Terminal-Bench 4.0 leaderboard
Terminal-Bench 4.0 measures whether an agent can finish real, long-running work in a terminal. It is maintained by the Terminal-Bench team on the Harbor framework and was released at the end of August 2026 as a revision of 3.0: eight tasks were removed (saturated, refusal-prone, publicly solved, or broken), nineteen were fixed, and resource limits were recalibrated around a flat eight-hour agent timeout. The 66 tasks span software, science, machine learning, operations, hardware, security, and media, and each one produces a real deliverable, such as a running service, a proof, a CAD model, a kernel, or a forensic report. About three quarters of the set falls outside traditional software engineering, and the median task carries an expert time estimate of around four hours.
Last refreshed 2026-10-01. 32 models scored on this benchmark.
Full leaderboard
Score interpretation
Every task has its own verifier and passes only if every check passes, so there is no partial credit; timeouts and unverifiable submissions score zero, and refusals count as failures. TensorFeed reports the Vals AI run, which uses one harness (mini-swe-agent) for every model and averages three runs, so the column compares like with like. With its September 29, 2026 update Vals moved the whole board from the Terminus 2 harness to mini-swe-agent, and every score here was replaced in one pass, so figures quoted before that date are not comparable. Two things move the number more than people expect. The harness: Claude Fable 5 went from 22.73 to 41.41 when Vals switched harnesses, and the official board, where each lab runs its own agent, differs again. And fallback: when a safeguard reroutes a task to another model, Vals counts the attempt, and the per-cell note says how many attempts that was. Terminal-Bench 2.x and 4.0 share no tasks, so never compare scores across versions.
Why this matters for AI agents
Most agent failures in production are not a single bad answer; they are a long task that drifts, stalls, or gives up halfway. Terminal-Bench 4.0 is built out of exactly those tasks, with hours-long horizons and binary outcomes, which makes it a better predictor of whether an agent will finish unattended work than any single-turn benchmark. It is also one of the few boards still separating the September 2026 frontier, where the saturated academic sets no longer do.
Other benchmarks
Premium API: time-series for Terminal-Bench 4.0
The leaderboard above is a snapshot. Want to see how a model's Terminal-Bench 4.0 score has moved over the last 30-90 days, or set a webhook that fires when a score crosses a threshold? The premium API has both:
/api/premium/history/benchmarks/series?model=&benchmark=terminal_bench_4: daily score evolution for one model on this benchmark, 1 credit per call