SWE-bench Verified vs Terminal-Bench

The task unit determines the question

SWE-bench Verified starts from a software issue linked to an existing repository. The agent must inspect the code and produce a change intended to resolve that issue, after which the benchmark harness checks whether the patch satisfies the associated tests. Its Verified subset contains 500 instances that human annotators reviewed for clarity, test quality and solvability. This curation is useful evidence about the dataset design, but it does not transform the set into a random sample of bug reports or a complete measure of software engineering.

Terminal-Bench takes a broader command-line framing. Its paper describes 89 curated tasks, each packaged with instructions, a container image, tests and a human-written solution. A task may require a sequence of environment operations rather than a single repository patch. The benchmark therefore asks how an agent performs in selected terminal-based work under the specified environment. The paper itself notes remaining sources of variation, including hardware and external-service behavior, so packaging helps repeatability without making every run perfectly deterministic.

Validation is shaped by the grader

A grader is part of the benchmark definition. In SWE-bench Verified, tests are connected to the issue and repository; in Terminal-Bench, task tests inspect whether the requested state was achieved in the environment. Passing provides a bounded result under those tests. It cannot guarantee that a change is maintainable, that an unstated requirement was met, or that production systems remain secure. A test can be too narrow, and a task can omit organizational constraints that matter in practice.

The benchmark sources also describe different ways to select and package tasks. SWE-bench Verified reports human review of the issue and test patch; Terminal-Bench reports curated environments and human-written solutions, with manual verification used to check solvability and shortcuts. These are design choices that affect what a score means. They are not an identical validation pipeline, and a comparison should not assume that two percentage values represent the same difficulty or error tolerance.

Why published scores do not form one ranking

Model results can depend on much more than the model family: exact version, agent scaffold, tool permissions, repository state, time and token budgets, network access, retry policy, and whether the run had human assistance. SWE-bench’s own site distinguishes broad agent leaderboard results from a constrained language-model comparison setup. Terminal-Bench includes its own environments and task-specific checks. A result without these conditions is not enough to reproduce or compare the run.

Even when an organization has results on both benchmarks, the difference between scores cannot be read as a gain or loss in general coding ability. The tasks differ in scope, data and evaluation. The count of 500 versus 89 is not a direct measure of difficulty or statistical reliability. If a report supplies only an aggregate, a small margin should not be treated as decisive without uncertainty and per-task information. Keep every score attached to its exact release and harness.

Select a benchmark for the work you need

A team maintaining a large application may care most about issue resolution across its own repositories, build system and review policies. A team that expects an agent to configure environments, run tools or complete a multi-step terminal workflow may want tasks closer to those operations. The first question is not which benchmark is more prestigious; it is which test family resembles the job. Local validation remains necessary because internal dependencies, permissions, secrets handling, deployment rules and rollback procedures may be absent from public benchmark containers.

Build an evaluation set with held-out examples and an acceptance rule that reflects the actual workflow. Pin the model and scaffold, provide the same tools, set a fixed budget, log retries and human interventions, and run the tests under controlled conditions. Review patch quality and security separately from task completion. For an agent that can change files or call services, include authorization checks, denied actions, interruption and recovery. Record the exact grader and source revision so future results remain interpretable.

For teams in India, the useful adaptation is to select tasks from the repositories, programming languages and build systems the local team actually maintains. The benchmark descriptions do not provide an India-specific language or infrastructure slice, so local performance cannot be inferred from their global aggregate results. A team can use the public benchmark designs to structure its own held-out evaluation, but should report that local test separately and avoid describing it as a direct replication unless the same task set and harness were used.