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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →A fine-tuned coding model is better only if it improves the work you intend to use it for. Compare it with the exact base checkpoint on held-out tasks that represent that work, keep the evaluation conditions matched, inspect task-level results and uncertainty, then verify the gain in the target workflow. A higher score on one public benchmark is not enough.
Define what “better” means for your use case
Start by describing the work the model is supposed to do and what counts as success. A fine-tune for fixing bugs in an existing repository should be evaluated on repository repair, not declared successful solely because it improves at writing short functions from a prompt.
Before running the comparison, record the target languages and repository types, prompt style, whether the model runs alone or inside an editor or agent loop, available tools, and success criteria. Choose a primary metric and define which regressions would be unacceptable before you see the scores. This prevents a favorable benchmark result from becoming the definition of “better” after the fact.
Compare the fine-tune and base model under matched conditions
Use the exact base checkpoint from which the fine-tune was made, if it is available. Hold the evaluation setup constant: task prompts and templates, decoding parameters, samples per task, context limits, tools, timeout, dependencies, hardware and runtime class. Record model versions and checkpoint hashes so the comparison can be reproduced.
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If you are evaluating a model-plus-agent product, keep the agent scaffold fixed too. If the scaffold changes, measure that as a separate comparison; otherwise you cannot tell whether a result came from the fine-tuned model or from different orchestration. For repository tasks, setup details matter: the SWE-bench documentation describes applying patches and running issue-fixing and regression tests, so environment variation can cause failures unrelated to the patch.
Choose tasks that represent the work—and test more than one capability
Build a task mix around the intended workflow. Short standalone synthesis tasks test functional correctness on compact problems. Repository issue repair tests whether a model can understand an existing codebase and produce a patch that passes both issue and regression tests. Add self-repair, execution reasoning, or test-output prediction only when those capabilities matter to the product.
No single benchmark covers all of these. LiveCodeBench, for example, proposes collecting newly published contest tasks over time and evaluates capabilities beyond code generation. A public static benchmark can provide a stable reference, but the decision should also include a private, held-out set. If you draw tasks from a real codebase or customer workflow, remove sensitive information and keep the final evaluation set separate from fine-tuning and prompt development.
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Check that the tasks and tests measure the intended behavior
A passing test suite is only useful evidence if the task statement and tests agree about what the fix must do. Review tasks for hidden requirements, tests that enforce incidental implementation details, weak tests that let incomplete fixes pass, misleading prompts, broken dependencies, and failures caused by the runtime instead of the generated code. For important comparisons, manually inspect a sample of wins, losses, and apparent ties.
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Recent audits show why this validation matters. In OpenAI’s 2026 review of SWE-bench Verified, 59.4% of the 138 audited tasks had material issues in test design or problem descriptions. The audit covered tasks that o3 did not consistently solve over 64 independent runs; it is not a random estimate for all tasks or benchmarks. In its 2026 SWE-Bench Pro audit, OpenAI flagged 27.4% of tasks in its pipeline-reviewed set as likely broken and identified 34.1% of tasks in its human-annotated set as broken. Those findings apply to the audited versions and subsets, not every task in either benchmark. See OpenAI’s SWE-bench Verified review and its SWE-Bench Pro evaluation audit.
An automated judge can help prioritize cases for review, but it cannot establish that the benchmark is valid. Treat questionable failures as evaluation problems to investigate, not automatic evidence that one model is worse.
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Control for benchmark exposure and sampling budget
Widely published problems, repositories, solutions, and release notes may have appeared in training data. Prefer private or post-training-cutoff tasks where possible, keep the final holdout undisclosed, and do not use it to tune prompts or hyperparameters. Record what is known about the model’s training-data cutoff and benchmark exposure. If an output closely reproduces a distinctive known solution, investigate rather than treating it as uncomplicated evidence of general ability.
Also fix and report the generation budget. In the 2021 Codex paper, the authors reported 28.8% of HumanEval problems solved at one setting and 70.2% when using 100 samples per problem. Those historical results illustrate how much repeated sampling can change a score; they are not expected scores for current models or a current model ranking. State whether you report pass@1 or use multiple samples, how many samples each task receives, and how a result is selected from them. The relevant source is the Codex paper.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesBenchmark scores can also shift as models and evaluation conditions change. OpenAI’s July 2026 SWE-Bench Pro audit reported a frontier-model pass-rate change from 23.3% to 80.3% on its 731-task public split over eight months. That is not a controlled comparison of one model and does not establish that the benchmark remained valid; it is a reason to report the benchmark version and evaluation date alongside results.
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Report uncertainty and inspect what changed
Publish the task set’s identity and version, number of tasks, task-level outcomes, aggregate metric, sampling policy, and uncertainty. A small aggregate difference can be misleading if it depends on a few tasks or varies across runs. Because the base and fine-tune answer the same tasks, use an uncertainty analysis that respects this paired design; if generation is stochastic, use repeated runs or samples as appropriate.
Do not stop at the aggregate. Break results down by relevant task categories and examine representative successes and failures. This reveals whether the fine-tune improved repository repair while regressing on another important language or task type, or whether the apparent gain is concentrated in a handful of examples.
If code quality beyond test passing matters, add a blinded human comparison with a written rubric. Hide model identity, randomize output order, and allow reviewers to call a tie. Keep those preferences alongside functional correctness rather than using them as a substitute for execution tests. HumanEval.org’s methodology documents bootstrap confidence intervals for its blind preference leaderboard; its precise rating procedure applies to that leaderboard, but it is a useful example of making uncertainty visible.
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Measure the result at the level where you will use the model
A benchmark gain is a reason to run a realistic pilot, not by itself a deployment decision. Try the model on a small set of representative workflow tasks and choose outcome measures in advance. Depending on the work, track accepted task completion, regressions, human review effort, elapsed time, or compute per accepted task. Adapt these measures to your setting; there is no universal production KPI set.
| Evaluation layer | What it tells you | What it does not establish alone |
|---|---|---|
| Held-out functional tests | Whether outputs satisfy the tested behavior under the stated conditions. | That the task set reflects your workflow or that code is maintainable. |
| Repository issue and regression tests | Whether patches work in the evaluated codebase and avoid the tested regressions. | That all real repository tasks are represented or tests are valid. |
| Task-category and uncertainty analysis | Where gains or losses occur and how stable the measured difference appears. | That a benchmark gain improves day-to-day outcomes. |
| Workflow pilot | Whether the model helps in the target process, including review and operating costs that matter there. | That results will generalize to every team, repository, or future task. |
Keep model-only results separate from full agent-system results, and keep human-rated usefulness separate from functional correctness. This makes it possible to see what improved and whether that improvement is valuable in practice.
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