Benchmarks

Headroom's core promise: compress context without losing accuracy. This page shows accuracy benchmarks, compression performance, and real-world production telemetry from 250+ active proxy instances.

Key Results

98.2% recall on article extraction with 94.9% compression. 52ms median overhead in production. 1.4 billion tokens saved across 249 instances.

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Compression Performance

Tested on Apple M-series (CPU), headroom v0.5.18. Each test runs compress() on realistic tool outputs.

Content Type	Original	Compressed	Saved	Ratio	Latency
JSON array (100 items)	3,163	297	2,866	90.6%	1ms
JSON array (500 items)	9,526	1,614	7,912	83.1%	2ms
Shell output (200 lines)	3,238	469	2,769	85.5%	1ms
Build log (200 lines)	2,412	148	2,264	93.9%	1ms
grep results (150 hits)	2,624	2,624	0	0.0%	<1ms
Python source (~480 lines)	2,958	2,958	0	0.0%	<1ms
Total	23,921	8,110	15,811	66.1%	5ms

Notes:

• grep results and Python source show 0% compression — these are already compact structured formats. SmartCrusher only compresses JSON arrays; code passes through to preserve correctness.
• Latency is for the compress() SDK call, not the full proxy round-trip.

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Production Telemetry

Real-world data from 50,000+ proxy sessions across 250+ unique instances (March 30 – April 2, 2026). Collected via anonymous telemetry beacon (opt-in: HEADROOM_TELEMETRY=on; telemetry is off by default).

Proxy Overhead

Percentile	Latency
Median (P50)	52ms
P90	309ms
P99	4,172ms
Mean	161ms

The median 52ms overhead is negligible compared to LLM inference time (typically 2-10 seconds).

Compression Rate

Percentile	Compression
P25	4.8%
Median	4.8%
P75	6.9%
Mean	11.3%

Median compression is modest because many requests are short conversational turns. Heavy tool-use sessions (file reads, shell output) see 40-80% compression.

Pipeline Step Timing (Production Median)

Step	Median	P90	Description
pipeline_total	16.9ms	289ms	Full compression pipeline
content_router	11.7ms	259ms	Content detection + routing
compressor:smart_crusher	50.1ms	50ms	JSON array compression
compressor:text	32.0ms	576ms	Text compression (Kompress ONNX)
compressor:mixed	316ms	428ms	Mixed content compression
compressor:code_aware	815ms	886ms	Tree-sitter AST compression
_initial_token_count	2.9ms	16ms	Token counting (tiktoken)
_deep_copy	0.1ms	0.3ms	Message copy overhead

Fleet Summary

Metric	Value
Clean instances	249
Total tokens saved	1.4 billion
Total $ saved	~$4,000
OS distribution	Linux 57%, macOS 38%, Windows 5%
Top version	0.5.17 (77%)
Models used	Claude Opus 4.6, Sonnet 4.6, Haiku 4.5

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Accuracy Benchmarks

HTML Extraction

Dataset: Scrapinghub Article Extraction Benchmark Samples: 181 HTML pages with ground truth article bodies Baseline: trafilatura (0.958 F1)

Metric	Value	Description
F1 Score	0.919	Token-level overlap with ground truth
Precision	0.879	Proportion of extracted content that's relevant
Recall	0.982	Proportion of ground truth content captured
Compression	94.9%	Average size reduction

For LLM applications, recall is critical — 98.2% means nearly all article content is preserved. The slight precision drop (some extra content) doesn't hurt LLM accuracy.

# Run it yourself
pip install "headroom-ai[html]" datasets
pytest tests/test_evals/test_html_oss_benchmarks.py::TestExtractionBenchmark -v -s

JSON Compression (SmartCrusher)

Test: 100 production log entries with critical error at position 67 Task: Find the error, error code, resolution, and affected count

Metric	Baseline	Headroom
Input tokens	10,144	1,260
Correct answers	4/4	4/4
Compression	—	87.6%

SmartCrusher preserves first N items (schema), last N items (recency), all anomalies (errors, warnings), and statistical distribution.

QA Accuracy Preservation

Metric	Original HTML	Extracted	Delta
F1 Score	0.85	0.87	+0.02
Exact Match	60%	62%	+2%

Extraction Can Improve Accuracy

Removing HTML noise sometimes helps LLMs focus on relevant content.

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Limitations

What Headroom Does NOT Compress

• Short messages (< 300 tokens) — overhead exceeds savings
• Source code — passes through unchanged to preserve correctness (unless tree-sitter AST compression is enabled)
• grep/search results — compact structured format, already minimal
• Images — counted at fixed token cost (~1,600 tokens), not compressed as text
• System prompts — preserved for prefix cache compatibility

Known Overhead Sources

• Token counting (P90: 16ms) — runs tiktoken twice (before + after compression)
• Tree-sitter AST parsing (P90: 886ms) — expensive for large code files
• Kompress ONNX (P90: 576ms) — ML inference on CPU for text compression
• Content detection (Magika) — ML classification of content type

When Headroom Adds the Most Value

• Long agent sessions with accumulated tool outputs (40-80% compression)
• JSON-heavy workflows (API responses, database queries) — 83-94% compression
• Build/test output — 85-94% compression
• Multi-tool agents — 60-76% compression across tool results

When Headroom Adds Little Value

• Short conversational exchanges — median 4.8% compression
• Code-only sessions (reading/writing files) — code passes through
• Single-turn requests — no accumulated context to compress

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Methodology

Token-Level F1

Precision = |predicted ∩ ground_truth| / |predicted|
Recall = |predicted ∩ ground_truth| / |ground_truth|
F1 = 2 * (Precision * Recall) / (Precision + Recall)

Compression Ratio

Compression = 1 - (compressed_size / original_size)

A 94.9% compression means the output is 5.1% of the original size.

Production Telemetry

• Collected via anonymous beacon (no prompts, no content, no PII)
• Image-inflated instances excluded (base64 counted as text tokens — fixed in v0.5.18)
• Multi-worker beacon spam excluded (per-instance MAX, not SUM)
• Opt-in: HEADROOM_TELEMETRY=on (telemetry is off by default)

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Reproducing Results

# Clone the repo
git clone https://github.com/chopratejas/headroom.git
cd headroom

# Install with eval dependencies
pip install -e ".[evals,html]"

# Run all benchmarks
pytest tests/test_evals/ -v -s

# Run compression benchmark
python -c "from headroom import compress; print(compress([{'role':'user','content':'test'}]))"

# Run local proxy mode benchmark (no API calls)
python benchmarks/proxy_mode_benchmark.py --turns 12 --show-real-harness

# Replay local Claude Code transcripts (no API calls)
python benchmarks/claude_session_mode_benchmark.py --workers 1

# Compare two refs on the same local Claude transcript corpus
python benchmarks/claude_session_branch_compare.py --left-ref upstream/main --right-ref HEAD --recent-turns-per-session 200 --workers 1

This benchmark compares token vs cache proxy modes on the same synthetic conversation:

• token should show higher compression.
• cache should preserve prior-turn stability and can win in long sessions with strong prefix-cache reuse.

--show-real-harness prints optional steps for running the same comparison with Claude Code, but does not call APIs by default.

claude_session_branch_compare.py runs the real local session replay benchmark twice, once per git ref, in isolated worktrees. It writes:

• per-ref replay outputs under benchmark_results/branch_compare/<label>/
• a combined comparison report under benchmark_results/branch_compare/

Use it when you want a clean PR-vs-main comparison on the same transcript slice.

For a deterministic cache-busting proof case, run:

python benchmarks/synthetic_token_cache_bust_report.py

That synthetic replay forces token mode to retroactively rewrite a prior tool result on the second turn while cache mode remains stable. Use it to verify the simulator can distinguish:

• token: history rewrite + cache bust
• cache: no rewrite + no bust

For a reproducible local report bundle that combines:

• full real-session replay summaries
• local-only processed real input/output excerpts
• synthetic token-bust proof
• synthetic long-form stress tests

run:

python benchmarks/cache_validation_bundle.py --workers 1 --output-dir benchmark_results/cache_validation_bundle_full

Notes:

• By default the bundle is redaction-safe for sharing:
• real processed reports redact transcript-derived content excerpts
• manifest paths are redacted
• To include local processed content excerpts for private review on your own machine:

python benchmarks/cache_validation_bundle.py --workers 1 --include-content

• The bundle writes:
• index.html / index.md: top-level summary and links
• bundle_manifest.json: runtime metadata + corpus fingerprint
• real/: full real-session replay reports
• real_processed/: processed before/after excerpts from real transcripts
• synthetic_token_bust/: minimal explicit cache-bust proof
• synthetic_long_suite/: long deterministic rewrite/TTL scenarios
• Checkpoints are scoped under the bundle output directory and fingerprinted by the selected corpus so stale runs do not contaminate new results.

The Claude session benchmark replays local transcript data from ~/.claude/projects through baseline, token, and cache modes. It estimates raw tokens, cache read/write tokens, paid input/output costs, and prompt-window winners under two assumptions:

• cached tokens count against the model window
• cache reads do not count against the model window

Notes:

• It writes local output to benchmark_results/, which is gitignored.
• It is intentionally conservative on memory. Run with --workers 1 for the most stable full-corpus replay. Higher worker counts increase memory use.
• It uses transcript-visible messages only. Hidden Claude Code system/tool schemas are not available in the local .jsonl files, so the numbers are comparative estimates rather than exact provider billing replicas.