Terminal-Bench 4.0 leaderboard
As of November 9, 2026, Claude Opus 5.5 from Anthropic has the highest Terminal-Bench 4.0 score TensorFeed tracks: 65.2%, ahead of Claude Fable 5.2 (65.2%), across 45 models.
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 2026-11-09. 45 models scored on this benchmark. This board ranks the best AI model for agents, with prices alongside.
Full leaderboard
| # | Model | Provider | Score | Released |
|---|---|---|---|---|
| 1 | Claude Opus 5.5 | Anthropic | 65.15%independent | 2026-09 |
| 2 | Claude Fable 5.2 | Anthropic | 65.15%independent | 2026-11 |
| 3 | Claude Sonnet 5.5 | Anthropic | 64.14%independent | 2026-09 |
| 4 | Kimi K3.1 Pro | Moonshot AI | 63.64%independent | 2026-10 |
| 5 | Gemini 4 Carbon | 63.13%independent | 2026-10 | |
| 6 | GPT-6 Astra | OpenAI | 59.6%independent | 2026-09 |
| 7 | Claude Fable 5.1 | Anthropic | 58.08%independent | 2026-09 |
| 8 | Gemini 4 Argon | 57.58%independent | 2026-10 | |
| 9 | GPT-6.1 Sol | OpenAI | 55.05%independent | 2026-09 |
| 10 | Claude Opus 5 | Anthropic | 53.54%independent | 2026-07 |
| 11 | Pareto | Unbiased | 50.8%vendor | 2026-09 |
| 12 | Muse Wonder | Meta | 49.49%independent | 2026-10 |
| 13 | GPT-6.1 Terra | OpenAI | 49.49%independent | 2026-10 |
| 14 | GPT-6 Sol | OpenAI | 44.44%independent | 2026-09 |
| 15 | Claude Fable 5 | Anthropic | 41.41%independent | 2026-06 |
| 16 | GLM-5.3 | Z.ai | 38.89%independent | 2026-08 |
| 17 | LongCat-2.5 | Meituan | 38.5%vendor | 2026-11 |
| 18 | GPT-5.6 Sol | OpenAI | 37.88%independent | 2026-07 |
| 19 | GLM-5.3 Flash | Z.ai | 37.37%independent | 2026-08 |
| 20 | Claude Haiku 5.5 | Anthropic | 36.36%independent | 2026-10 |
| 21 | Qwen3.8-Max | Alibaba | 34.34%independent | 2026-08 |
| 22 | MiMo-V2.6-Pro | Xiaomi | 31.31%independent | 2026-09 |
| 23 | Grok 4.7 | xAI | 28.79%independent | 2026-09 |
| 24 | Mistral Large 4 | Mistral | 26.26%independent | 2026-10 |
| 25 | MiniMax M3.1 Flash | MiniMax | 25.25%independent | 2026-10 |
| 26 | MiMo-V2.6-Flash | Xiaomi | 24.24%independent | 2026-09 |
| 27 | Claude Opus 4.8 | Anthropic | 23.23%independent | 2026-05 |
| 28 | GPT-5.6 Terra | OpenAI | 22.73%independent | 2026-07 |
| 29 | Ember-1 | Fireworks AI | 20.71%independent | 2026-09 |
| 30 | DeepSeek V4.1 Flash | DeepSeek | 19.7%independent | 2026-09 |
| 31 | Gemini 3.8 Flash | 19.7%independent | 2026-09 | |
| 32 | Grok 4.6 | xAI | 17.17%independent | 2026-08 |
| 33 | Kimi K3 | Moonshot AI | 17.17%independent | 2026-07 |
| 34 | GPT-6 Luna | OpenAI | 13.64%independent | 2026-09 |
| 35 | Gemini 3.7 Flash | 12.12%independent | 2026-08 | |
| 36 | GPT-5.6 Luna | OpenAI | 11.62%independent | 2026-07 |
| 37 | Claude Sonnet 5 | Anthropic | 10.61%independent | 2026-06 |
| 38 | Muse Spark 1.3 | Meta | 10.61%independent | 2026-09 |
| 39 | Grok 4.5 | xAI | 8.59%independent | 2026-07 |
| 40 | Hy4 preview | Tencent | 8.08%independent | 2026-08 |
| 41 | Muse Spark Open | Meta | 6.57%independent | 2026-10 |
| 42 | Muse Spark 1.2 | Meta | 6.06%independent | 2026-08 |
| 43 | Gemini 3.5 Flash | 6.06%independent | 2026-05 | |
| 44 | MiniMax M3 | MiniMax | 1.01%independent | 2026-06 |
| 45 | Inkling | Thinking Machines | 0.51%independent | 2026-07 |
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 late 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