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The fairest measure of an AI coding agent is not how few tokens it uses. It is how many tasks it completes correctly, without regressions or avoidable human repair, for a known cost and elapsed time. Measure each repeated run as a bundle of verified task outcome, provider-priced token usage, tool activity, retries, latency and human intervention. Then report cost per successful task, success at a fixed budget, token distributions and reliability—not a single average from one run.
This approach separates a cheap failure from an efficient success and makes comparisons reproducible when model versions, prompts, caches, scaffolds and benchmark tasks change.
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What “efficient” means for an AI coding agent
Efficiency is a constrained optimization problem: maximize verified software work while controlling spend, time and supervision. A run is successful only when it satisfies a written rubric, passes the relevant automated checks and leaves unrelated behavior intact. The primary business metric is therefore cost per successful task, not cost per request or tokens per request.
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Use outcome metrics alongside resource metrics
- Cost per successful task: total measured spend divided by tasks that meet the success rubric.
- Success at a fixed budget: successful tasks divided by all tasks attempted under a stated dollar or token ceiling.
- Tokens per successful task: total tokens divided by successful tasks. You can also report the reciprocal, successful tasks per token; label which form you use.
- Wall-clock completion time: elapsed time from task start to a verified result, including waiting, tool execution and retries.
- Tool-call efficiency: tool calls required per successful task, with failed calls and repeated calls visible.
- Reliability: successful runs divided by repeated runs on the same task, with a confidence interval.
- Human rework: review, edits, rollback or debugging effort required after the agent reports completion.
An agent that emits fewer tokens but fails more often can have a worse cost per successful task. An agent with a high success rate can still be inefficient if it rereads large contexts, invokes tools repeatedly or retries unstable commands.
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Freeze the unit of work before measuring
Comparisons become meaningless when one agent receives a vague request and another receives a fully specified issue. Select a fixed issue, feature request, test task or repository question and attach a written success rubric before any run begins.
Record the experimental identity
- Repository state: record the exact commit, branch and dependency state. A changed repository is a different task.
- Task text and acceptance criteria: preserve the prompt exactly, including requirements that tests cannot express.
- Allowed tools: list shell commands, editors, network access, package installation, test runners and any APIs available to the agent.
- Timeout and resource limits: specify maximum wall-clock time, token ceiling, dollar ceiling, concurrency and sandbox limits.
- Model and agent version: record the model identifier, agent release, system instructions, reasoning setting, context-window configuration and scaffold.
- Harness and evaluator version: pin the orchestration code, test commands and result parser so a later evaluator change does not look like an agent improvement.
Keep these fields attached to every run. A leaderboard number without model identity, scaffold, reasoning setting, cache policy and benchmark version cannot be reproduced reliably.
Capture complete telemetry
Log every billable and operational event, not only the final response. Artificial Analysis reports token classes and pooled per-task-attempt averages; its approach illustrates why the classes must remain separate. If a provider does not expose a field, mark it as unavailable and exclude that value from calculations rather than treating it as zero.
| Telemetry field | What to record | Why it changes the result |
|---|---|---|
| Input tokens | All uncached prompt and context tokens for each request | Large repository context can dominate spend even when output is short. |
| Cached-input tokens | Tokens served from provider cache | Cache-read rates may differ from ordinary input rates. |
| Cache-write tokens | Tokens written to a cache, when billed | Writing context can create a separate charge. |
| Reasoning tokens | Hidden or separately reported reasoning usage | Reasoning may be billed independently and can vary sharply between runs. |
| Output tokens | Visible model output for every request | Plans and models can price output differently from input. |
| Tool calls | Tool name, arguments, start and finish time, exit status and returned bytes | Repeated searches, tests and failed commands add latency and often trigger more model turns. |
| Retries | Automatic and agent-initiated retries, with the reason | A retry is part of the task cost, even if the final patch succeeds. |
| Wall-clock time | Start, finish, queue delay and tool duration | Two runs with equal tokens can have very different delivery times. |
| Tests and exit codes | Exact command, output summary, exit code and environment | Success requires an auditable verification trail. |
| Human intervention | Edits, prompts, approvals, rollbacks and review time | A superficially successful run may transfer work to a person. |
Store raw events as well as aggregates. Raw records let you recompute spend when a provider changes a rate or when a cache classification is corrected. Include timestamps so the price schedule that applied at run time is unambiguous.
Calculate provider-priced spend
For each request, multiply every token class by the rate that applied when the request ran, then sum all requests in the run:
Run cost = Σ(input tokens × input rate + cached-input tokens × cached rate + cache-write tokens × cache-write rate + reasoning tokens × reasoning rate, if separately billed + output tokens × output rate).
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Apply the same accounting to retries and tool-triggered requests. If a provider bills a tool or execution service separately, add that charge to the run ledger rather than hiding it inside a token estimate. Report both cost per attempt and cost per successful task:
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Rates, model catalogs and cache policies change. Save the price sheet or rate identifier with the run date, region and currency. A current price should never be presented as permanent.
Do not mix subscription and API accounting silently
A consumer subscription is not directly comparable with pay-per-token API usage. A subscription may have a flat fee, usage limits, queueing rules or undocumented allocation. If you compare it with API runs, state the normalization explicitly—for example, an allocated monthly fee divided by measured tasks—and show the assumptions. Otherwise, present the two cost models separately.
Define success and regression safety
Automated tests are necessary but not sufficient. Use tests for behavior that can be executed, and a review rubric for requirements such as API compatibility, migration quality, security properties, documentation or maintainability.
Use a two-part test gate
SWE-bench provides a concrete pattern: a patch must pass the FAIL_TO_PASS tests for the target issue and the PASS_TO_PASS tests that protect unrelated behavior. Adopt the same logic for internal tasks:
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- Target tests must fail before the fix and pass afterward.
- Regression tests that passed before the run must continue to pass.
- Static checks, type checks, linters and build steps belong in the rubric when the repository uses them.
- Human review must record which acceptance criteria are not represented by tests.
Count a task as successful only when every required gate passes. A patch that fixes the reported bug but breaks an unrelated module is not a successful task; classify it as a regression and retain its full cost.
Separate task success from run success
If an agent makes several attempts on one task, define whether your denominator is tasks or attempts. For product planning, cost per successful task usually counts one task once, even if it required retries. For system diagnostics, also publish attempt-level success and retry rate so reliability problems remain visible.
Repeat runs to expose variance
One run is an anecdote. Run each agent variant multiple times on the same task set with identical starting commits and settings. Randomness, context ordering, tool failures and model-serving changes can produce materially different outcomes.
Report distributions, not just means
- Success rate with a confidence interval.
- Median, p90 and p95 cost per attempt and cost per successful task.
- Median and p95 wall-clock latency.
- Token distributions by input, cache, reasoning and output class.
- Retry and regression rates.
- Tool calls per successful task and human minutes per task.
Percentiles matter because a team experiences the expensive tail, not just the average. A median-fast agent with rare, extremely long retries may be a poor fit for a strict delivery window.
Keep the task split visible
Publish per-task results as well as pooled averages. A pooled score can hide that one agent excels on small fixes while another handles large refactors. Include the number of tasks attempted, the number meeting every success gate and the confidence interval method used.
Compare agents on a cost-success frontier
Use several axes rather than one ranking. Plot verified success against spend for each variant and identify the Pareto frontier: a point is dominated when another configuration is at least as successful and no more expensive, or at least as cheap and no less successful.
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| Comparison axis | Recommended report | Decision it supports |
|---|---|---|
| Verified task success | Successful tasks and success rate, with target and regression gates | Whether the agent can deliver acceptable patches. |
| Cost per successful task | Total measured spend divided by successful tasks | Budget required for dependable output. |
| Success at fixed budget | Success rate under a stated dollar or token ceiling | Which agent fits a spending limit. |
| Token mix | Input, cached, cache-write, reasoning and output distributions | Where optimization or prompt reduction may help. |
| Latency | Median and p95 completion time, including tools and retries | Whether the agent meets interactive or batch deadlines. |
| Retry rate | Retries per attempt and proportion of runs needing one | Operational stability and hidden spend. |
| Regression rate | Runs that pass the target but fail unrelated checks | Risk of merging apparently successful patches. |
| Tool calls per success | Total calls and failed calls divided by successful tasks | Whether the scaffold explores efficiently. |
| Human rework | Review minutes, edits, rollback and follow-up prompts | True labor cost after the agent stops. |
Keep model identity, agent scaffold, reasoning setting, context window, cache policy and benchmark version visible next to every point on the chart. Changing any of them creates a new variant.
Interpret token use and reasoning settings carefully
More reasoning can improve a difficult fix, but token volume is not a quality guarantee. Bai and colleagues reported up to a 30× variation in total tokens across runs of the same task and found that higher token use did not reliably improve accuracy. Their study also found about 1,000× more tokens for agentic coding than for code reasoning or code chat; treat that as the study’s comparative result, not a universal multiplier for every workflow.
Use reasoning settings as experimental factors. Hold the task, scaffold and limits constant, then compare success, cost and latency distributions. A setting that raises reasoning tokens but lowers regression rate may be worthwhile; a setting that only increases retries or produces longer explanations is not automatically better.
Audit benchmark validity before trusting a score
Benchmarks measure the benchmark as configured. Record task provenance, benchmark version, contamination controls, scaffold, model settings, evaluator version and the policy for broken tasks.
OpenAI’s July 8, 2026 audit found widespread issues in SWE-bench Verified and estimated that about 30% of SWE-bench Pro tasks were broken. That estimate is a warning to inspect task validity rather than treating a benchmark label as proof of agent quality.
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Build a reproducible measurement workflow
- Prepare the task set: choose representative issues, freeze commits and write acceptance and regression criteria.
- Pin the configuration: record model, agent version, system prompt, reasoning setting, context policy, tools, timeout, cache policy and harness version.
- Instrument every request: capture all token classes, rates, tool calls, retries, timestamps, test commands, exit codes and human actions.
- Run a clean baseline: execute the tests before the agent starts to verify the repository and identify pre-existing failures.
- Run repeated trials: use the same task set and limits for each variant; preserve failed runs rather than rerunning only until success.
- Verify outcomes: execute target and regression tests, apply the review rubric and classify pass, failure, regression, timeout or infrastructure error.
- Compute metrics: calculate run cost, cost per successful task, success at budget, token efficiency, latency, retry rate and human rework.
- Inspect outliers: open the raw trace for unusually expensive, fast, long or successful runs before drawing conclusions.
- Publish uncertainty: include confidence intervals, percentiles, task-level results and all exclusions with reasons.
Common measurement failures and their fixes
Counting missing telemetry as zero
If reasoning or cache tokens are absent from a trace, the apparent cost is understated. Exclude the incomplete measurement from that metric or label the result as partial; never convert an unknown quantity to zero.
Optimizing tokens before verifying outcomes
Short prompts, early stopping and aggressive context trimming can reduce usage while lowering task success. Gate optimization on unchanged or improved verified success and regression safety.
Ignoring cache reads and writes
Cached-input and cache-write rates can differ from ordinary input pricing. Keep them as separate fields and apply the rate that actually billed each class.
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Discarding failed attempts hides retries, latency and spend. Include every attempt in cost and reliability calculations, then identify the successful one for outcome scoring.
Mixing broken tasks with agent failures
A malformed benchmark issue, missing fixture or evaluator defect should be classified separately. Document the exclusion rule and retain the original task record so another evaluator can audit it.
Changing the scaffold between agents
Prompt wrappers, retrieval, tool permissions, context limits and retry policies can matter as much as the underlying model. Compare like with like or report each scaffold as its own treatment.
What a defensible result looks like
A useful report lets a reader answer five questions without guessing: How many tasks succeeded under the full rubric? What did each attempt cost, including cache and reasoning charges? How variable were tokens and latency? How often did the agent retry, regress or require human repair? And are the benchmark and evaluator trustworthy for this decision?
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