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Run a paired, controlled comparison: have the same coding agent solve the same tasks with and without compression, then compare verified task success with the full billed cost of each run. Token reduction alone cannot show that the agent stayed accurate or that the complete workflow became cheaper.
What a valid comparison measures
Prompt compression changes the context presented to an agent, but coding-agent performance depends on more than prompt size. The agent may make several model calls, use tools, retry after errors, and incur different charges for cached and uncached input. A meaningful result therefore needs both a reproducible measure of task completion and an end-to-end accounting of the run.
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Keep the uncompressed baseline and compressed condition identical in every material respect except the compression layer. Compare solve rate and cost per solved task together: a lower cost per solve can conceal a drop in the number of tasks solved, while a smaller prompt says nothing by itself about correctness.
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Define the compression treatment
Document what is compressed, when the compression runs, and what information remains available to the agent. Include any extra compressor model calls or compute in the treatment’s cost. If compression happens repeatedly during a task, log each invocation rather than treating it as a one-time prompt transformation.
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Hold the other conditions constant
Use the same model version, agent implementation and scaffold, tool permissions, task instances, execution environment, grading criteria, and run limits in both conditions. Pair the runs by task: each task should be attempted once with the baseline and once with compression. This makes it easier to see which individual tasks changed outcome instead of relying only on two aggregate rates.
A concrete example is Dasein Labs’ Code-Compression Bench. Its project description reports a setup with one coding-agent scaffold, one model, 100 SWE-bench Verified tasks, and the official Docker grader, changing the compression layer. The 100-task count describes that project’s run; it is not a universal sample-size recommendation.
Choose tasks that match the claim
Name the benchmark and version, task count, and any inclusion or exclusion rules. Results apply most directly to the tested repositories, languages, issue types, and difficulty mix. A benchmark with a narrow task mix cannot establish that compression behaves the same way across every coding workflow.
Define accuracy as task success, not prompt size
Choose the success criterion before running the comparison. Prefer a reproducible benchmark grader when one is available; otherwise, define a human-review rubric in advance and apply it consistently to both conditions. For coding tasks, the criterion should determine whether the submitted change actually meets the task requirements, not merely whether the agent produced a patch.
Record distinct failure types separately. A test failure, invalid patch, timeout, and infrastructure failure do not necessarily mean the same thing. Separating them helps distinguish a compression-related quality regression from a broken environment or an interrupted run.
Final task completion may not capture every change in agent behavior. If workflow reliability matters, record tool-use failures, unusual retries, or other failures relevant to the task. ACBench was designed to evaluate agentic abilities beyond conventional language-model and language-understanding metrics; its paper describes 12 tasks across four capabilities and 15 models. That scope supports looking beyond a single language-model score, but it is not itself a universal recipe for evaluating every prompt-compression system. See the ACBench paper.
Count the complete cost of each agent run
Use actual provider-billed cost where available, covering the full agent trajectory rather than only the initial prompt. Log model and compressor calls, input and output tokens, cache reads and writes when reported, tool activity, and retries. Cached and fresh input may have different charges, so a total based only on token counts can misstate spend.
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Include compression overhead: the calls or compute required to shorten context are part of the cost of using compression. Also record wall-clock time. A condition can reduce billed spend while adding enough delay to matter to the workflow, or incur extra calls that erase the apparent savings from shorter prompts.
Report solve rate and cost per solved task together
For each condition, report the number of tasks attempted and solved, the solve rate, total billed cost, and cost per solved task. Show paired task outcomes as well, so readers can see whether compression fixed, broke, or left each task unchanged. Include latency and token reduction as supporting measures; token reduction is a diagnostic, not the result that determines whether the method is useful.
Calculate cost per solved task as total billed cost divided by the number of tasks solved in that condition. State the denominator and cost scope clearly, including whether compressor calls and retries are included. If a condition solves no tasks, cost per solved task is undefined; report that outcome instead of implying a finite ratio.
The Code-Compression Bench project ranks methods by cache-aware cost per solved task. That is a useful way to make quality and economics visible in one metric, provided solve rate is also reported. Do not treat the project’s ranking choice as a universal standard: teams should decide in advance how they will weigh success, cost, latency, and any workflow requirements.
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Report the task count and number of repetitions, and describe how runs were paired. Small observed differences may reflect variation between runs rather than a stable effect. Use an uncertainty analysis appropriate to the design before making a strong claim. The sources cited here do not prescribe one universally correct sample size or statistical test.
Do not assume that a single-shot compression result predicts savings in a multi-turn coding agent. A 2026 preprint distinguishes single-shot compression evaluation from multi-turn agent cost, but its available abstract does not establish detailed quantitative guidance. Treat the distinction as a reason to measure the full trajectory directly, not as evidence for a particular savings estimate: the preprint abstract.
Use a decision rule that matches your use case
Before seeing results, decide what trade-off is acceptable. For example, a team may require that solve rate not fall while total cost per solved task improves, or it may accept a small quality change in exchange for a sufficiently large cost or latency benefit. The right threshold depends on the application; the available evidence does not establish a universal required saving or weighting.
Quick Recap
- Keep the model, scaffold, tasks, tools, environment, grader, and run limits matched.
- Include compressor calls, retries, and cache-aware input charges in billed cost.
- Report solve rate, paired outcomes, total cost, cost per solve, and latency.
- Limit conclusions to the task types and setup actually tested.
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