compression with a quality contract

Distil vs everything else

Benchmark measures savings and decision-equivalence head-to-head; this page covers the other axis — what each tool actually guarantees, and what happens to real task success on a real harness. Competitors here are real, shipping tools, and the honest gap is narrower on savings than on proof.


Capability matrix

Marked from what each project documents or ships, not from marketing. Distil's own claims are the ones audited throughout this site — see Concepts and Deploy & Security for the code behind each ✔.

CapabilityDistilHeadroomrtkLLMLingua-2Provider caching
Per-step statistical certificate DERC (Learn-Then-Test / CRC) none published none published raw score deltas, not an equivalence test — not a compressor
Trajectory-level (task-outcome) certificate distil certify-trajectories — not a compressor
Reversible (bytes recoverable on demand) Tier-1 digest + handle, distil_expand documents a local store + headroom_retrieve; the compression itself is lossy in-context (the configuration benchmarked in E8) lossy filtering (strips boilerplate; keeps raw logs on failure) lossy trivially — nothing is removed
Live shadow decision-equivalence on real traffic --shadow / shadow-stats not published not published not published — no decision risk to measure
SSE streaming pass-through (TTFT preserved) chunk-by-chunk relay n/a — library, not an HTTP relay n/a — CLI output proxy, not an LLM API relay n/a — library, not an HTTP relay n/a — no proxy layer in the path
Savings ledger + status line local ledger, leaderboard/statusline not published not published not published — provider billing dashboard covers this natively
Multi-tenant gateway (per-tenant accounting) distil gateway not published not published not published — orthogonal, provider-side
What each does well. Headroom is a real, adopted compressor with a useful whole-conversation optimizer and a ModernBERT relevance scorer — and on the live head-to-head, the cheaper compute path (Distil's path has no model either, but Headroom's scorer is lighter than a full digest cycle in some configs). rtk solves a different problem well: stripping known boilerplate from wrapped CLI commands (git, ls, psql, aws…) fits shell-heavy agents, which is why it's adopted there. It doesn't compress arbitrary agent context, so it isn't a head-to-head contender (see the note on Benchmark). LLMLingua-2 posts genuinely high raw compression ratios and is the most-cited method in this space for a reason. Provider prompt caching is zero-risk by construction — it doesn't touch what the model reads, only what you pay to re-send it — and composes cleanly with everything above, including Distil, whose cache-aware compression exists to keep that discount intact rather than compete with it.

Real task success — SWE-bench Verified, official harness

Capability tables can't settle whether any of this holds up on a real, long-horizon coding agent. E8 runs six conditions of the identical ReAct agent (only the compressor differs) end-to-end on the full 500-instance SWE-bench Verified set, scored by the official swebench harness (hidden tests, per-instance Docker) — not a proxy metric.

ConditionTask successTied with full context?Reversible + certified?
Distil (gated + surprise digest, v1.7) 42.0% ✔ +2.8pp over full (paired CI −0.6..+6.2pp)
Distil (relevance-gated, E8) 36.8%
Headroom (lossy) 32.6% ✘ −6.6 pp
LLMLingua-2 (lossy) 2.4% ✘ −36.8 pp
No compression (full) 39.2%

500-instance long-horizon ReAct agent, full SWE-bench Verified, official swebench harness (hidden tests, per-instance Docker), paired McNemar across all six conditions. Distil's gate is the only condition statistically non-inferior to full context (−2.4 pp, 95% CI [−5.7, +0.9], McNemar p=0.19) and beats Headroom with significance (+4.2 pp, p=0.035). rtk was not run in this harness — it does not compress arbitrary agent context (see above), so it has no comparable condition. Full methodology, CIs, and the recovery-round-trip ablation: Research → E8.

Honest scope. This table is task success on one harness (SWE-bench Verified), one agent design, one base model (claude-haiku-4-5) — not a claim that Distil wins every workload (see E7 for the negative result that motivated the trajectory-level certificate). It's a claim that when someone measured end-to-end outcomes instead of raw compression ratios, the guarantee-carrying compressor led on the number that pays the bills.