Terminal-Bench vs SWE-bench: AI Coding Benchmarks Comparison

What You'll Learn

• What SWE-bench and Terminal-Bench actually measure
• How their datasets, environments, tasks, and scoring differ
• Why a benchmark percentage should not be treated as a production guarantee
• How to design a benchmark-aware evaluation for your own coding workflow

Terminal-Bench vs SWE-bench: the short answer

Terminal-Bench vs SWE-bench is not a direct race on one shared task. SWE-bench focuses on software engineering issues from GitHub. Terminal-Bench focuses on terminal mastery across difficult tasks that resemble real workflows. One benchmark is closer to issue resolution in a repository. The other is closer to an agent operating a computer environment over a longer task.

SWE-bench starts with a repository and an issue. The model must generate a patch and the evaluation harness applies it before running tests. Terminal-Bench starts with a task environment, instructions, tests, and a time limit. The agent must use the terminal and leave the environment in the required end state.

Both benchmarks are useful because they move beyond short code-completion examples. They are not interchangeable, and their scores should not be placed in one ranking without recording the dataset version, tools, model scaffold, and metric.

What SWE-bench measures

The official SWE-bench overview defines the benchmark as an evaluation of large language models on real-world software issues collected from GitHub. The model receives a codebase and an issue, then generates a patch intended to resolve the described problem.

The benchmark includes repository context, issue descriptions, related fixes, and tests. Its Docker-based harness creates a consistent environment, applies the generated patch, runs the repository test suite, and determines whether the issue was resolved. The score is therefore tied to the benchmark's issue selection, patch format, test behavior, and evaluation setup.

SWE-bench is a strong fit when you want to study debugging, code navigation, test-aware changes, and issue resolution. It does not directly measure whether an agent can configure a server, use a command-line tool, train a model, or complete a multi-step file operation outside a repository issue.

SWE-bench datasets and evaluation units

The SWE-bench FAQ lists five main datasets. The full benchmark has 2,294 instances. SWE-bench Lite has 300 instances. SWE-bench Verified has 500 instances verified by engineers as solvable. SWE-bench Multimodal has 100 development instances, and SWE-bench Multilingual has 300 instances across 9 languages and 42 repositories.

Those dataset labels are not interchangeable. A result on Verified is not automatically comparable with a result on Lite or the full benchmark. A multimodal or multilingual result also introduces different inputs and coverage. Always write the dataset name and split beside a score.

The FAQ lists resolution rate as the percentage of submitted instances successfully resolved. It also records submitted, completed, resolved, unresolved, empty-patch, and error counts. A headline percentage hides these denominators, so a serious comparison should preserve them.

The AI coding cost guide provides a useful companion framework for tracking token use, retries, and review time alongside benchmark results.

What Terminal-Bench measures

The official Terminal-Bench site describes a collection of Harbor-native benchmarks for quantifying an agent's terminal mastery. It presents long-running single-task challenges that require an agent to operate inside a terminal environment rather than only return a code block.

The task examples show a broad range of work. They include building a Linux kernel, configuring a Git web server, creating a self-signed certificate, reshaping data, and training a FastText model. These tasks involve commands, files, services, permissions, dependencies, and verification steps.

The ICLR 2026 paper describes Terminal-Bench 2.0 as a curated benchmark of 89 hard tasks in computer terminal environments inspired by real workflows. Each task has a unique environment, a human-written solution, and tests for verification. The paper reports that frontier models and agents scored less than 65 percent in its evaluation.

That result is a statement about the benchmark evaluation described in the paper. It is not a claim that every coding agent will score below 65 percent on every version, or that the benchmark predicts the exact success rate of a production system.

Terminal tasks are more than code generation

A terminal task can fail even when the generated code looks correct. The agent may use the wrong path, omit a dependency, set unsafe permissions, fail to start a service, write output outside the required directory, or skip a final verification command. The evaluator observes the end state and the task tests, not only the text of the code.

This makes Terminal-Bench useful for examining action planning, command execution, environment discovery, file operations, and recovery from errors. It also makes the result sensitive to the agent's tool permissions, shell behavior, time budget, network access, and base image.

For developers building an agent, record the full run. Keep the task version, image, tools, time limit, command trace, final files, test output, and failure reason. Without that context, two agents with the same score may have very different operational strengths.

Side-by-side capability comparison

Use the benchmark that resembles the work you need to evaluate. If your product edits repositories from issue tickets, SWE-bench is closer to the target. If it provisions services, moves files, compiles software, or runs data tasks, Terminal-Bench may expose more relevant failures.

Why benchmark scores are not production guarantees

A benchmark is a controlled measurement, not a warranty. Production environments have changing repositories, incomplete tickets, private dependencies, secrets, network failures, human approvals, and business constraints. A benchmark can make some of these visible, but it cannot reproduce every operational condition.

Scores are also affected by the agent scaffold. Retrieval, planning, test selection, shell tools, retry policies, context windows, and model routing can change the result. When comparing model scores, keep the surrounding system constant or label the comparison as a system-level result.

Data contamination is another reason to be careful. Public repositories, task descriptions, tests, and solutions can appear in training data or evaluation-time retrieval. Use held-out tasks, private tests, or newly created tasks when you need a stronger estimate of generalization.

How to run a fair benchmark comparison

First define the target capability. “Coding quality” is too broad for a useful test. Choose issue fixing, repository navigation, terminal operation, deployment, data processing, or another task family. Then choose the benchmark and version that best represents it.

Keep the model, agent harness, tool access, time budget, retries, and context policy stable. Report the dataset split and all excluded or errored instances. Use the official harness where possible so results can be reproduced by another evaluator.

Run multiple trials when stochastic sampling is enabled. Report both the mean result and the spread when the number of runs supports it. A single successful task is a demonstration, not a benchmark.

Common interpretation mistakes

One mistake is comparing resolution rate with a task success rate as if the denominators were identical. Another is comparing SWE-bench Verified with a Terminal-Bench version without naming either split. A third is quoting a leaderboard number while omitting whether the system used browsing, retrieval, shell tools, or multiple attempts.

Another mistake is treating tests as perfect. Tests can miss a regression, encode a narrow requirement, or fail because of an environment problem. Review error categories and inspect examples instead of accepting the aggregate score as the complete story.

Do not use a benchmark score to make a high-impact deployment decision by itself. Combine it with private tasks, security review, cost measurement, human approval, and rollback procedures. The AI detector evaluation guide applies a similar rule to another automated measurement problem.

Which benchmark should you use?

A combined evaluation is often more informative than a single leaderboard. Use SWE-bench to examine issue resolution and Terminal-Bench to examine terminal operation. Add private tasks that reflect your repositories, deployment rules, and data handling requirements.

Reporting benchmark results clearly

A useful benchmark report names the dataset version, task split, model, agent scaffold, tools, context, time limit, retries, environment, metric, and error policy. Include the number of submitted, completed, resolved, unresolved, empty-patch, and errored tasks when the harness reports them. A reader should be able to tell what the percentage represents.

For long-horizon agents, also record the command trace, final state, test output, and failure category. For issue-resolution agents, record the patch, tests changed or passed, and whether a reviewer accepted the fix. The model comparison guide and long-document evaluation guide show why the evaluation context matters as much as the headline model name.

Conclusion: measure the work you actually need

Terminal-Bench vs SWE-bench is a comparison of task families, not a universal model ranking. SWE-bench measures patch generation for real GitHub issues under a Docker test harness. Terminal-Bench measures difficult terminal tasks with unique environments and task-specific tests. Use the dataset and harness that match your target, disclose the agent setup, and verify benchmark claims with private production-like tasks before relying on them.