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What the comparison needs to prove
Prompt compression can reduce the context sent to a model, but that alone does not show that a coding agent is cheaper overall or still produces correct patches. An agent may make multiple model calls, use tools, retry after failures, and incur separate compression costs. The comparison therefore needs to answer two questions: how often does each setup solve the task, and how much does each successful solution cost?
A useful example is Dasein Labs’ Code-Compression Bench, which describes a run using one coding-agent scaffold, one model, 100 SWE-bench Verified tasks, and the official Docker grader. Its README says, “This benchmark fixes everything except the compression layer.” That is a concrete project design, not a universal sample-size recommendation or an industry standard. See the Code-Compression Bench project.
Design a fair paired experiment
Define exactly what changes
Describe the compression treatment before running it: what content is compressed, when the compressor acts, what information remains available to the agent, and whether compression uses a separate model call or other billable compute. Compare that condition with an uncompressed baseline. Keep all other parts of the setup fixed so the compression layer is the meaningful difference.
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Keep the two arms aligned
Use the same model version, agent implementation and scaffold, tools and permissions, task instances, execution environment, time and turn limits, and grading rules in both arms. A paired task set—each task attempted once in each condition—makes the comparison easier to interpret because repository and issue difficulty are shared rather than split between groups.
Choose tasks that match the claim
Name the benchmark and version, the number of tasks, and any inclusion or exclusion rules. Results apply most directly to the repositories, languages, issue types, and difficulty mix actually tested. A result on one coding benchmark should not be presented as proof that compression has the same effect on every agent workflow.
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Define and grade success before the run
Set the success criterion in advance. For a benchmark with a reproducible grader, use its specified evaluation process; the Code-Compression Bench example uses the official SWE-bench Docker grader. If human review is required, write down a rubric before inspecting results. Do not treat a shorter prompt or a plausible-looking patch as a solved task without checking it against the chosen criterion.
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Measure the cost of the complete agent trajectory
Log each task from its first model or compressor call through completion or failure. For every call, capture input and output tokens, cache reads and writes when the provider exposes them, compressor calls, tool activity, retries, elapsed time, and provider-billed cost. Sum the charges for the whole run, including compression overhead.
Use billed cost rather than assuming all input tokens have the same price. In multi-turn runs, context can be sent repeatedly, and cached input may be charged differently from fresh input. The Code-Compression Bench explicitly ranks methods using cache-aware cost per solved task; this is why a one-shot token reduction cannot stand in for end-to-end savings.
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Report solve rate and cost per solved task
For each condition, publish the number of tasks solved and the solve rate, alongside total billed cost and latency. Calculate cost per solved task as total billed cost divided by the number of tasks solved. If no tasks are solved, report that the ratio is undefined rather than presenting a misleading value. Also show paired task outcomes so readers can see which tasks changed from solved to unsolved or vice versa.
Include compression ratio or token reduction as a diagnostic measure, not as the main result. A compression method is not an improvement if its token savings are offset by compressor calls, retries, longer runs, or lost task completions.
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Compare multiple compression options consistently
Run every candidate against the same uncompressed baseline and use the same task set, grader, and run conditions. A comparison table can make the trade-offs explicit:
| Measure | What to report |
|---|---|
| Task success | Tasks solved, solve rate, and paired task outcomes |
| End-to-end economics | Total provider-billed cost, including compression and cache-aware input charges, plus cost per solved task |
| Runtime | Wall-clock latency under the stated run limits |
| Agent behavior | Relevant workflow, tool-use, retry, timeout, or other execution failures |
| Compression diagnostics | Token reduction or compression ratio, kept distinct from task success and billed savings |
There is no established universal weighting that makes one of these measures decisive for every team. State the decision rule before seeing the results—for example, whether a small solve-rate decline is acceptable for a cost reduction, or whether solution quality must remain within a defined margin.
Check agent behavior beyond the final patch
If the intended use involves tool-driven or long-running work, track behavior such as tool-use failures and workflow completion as well as whether the final task passes. ACBench was designed to assess agentic abilities beyond conventional language-model and language-understanding metrics. Its 2025 paper describes a benchmark spanning 12 tasks across four capabilities and 15 models; those figures describe ACBench’s scope, not a required recipe for a prompt-compression trial. Read the ACBench paper.
State the limits and uncertainty
Publish the task count, number of repetitions, and the observed variation. A small difference may not be reliable; the available sources do not establish one universal sample size or statistical test. Preserve per-task outcomes and raw run logs so the aggregate solve rates and cost calculations can be checked.
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