime is pure-JS — runs on Node and edge/Workers. `@napi-rs/canvas` is a
build-time-only dev dep (regenerating the glyph atlas), not a runtime dep.
* Fable 5 only.
## Roadmap
Everything above is measured. Everything here is not. These are hypotheses, not
claims; they ship as numbers with an n or they get cut.
* **Sharper glyphs.** The 13/15 verbatim gap is partly font legibility, not just
the model. A per-char confusion matrix across render styles is paused mid-run
(`eval/glyph-matrix/`); if a zero-cost style lowers read error, the gate
compresses harder at the same fidelity.
* **Effective context.** Dense text carries at ~3x fewer tokens as images. If
that holds in the live window and not just the bill, 1M tokens holds ~2x the
real content. Open question: can a task needing ~2M raw context run inside
Fable's 1M once the bulk is imaged?
* **Less active text, sharper model.** Long contexts degrade reasoning as they
fill. Imaging old bulk shrinks what the model actively reads while keeping it
reachable. Hypothesis: same information, smaller active context, better
long-task accuracy.
One bet: longer effective context and a sharper model on long tasks, from the
same Fable 5. Numbers or retraction, no hype between.
## License
MIT.