Terminal-Bench 2.0
89 hard terminal tasks across 16 categories in containers. Hidden tests verify the final state. 5 trials per task.
Leaderboard entries are audited and trajectories are public. Later versions exist (Vals now uses Terminal-Bench 4.0).
- Made by
- Stanford + Laude Institute
- Released
- November 2025
- Kind
- Terminal tasks
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-5.5 #20 on the capability index
- Last in a launch post
- July 2026
- Last model added
- July 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at https://www.tbench.ai/ .
GPT-5.5 leads with 84.7% among models released by April 2026
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
GPT-5.5 leads with 84.7%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
The newest model with a score here came out in April 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 15 more
- 84.7 GPT-5.5
- 80.2
Gemini 3.1 Pro
- 80.2 Claude Opus 4.7
- 78.4 GPT-5.3 Codex
- 76.4 Claude Opus 4.6
- 69.4
Gemini 3 Pro
- 66.5 GPT-5.2 Codex
- 64.9 GPT-5.2
- 64.3
Gemini 3 Flash
- 63.1 Claude Opus 4.5
- 61.6 GPT-5.1-Codex-mini
- 60.5 GPT-5.1-Codex-Max
- 57.8 GPT-5.1-Codex
- 57.3 Grok 4.20
- 53.4 Claude Sonnet 4.6
- 52.4 GLM-5
- 49.6 GPT-5
- 47.6 GPT-5.1
- 46.5 Claude Sonnet 4.5
- 45.1
MiniMax-M2.7
- 44.3 GPT-5-Codex
- 43.2 Kimi K2.5
- 42.9 MiniMax M2.7 Highspeed
- 42.7
MiniMax-M2.5
- 39.5
DeepSeek-V3.2
- 38.0 Claude Opus 4.1
- 36.6
MiniMax-M2.1
- 35.7 Kimi K2 Thinking
- 35.5 Claude Haiku 4.5
- 34.8 GPT-5 mini
- 33.4 GLM-4.7
- 32.6
Gemini 2.5 Pro
- 30.0
MiniMax-M2
- 27.8 Kimi K2
- 27.2 Grok 4
- 27.2
Qwen3-Coder-480B-A35B
- 25.8 grok-code-fast-1
- 24.6
Qwen 3.6 35B-A3B
- 24.5 GLM-4.6
- 21.8 GPT-5 nano
- 19.3 TermiGen-32B
- 18.7 gpt-oss-120b
- 17.1
Gemini 2.5 Flash
- 17.0 AfterQuery-GPT-OSS-20B
- 9.21
Qwen3.5-9B
- 3.37 gpt-oss-20b
- 1 GPT-5.5 84.7%
- 2
Gemini 3.1 Pro 80.2%
- 3 Claude Opus 4.7 80.2%
- 4 GPT-5.3 Codex 78.4%
- 5 Claude Opus 4.6 76.4%
- 6
Gemini 3 Pro 69.4%
- 7 GPT-5.2 Codex 66.5%
- 8 GPT-5.2 64.9%
- 9
Gemini 3 Flash 64.3%
- 10 Claude Opus 4.5 63.1%
- 11 GPT-5.1-Codex-mini 61.6%
- 12 GPT-5.1-Codex-Max 60.5%
- 13 GPT-5.1-Codex 57.8%
- 14 Grok 4.20 reasoning 57.3%
- 15 Claude Sonnet 4.6 53.4%
- 16 GLM-5 52.4%
- 17 GPT-5 49.6%
- 18 GPT-5.1 47.6%
- 19 Claude Sonnet 4.5 46.5%
- 20
MiniMax-M2.7 45.1%
- 21 GPT-5-Codex 44.3%
- 22 Kimi K2.5 43.2%
- 23 MiniMax M2.7 Highspeed 42.9%
- 24
MiniMax-M2.5 42.7%
- 25
DeepSeek-V3.2 39.5%
- 26 Claude Opus 4.1 38%
- 27
MiniMax-M2.1 36.6%
- 28 Kimi K2 Thinking 35.7%
- 29 Claude Haiku 4.5 35.5%
- 30 GPT-5 mini 34.8%
- 31 GLM-4.7 33.4%
- 32
Gemini 2.5 Pro Jun 2025 32.6%
- 33
MiniMax-M2 30%
- 34 Kimi K2 Sep 2025 27.8%
- 35 Grok 4 27.2%
- 36
Qwen3-Coder-480B-A35B 27.2%
- 37 grok-code-fast-1 25.8%
- 38
Qwen 3.6 35B-A3B 24.6%
- 39 GLM-4.6 24.5%
- 40 GPT-5 nano 21.8%
- 41 TermiGen-32B 19.3%
- 42 gpt-oss-120b 18.7%
- 43
Gemini 2.5 Flash Jun 2025 17.1%
- 44 AfterQuery-GPT-OSS-20B 17%
- 45
Qwen3.5-9B 9.21%
- 46 gpt-oss-20b 3.37%
Source: Official Terminal-Bench 2.0 leaderboard
- 84.7 GPT-5.5
- 82.0 Claude Mythos Preview
- 80.2
Gemini 3.1 Pro
- 80.2 Claude Opus 4.7
- 78.4 GPT-5.3 Codex
- 76.4 Claude Opus 4.6
- 70.3
Qwen3.7-Plus
- 69.7
Qwen3.7-Max
- 69.4
Gemini 3 Pro
- 69.3 Composer 2.5
- 69.0 GLM-5.1
- 68.4 MiMo-V2.5-Pro
- 67.9
DeepSeek-V4-Pro
- 66.7 Kimi K2.6
- 66.5 GPT-5.2 Codex
- 65.8 MiMo-V2.5
- 65.4
Qwen 3.6 Max (Preview)
- 64.9 GPT-5.2
- 64.3
Gemini 3 Flash
- 63.1 Claude Opus 4.5
- 61.7 Composer 2
- 61.6
Qwen 3.6 Plus
- 61.6 GPT-5.1-Codex-mini
- 60.5 GPT-5.1-Codex-Max
- 60.0 GPT-5.4 Mini
- 59.3
Qwen3.6 27B
- 59.1 Claude Sonnet 4.6
- 59.0
Muse Spark
- 58.4 GPT-5.3-Codex-Spark
- 57.8 GPT-5.1-Codex
- 57.3 Grok 4.20
- 57.1 MiMo-V2-Pro
- 57.0
MiniMax-M2.7
- 56.9
DeepSeek-V4-Flash
- 56.2 GLM-5
- 55.8
Seed2.0 Pro
- 54.8
MAI-Code-1-Flash
- 54.4
Tencent Hy3 preview
- 52.5
Qwen3.5 397B-A17B
- 51.7
MiniMax-M2.5
- 51.5
Qwen 3.6 35B-A3B
- 51.0
Step 3.5 Flash
- 50.8 Kimi K2.5
- 49.6 GPT-5
- 49.4
Qwen3.5-122B-A10B
- 47.9
MiniMax-M2.1
- 47.9 Composer 1.5
- 47.6 GPT-5.1
- 46.5 Claude Sonnet 4.5
- 46.4
DeepSeek-V3.2
- 46.3 GPT-5.4 Nano
- 46.0
MAI-Thinking-1
- 45.8
Laguna M.1
- 45.2
Seed1.8
- 44.3 GPT-5-Codex
- 43.3
Seed2.0 Lite (0428)
- 41.6
Qwen3.5-27B
- 41.0 GLM-4.7
- 40.5
Qwen3.5-35B-A3B
- 40.5
Ling-2.6-1T
- 38.5 MiMo-V2-Flash
- 38.0 Claude Opus 4.1
- 37.5
Laguna XS 2.1
- 36.2
Qwen3-Coder-Next
- 36.0
North Mini Code 1.0
- 35.7 Kimi K2 Thinking
- 35.7
Laguna XS.2
- 35.5 Claude Haiku 4.5
- 34.8 GPT-5 mini
- 32.6
Gemini 2.5 Pro
- 32.6
Devstral 2
- 31.0
Nemotron 3 Super
- 30.0
MiniMax-M2
- 29.0
K-EXAONE
- 27.8 Kimi K2
- 27.2 Grok 4
- 27.2
Qwen3-Coder-480B-A35B
- 25.8 grok-code-fast-1
- 24.5 GLM-4.6
- 22.5
Devstral Small 2
- 21.8 GPT-5 nano
- 21.1
Nemotron-Cascade 2 30B-A3B
- 18.7 gpt-oss-120b
- 17.1
Gemini 2.5 Flash
- 10.1
Solar Pro 3 (March 2026 update)
- 9.21
Qwen3.5-9B
- 3.37 gpt-oss-20b
- 1 GPT-5.5 84.7%
- 2 Claude Mythos Preview 82%
- 3
Gemini 3.1 Pro 80.2%
- 4 Claude Opus 4.7 80.2%
- 5 GPT-5.3 Codex 78.4%
- 6 Claude Opus 4.6 76.4%
- 7
Qwen3.7-Plus 70.3%
- 8
Qwen3.7-Max 69.7%
- 9
Gemini 3 Pro 69.4%
- 10 Composer 2.5 69.3%
- 11 GLM-5.1 69%
- 12 MiMo-V2.5-Pro 68.4%
- 13
DeepSeek-V4-Pro 67.9%
- 14 Kimi K2.6 66.7%
- 15 GPT-5.2 Codex 66.5%
- 16 MiMo-V2.5 65.8%
- 17
Qwen 3.6 Max (Preview) 65.4%
- 18 GPT-5.2 64.9%
- 19
Gemini 3 Flash 64.3%
- 20 Claude Opus 4.5 63.1%
- 21 Composer 2 61.7%
- 22
Qwen 3.6 Plus 61.6%
- 23 GPT-5.1-Codex-mini 61.6%
- 24 GPT-5.1-Codex-Max 60.5%
- 25 GPT-5.4 Mini 60%
- 26
Qwen3.6 27B 59.3%
- 27 Claude Sonnet 4.6 59.1%
- 28
Muse Spark 59%
- 29 GPT-5.3-Codex-Spark 58.4%
- 30 GPT-5.1-Codex 57.8%
- 31 Grok 4.20 reasoning 57.3%
- 32 MiMo-V2-Pro 57.1%
- 33
MiniMax-M2.7 57%
- 34
DeepSeek-V4-Flash 56.9%
- 35 GLM-5 56.2%
- 36
Seed2.0 Pro 55.8%
- 37
MAI-Code-1-Flash 54.8%
- 38
Tencent Hy3 preview 54.4%
- 39
Qwen3.5 397B-A17B 52.5%
- 40
MiniMax-M2.5 51.7%
- 41
Qwen 3.6 35B-A3B 51.5%
- 42
Step 3.5 Flash 51%
- 43 Kimi K2.5 50.8%
- 44 GPT-5 49.6%
- 45
Qwen3.5-122B-A10B 49.4%
- 46
MiniMax-M2.1 47.9%
- 47 Composer 1.5 47.9%
- 48 GPT-5.1 47.6%
- 49 Claude Sonnet 4.5 46.5%
- 50
DeepSeek-V3.2 46.4%
- 51 GPT-5.4 Nano 46.3%
- 52
MAI-Thinking-1 46%
- 53
Laguna M.1 45.8%
- 54
Seed1.8 45.2%
- 55 GPT-5-Codex 44.3%
- 56
Seed2.0 Lite (0428) 43.3%
- 57
Qwen3.5-27B 41.6%
- 58 GLM-4.7 41%
- 59
Qwen3.5-35B-A3B 40.5%
- 60
Ling-2.6-1T 40.5%
- 61 MiMo-V2-Flash 38.5%
- 62 Claude Opus 4.1 38%
- 63
Laguna XS 2.1 37.5%
- 64
Qwen3-Coder-Next 36.2%
- 65
North Mini Code 1.0 36%
- 66 Kimi K2 Thinking 35.7%
- 67
Laguna XS.2 35.7%
- 68 Claude Haiku 4.5 35.5%
- 69 GPT-5 mini 34.8%
- 70
Gemini 2.5 Pro Jun 2025 32.6%
- 71
Devstral 2 32.6%
- 72
Nemotron 3 Super 31%
- 73
MiniMax-M2 30%
- 74
K-EXAONE 29%
- 75 Kimi K2 Sep 2025 27.8%
- 76 Grok 4 27.2%
- 77
Qwen3-Coder-480B-A35B 27.2%
- 78 grok-code-fast-1 25.8%
- 79 GLM-4.6 24.5%
- 80
Devstral Small 2 22.5%
- 81 GPT-5 nano 21.8%
- 82
Nemotron-Cascade 2 30B-A3B 21.1%
- 83 gpt-oss-120b 18.7%
- 84
Gemini 2.5 Flash Jun 2025 17.1%
- 85
Solar Pro 3 (March 2026 update) 10.1%
- 86
Qwen3.5-9B 9.21%
- 87 gpt-oss-20b 3.37%
Sources: Official Terminal-Bench 2.0 leaderboard and labs’ model cards
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.5 | 84.7% | NexAU-AHE | Apr 2026 | Official leaderboard |
| 2 | | 80.2% | TongAgents | Feb 2026 | Official leaderboard |
| 3 | Claude Opus 4.7 | 80.2% | WOZCODE | Apr 2026 | Official leaderboard |
| 4 | GPT-5.3 Codex | 78.4% | SageAgent | Feb 2026 | Official leaderboard |
| 5 | Claude Opus 4.6 | 76.4% | Meta-Harness | Feb 2026 | Official leaderboard |
| 6 | | 69.4% | Ante | Nov 2025 | Official leaderboard |
| 7 | GPT-5.2 Codex | 66.5% | Deep Agents | Dec 2025 | Official leaderboard |
| 8 | GPT-5.2 | 64.9% | Droid | Dec 2025 | Official leaderboard |
| 9 | | 64.3% | Junie CLI | Dec 2025 | Official leaderboard |
| 10 | Claude Opus 4.5 | 63.1% | Droid | Nov 2025 | Official leaderboard |
| 11 | GPT-5.1-Codex-mini | 61.6% | hookele | Nov 2025 | Official leaderboard |
| 12 | GPT-5.1-Codex-Max | 60.5% | Codex CLI | Nov 2025 | Official leaderboard |
| 13 | GPT-5.1-Codex | 57.8% | Crux | Nov 2025 | Official leaderboard |
| 14 | Grok 4.20 | 57.3% | reasoning, Grok CLI | Feb 2026 | Official leaderboard |
| 15 | Claude Sonnet 4.6 | 53.4% | Simplai Agent | Feb 2026 | Official leaderboard |
| 16 | GLM-5 | 52.4% | Terminus 2 | Feb 2026 | Official leaderboard |
| 17 | GPT-5 | 49.6% | Codex CLI | Aug 2025 | Official leaderboard |
| 18 | GPT-5.1 | 47.6% | Terminus 2 | Nov 2025 | Official leaderboard |
| 19 | Claude Sonnet 4.5 | 46.5% | CAMEL-AI | Sep 2025 | Official leaderboard |
| 20 | | 45.1% | IndusAGI Coding Agent | Mar 2026 | Official leaderboard |
| 21 | GPT-5-Codex | 44.3% | Codex CLI | Sep 2025 | Official leaderboard |
| 22 | Kimi K2.5 | 43.2% | Terminus 2 | Jan 2026 | Official leaderboard |
| 23 | MiniMax M2.7 Highspeed | 42.9% | Harness Agent | Mar 2026 | Official leaderboard |
| 24 | | 42.7% | cchuter | Feb 2026 | Official leaderboard |
| 25 | | 39.5% | Terminus 2 | Dec 2025 | Official leaderboard |
| 26 | Claude Opus 4.1 | 38% | Terminus 2 | Aug 2025 | Official leaderboard |
| 27 | | 36.6% | Crux | Dec 2025 | Official leaderboard |
| 28 | Kimi K2 Thinking | 35.7% | Terminus 2 | Nov 2025 | Official leaderboard |
| 29 | Claude Haiku 4.5 | 35.5% | Goose | Oct 2025 | Official leaderboard |
| 30 | GPT-5 mini | 34.8% | spoox-o-m | Aug 2025 | Official leaderboard |
| 31 | GLM-4.7 | 33.4% | Terminus 2 | Dec 2025 | Official leaderboard |
| 32 | | 32.6% | Terminus 2 | Jun 2025 | Official leaderboard |
| 33 | | 30% | Terminus 2 | Oct 2025 | Official leaderboard |
| 34 | Kimi K2 Sep 2025 | 27.8% | Terminus 2 | Sep 2025 | Official leaderboard |
| 35 | Grok 4 | 27.2% | OpenHands | Jul 2025 | Official leaderboard |
| 36 | | 27.2% | Dakou Agent | Jul 2025 | Official leaderboard |
| 37 | grok-code-fast-1 | 25.8% | Mini-SWE-Agent | Aug 2025 | Official leaderboard |
| 38 | | 24.6% | little-coder | Apr 2026 | Official leaderboard |
| 39 | GLM-4.6 | 24.5% | Terminus 2 | Sep 2025 | Official leaderboard |
| 40 | GPT-5 nano | 21.8% | spoox-o-m | Aug 2025 | Official leaderboard |
| 41 | TermiGen-32B | 19.3% | Bash Agent | May 2026 | Official leaderboard |
| 42 | gpt-oss-120b | 18.7% | Terminus 2 | Aug 2025 | Official leaderboard |
| 43 | | 17.1% | Mini-SWE-Agent | Jun 2025 | Official leaderboard |
| 44 | AfterQuery-GPT-OSS-20B | 17% | Terminus 2 | Mar 2026 | Official leaderboard |
| 45 | | 9.21% | little-coder | Feb 2026 | Official leaderboard |
| 46 | gpt-oss-20b | 3.37% | Mini-SWE-Agent | Aug 2025 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.5 | 84.7% | NexAU-AHE | Apr 2026 | Official leaderboard |
| 2 | Claude Mythos Preview | 82% | Apr 2026 | Lab model card | |
| 3 | | 80.2% | TongAgents | Feb 2026 | Official leaderboard |
| 4 | Claude Opus 4.7 | 80.2% | WOZCODE | Apr 2026 | Official leaderboard |
| 5 | GPT-5.3 Codex | 78.4% | SageAgent | Feb 2026 | Official leaderboard |
| 6 | Claude Opus 4.6 | 76.4% | Meta-Harness | Feb 2026 | Official leaderboard |
| 7 | | 70.3% | Jun 2026 | Lab model card | |
| 8 | | 69.7% | May 2026 | Lab model card | |
| 9 | | 69.4% | Ante | Nov 2025 | Official leaderboard |
| 10 | Composer 2.5 | 69.3% | May 2026 | Lab model card | |
| 11 | GLM-5.1 | 69% | Apr 2026 | Lab model card | |
| 12 | MiMo-V2.5-Pro | 68.4% | Apr 2026 | Lab model card | |
| 13 | | 67.9% | Apr 2026 | Lab model card | |
| 14 | Kimi K2.6 | 66.7% | Apr 2026 | Lab model card | |
| 15 | GPT-5.2 Codex | 66.5% | Deep Agents | Dec 2025 | Official leaderboard |
| 16 | MiMo-V2.5 | 65.8% | Apr 2026 | Lab model card | |
| 17 | | 65.4% | Apr 2026 | Lab model card | |
| 18 | GPT-5.2 | 64.9% | Droid | Dec 2025 | Official leaderboard |
| 19 | | 64.3% | Junie CLI | Dec 2025 | Official leaderboard |
| 20 | Claude Opus 4.5 | 63.1% | Droid | Nov 2025 | Official leaderboard |
| 21 | Composer 2 | 61.7% | Mar 2026 | Lab model card | |
| 22 | | 61.6% | Apr 2026 | Lab model card | |
| 23 | GPT-5.1-Codex-mini | 61.6% | hookele | Nov 2025 | Official leaderboard |
| 24 | GPT-5.1-Codex-Max | 60.5% | Codex CLI | Nov 2025 | Official leaderboard |
| 25 | GPT-5.4 Mini | 60% | Mar 2026 | Lab model card | |
| 26 | | 59.3% | Apr 2026 | Lab model card | |
| 27 | Claude Sonnet 4.6 | 59.1% | Feb 2026 | Lab model card | |
| 28 | | 59% | Apr 2026 | Lab model card | |
| 29 | GPT-5.3-Codex-Spark | 58.4% | Feb 2026 | Lab model card | |
| 30 | GPT-5.1-Codex | 57.8% | Crux | Nov 2025 | Official leaderboard |
| 31 | Grok 4.20 | 57.3% | reasoning, Grok CLI | Feb 2026 | Official leaderboard |
| 32 | MiMo-V2-Pro | 57.1% | Mar 2026 | Lab model card | |
| 33 | | 57% | Mar 2026 | Lab model card | |
| 34 | | 56.9% | Apr 2026 | Lab model card | |
| 35 | GLM-5 | 56.2% | Feb 2026 | Lab model card | |
| 36 | | 55.8% | Feb 2026 | Lab model card | |
| 37 | | 54.8% | Jun 2026 | Lab model card | |
| 38 | | 54.4% | Apr 2026 | Lab model card | |
| 39 | | 52.5% | Feb 2026 | Lab model card | |
| 40 | | 51.7% | Feb 2026 | Lab model card | |
| 41 | | 51.5% | Apr 2026 | Lab model card | |
| 42 | | 51% | Feb 2026 | Lab model card | |
| 43 | Kimi K2.5 | 50.8% | Jan 2026 | Lab model card | |
| 44 | GPT-5 | 49.6% | Codex CLI | Aug 2025 | Official leaderboard |
| 45 | | 49.4% | Feb 2026 | Lab model card | |
| 46 | | 47.9% | Dec 2025 | Lab model card | |
| 47 | Composer 1.5 | 47.9% | Feb 2026 | Lab model card | |
| 48 | GPT-5.1 | 47.6% | Terminus 2 | Nov 2025 | Official leaderboard |
| 49 | Claude Sonnet 4.5 | 46.5% | CAMEL-AI | Sep 2025 | Official leaderboard |
| 50 | | 46.4% | Dec 2025 | Lab model card | |
| 51 | GPT-5.4 Nano | 46.3% | Mar 2026 | Lab model card | |
| 52 | | 46% | Jun 2026 | Lab model card | |
| 53 | | 45.8% | May 2026 | Lab model card | |
| 54 | | 45.2% | Dec 2025 | Lab model card | |
| 55 | GPT-5-Codex | 44.3% | Codex CLI | Sep 2025 | Official leaderboard |
| 56 | | 43.3% | Apr 2026 | Lab model card | |
| 57 | | 41.6% | Feb 2026 | Lab model card | |
| 58 | GLM-4.7 | 41% | Dec 2025 | Lab model card | |
| 59 | | 40.5% | Feb 2026 | Lab model card | |
| 60 | | 40.5% | Jun 2026 | Lab model card | |
| 61 | MiMo-V2-Flash | 38.5% | Dec 2025 | Lab model card | |
| 62 | Claude Opus 4.1 | 38% | Terminus 2 | Aug 2025 | Official leaderboard |
| 63 | | 37.5% | Jul 2026 | Lab model card | |
| 64 | | 36.2% | Feb 2026 | Lab model card | |
| 65 | | 36% | Jun 2026 | Lab model card | |
| 66 | Kimi K2 Thinking | 35.7% | Terminus 2 | Nov 2025 | Official leaderboard |
| 67 | | 35.7% | May 2026 | Lab model card | |
| 68 | Claude Haiku 4.5 | 35.5% | Goose | Oct 2025 | Official leaderboard |
| 69 | GPT-5 mini | 34.8% | spoox-o-m | Aug 2025 | Official leaderboard |
| 70 | | 32.6% | Terminus 2 | Jun 2025 | Official leaderboard |
| 71 | | 32.6% | Dec 2025 | Lab model card | |
| 72 | | 31% | Mar 2026 | Lab model card | |
| 73 | | 30% | Terminus 2 | Oct 2025 | Official leaderboard |
| 74 | | 29% | Dec 2025 | Lab model card | |
| 75 | Kimi K2 Sep 2025 | 27.8% | Terminus 2 | Sep 2025 | Official leaderboard |
| 76 | Grok 4 | 27.2% | OpenHands | Jul 2025 | Official leaderboard |
| 77 | | 27.2% | Dakou Agent | Jul 2025 | Official leaderboard |
| 78 | grok-code-fast-1 | 25.8% | Mini-SWE-Agent | Aug 2025 | Official leaderboard |
| 79 | GLM-4.6 | 24.5% | Terminus 2 | Sep 2025 | Official leaderboard |
| 80 | | 22.5% | Dec 2025 | Lab model card | |
| 81 | GPT-5 nano | 21.8% | spoox-o-m | Aug 2025 | Official leaderboard |
| 82 | | 21.1% | Mar 2026 | Lab model card | |
| 83 | gpt-oss-120b | 18.7% | Terminus 2 | Aug 2025 | Official leaderboard |
| 84 | | 17.1% | Mini-SWE-Agent | Jun 2025 | Official leaderboard |
| 85 | | 10.1% | Mar 2026 | Lab model card | |
| 86 | | 9.21% | little-coder | Feb 2026 | Official leaderboard |
| 87 | gpt-oss-20b | 3.37% | Mini-SWE-Agent | Aug 2025 | Official leaderboard |
Labs report scores for 63 models in their cards. Epoch AI collected claims for 5 models from labs’ reports and papers. Switch the scores above to rank them together.
6 models have held number one on the official leaderboard since its release in November 2025 7 models have held number one with claims included since its release in November 2025
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official Terminal-Bench 2.0 leaderboard, labs’ model cards and claims collected by Epoch AI (CC BY 4.0)
4 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Jul 2, 2026 | 37.5 | Terminal-Bench 2.0 | |
| Jun 9, 2026 | 36 | Terminal-Bench v2 (simple ReAct harness with one terminal tool) | |
| Jun 2, 2026 | 54.8 | Terminal Bench 2 | |
| Jun 2, 2026 | 46 | Terminal-Bench 2.0 | |
| Jun 1, 2026 | 70.3 | Terminal Bench 2.0-Terminus (avg of 5 runs) | |
| May 20, 2026 | 69.7 | Terminal Bench 2.0-Terminus | |
| May 18, 2026 | Composer 2.5 | 69.3 | Terminal-Bench 2.0 |
| Apr 28, 2026 | 30.1 | Terminal-Bench 2.0 (mean pass@1 over 5 runs) | |
| Apr 28, 2026 | 40.7 | Terminal-Bench 2.0 (mean pass@1 over 5 runs, 48GB RAM/32 CPUs) | |
| Apr 28, 2026 | 43.3 | Terminal Bench 2.0 | |
| Apr 27, 2026 | MiMo-V2.5-Pro | 68.4 | Terminal-Bench 2.0 |
| Apr 24, 2026 | 56.9 | Terminal Bench 2.0 (Acc), V4-Flash Max | |
| Apr 24, 2026 | 67.9 | Terminal Bench 2.0 (Acc) | |
| Apr 23, 2026 | 54.4 | Terminal-Bench 2.0 | |
| Apr 23, 2026 | GPT-5.5 | 82.7 | Terminal-Bench 2.0 |
| Apr 22, 2026 | 59.3 | Terminal-Bench 2.0 (Terminus-2, avg of 5) | |
| Apr 22, 2026 | MiMo-V2.5 | 65.8 | Terminal-Bench 2.0 |
| Apr 20, 2026 | 65.4 | Terminal-Bench 2.0 (Terminus-2) Agentic Terminal Coding | |
| Apr 20, 2026 | Kimi K2.6 | 66.7 | Terminal-Bench 2.0 (Terminus-2) |
| Apr 16, 2026 | Claude Opus 4.7 | 69.4 | Agentic terminal coding Terminal-Bench 2.0 |
| Apr 15, 2026 | 51.5 | Terminal-Bench 2.0 (Terminus-2, avg of 5) | |
| Apr 8, 2026 | 59 | Terminal-Bench 2.0 (Agentic Terminal Coding) | |
| Apr 7, 2026 | GLM-5.1 | 69 | Terminal-Bench 2.0 (Best self-reported), Claude Code |
| Apr 7, 2026 | Claude Mythos Preview | 82 | Terminal-Bench 2.0 (Terminus-2) |
| Apr 2, 2026 | 61.6 | Terminal-Bench 2.0 | |
| Mar 24, 2026 | 10.1 | Terminal Bench 2 | |
| Mar 19, 2026 | 21.1 | Terminal Bench 2.0 | |
| Mar 19, 2026 | Composer 2 | 61.7 | Terminal-Bench 2.0 (Harbor, mean of 5 runs) |
| Mar 18, 2026 | MiMo-V2-Pro | 57.1 | Terminal-Bench 2.0 |
| Mar 18, 2026 | 57 | Terminal Bench 2 (text) | |
| Mar 17, 2026 | GPT-5.4 Nano | 46.3 | Terminal-Bench 2.0 |
| Mar 17, 2026 | GPT-5.4 Mini | 60 | Terminal-Bench 2.0 |
| Mar 11, 2026 | 31 | Terminal Bench Core 2.0 | |
| Feb 24, 2026 | 40.5 | Terminal Bench 2 | |
| Feb 24, 2026 | 41.6 | Terminal Bench 2 | |
| Feb 24, 2026 | 49.4 | Terminal Bench 2 | |
| Feb 19, 2026 | 68.5 | Terminal-Bench 2.0 (Terminus-2 harness) | |
| Feb 17, 2026 | Claude Sonnet 4.6 | 59.1 | Agentic terminal coding Terminal-Bench 2.0 |
| Feb 16, 2026 | 52.5 | Terminal Bench 2 | |
| Feb 14, 2026 | 55.8 | Terminal Bench 2.0 (Terminus 2, 3 tasks excluded) | |
| Feb 12, 2026 | GPT-5.3-Codex-Spark | 58.4 | Terminal-Bench 2.0 |
| Feb 12, 2026 | 51.7 | Terminal Bench 2 | |
| Feb 11, 2026 | GLM-5 | 56.2 | Terminal-Bench 2.0 (Terminus 2) |
| Feb 9, 2026 | Composer 1.5 | 47.9 | Terminal-Bench 2.0 Score (%) (Harbor, mean of 2 runs) |
| Feb 5, 2026 | Claude Opus 4.6 | 65.4 | Agentic terminal coding Terminal-Bench 2.0 |
| Feb 5, 2026 | GPT-5.3 Codex | 77.3 | Terminal-Bench 2.0 |
| Feb 2, 2026 | 36.2 | Terminal-Bench 2.0 (w/ Terminus-2 json) | |
| Feb 2, 2026 | 51 | Terminal-Bench 2.0 | |
| Jan 27, 2026 | Kimi K2.5 | 50.8 | Terminal Bench 2.0 |
| Dec 31, 2025 | 29 | Terminal-Bench 2.0 | |
| Dec 23, 2025 | 47.9 | Terminal-bench 2.0 | |
| Dec 22, 2025 | GLM-4.7 | 41 | Terminal Bench 2.0 |
| Dec 18, 2025 | GPT-5.2 Codex | 64 | Terminal-Bench 2.0 |
| Dec 18, 2025 | 45.2 | Terminal Bench 2.0 | |
| Dec 17, 2025 | 47.6 | Terminal-bench 2.0 (Terminus-2 harness) | |
| Dec 16, 2025 | MiMo-V2-Flash | 38.5 | Terminal-Bench 2.0 |
| Dec 9, 2025 | 22.5 | Terminal Bench 2 (HF card) | |
| Dec 9, 2025 | 32.6 | Terminal Bench 2 (HF card) | |
| Dec 1, 2025 | 46.4 | Terminal Bench 2.0 (Acc) | |
| Nov 24, 2025 | Claude Opus 4.5 | 59.3 | Agentic terminal coding Terminal-bench 2.0 (128k thinking budget) |
| Nov 19, 2025 | GPT-5.1-Codex-Max | 58.1 | Terminal-Bench 2.0, xhigh |
| Nov 18, 2025 | 54.2 | Terminal-Bench 2.0 (Terminus-2 agent) |
28 system cards report it, from 17 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Jun 13, 2026 | | 40.5 | terminal-bench 2.0, accuracy (Table 5) | Capabilities |
| Jun 12, 2026 | | 59 | Terminal-Bench 2.0 (2.3.1.1 AI R&D text) | AI R&D risk |
| Jun 2, 2026 | | 46 | Terminal-Bench 2.0, bash tool only, timeouts ignored (Table 11) | Capabilities |
| Jun 2, 2026 | | 54.8 | Terminal Bench 2, pass rate (Coding table) | Capabilities |
| May 26, 2026 | | 57 | Terminal-Bench 2.0, Terminus-2, verified 2.0 dataset (zai-org/terminal-bench-2-verified), 4 trials (Table 4) | Capabilities |
| May 26, 2026 | | 51.7 | Terminal-Bench 2.0, Terminus-2, verified 2.0 dataset (zai-org/terminal-bench-2-verified), 4 trials (Table 4) | Capabilities |
| May 25, 2026 | | 35.7 | Terminal-Bench 2.0, pool harness, 500 steps, mean pass@1 over 4 runs, some task images and verifiers patched (Table 6) | Capabilities |
| May 25, 2026 | | 45.8 | Terminal-Bench 2.0, pool harness, 500 steps, mean pass@1 over 4 runs, some task images and verifiers patched (Table 7) | Capabilities |
| Apr 26, 2026 | | 67.9 | Terminal Bench 2.0 (Acc), original dataset, DS-V4-Pro Max (Table 6). About 72.0 on the Verified subset (5.3.1 text) | Capabilities |
| Apr 26, 2026 | | 56.9 | Terminal Bench 2.0 (Acc), DeepSeek-V4-Flash Max (Table 7) | Capabilities |
| Apr 16, 2026 | Claude Opus 4.7 system card | 69.4 | Terminal-Bench 2.0, Terminus-2 harness, thinking disabled, mean reward over 5 attempts on 89 tasks (Table 8.1.A, 8.3) | Capabilities |
| Apr 7, 2026 | Claude Mythos Preview system card | 82 | Terminal-Bench 2.0, Terminus-2 harness in Harbor, adaptive thinking, max effort, 1x timeouts, mean reward over 5 attempts on 89 tasks (Table 6.3.A, 6.5); 92.1 with the 2.1 fixes and 4-hour timeouts | Capabilities |
| Mar 25, 2026 | Composer 2 technical report | 61.7 | Terminal-Bench, Cursor's own harness, version not stated, cites the ICLR 2026 Terminal-Bench paper (Table 1 and 7.2) | Capabilities |
| Mar 21, 2026 | | 45.2 | Terminal Bench 2.0 (Table 4) | Capabilities |
| Mar 19, 2026 | | 21.1 | Terminal Bench 2.0, Terminus-2, avg@5 (Table 1) | Capabilities |
| Mar 11, 2026 | | 31 | Terminal Bench Core 2.0, Harbor harness (Table 5) | Capabilities |
| Feb 28, 2026 | | 36.2 | Terminal-Bench 2.0, Terminus2-json, as in Figure 1. Terminus2-xml 34.2, ClaudeCode 30.9, QwenCode 25.8 (Table 5) | Capabilities |
| Feb 19, 2026 | | 68.5 | Terminal-Bench 2.0, Terminus-2 harness (results table, p.4) | Capabilities |
| Feb 17, 2026 | GLM-5 technical report | 56.2 | Terminal-Bench 2.0 (Terminus-2), original dataset (Table 7). Verified dataset 60.7; Claude Code 56.2 / 61.1 | Capabilities |
| Feb 17, 2026 | Claude Sonnet 4.6 system card | 59.1 | Terminal-Bench 2.0, max effort, no thinking budget, Terminus-2 harness (Table 2.1.A, 2.3) | Capabilities |
| Feb 14, 2026 | | 55.8 | Terminal Bench 2.0, Terminus 2, 3 tasks excluded (Table 11) | Capabilities |
| Feb 11, 2026 | | 51 | Terminal-Bench 2.0, avg@8 50.98%, pass@8 67/89 (Table 5 and E.2.2) | Capabilities |
| Feb 5, 2026 | Claude Opus 4.6 system card | 65.4 | Terminal-Bench 2.0, Terminus-2 harness, adaptive thinking, max effort, 89 tasks x 15 runs (Table 2.3.A, Figure 2.5.A); 61.1 at medium and 55.1 at low effort | Capabilities |
| Jan 30, 2026 | Kimi K2.5 technical report | 50.8 | Terminal Bench 2.0, Terminus-2, non-thinking mode (Table 4) | Capabilities |
| Jan 6, 2026 | MiMo-V2-Flash technical report | 38.5 | Terminal Bench 2.0 (Table 9) | Capabilities |
| Jan 5, 2026 | | 29 | Terminal-Bench 2.0, Terminus 2 agent (Table 3, Reasoning mode) | Capabilities |
| Dec 1, 2025 | | 46.4 | Terminal Bench 2.0 (Acc), Claude Code framework, DeepSeek-V3.2 Thinking (Table 2). 39.3 with Terminus in non-thinking mode | Capabilities |
| Nov 24, 2025 | Claude Opus 4.5 system card | 59.3 | Terminal-bench 2.0, Terminus-2 harness, 128k thinking budget (Table 2.3.A; 57.8% with 64k thinking, 2.5) | Capabilities |
The lab's number comes out ahead in 9 of 19 pairs that differ
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. Every benchmark.
| Model | Lab says | Board says | Gap | What differs |
|---|---|---|---|---|
| | 51.5% Terminal-Bench 2.0 (Terminus-2, avg of 5) | 24.6% Leaderboard, little-coder | +27 pts | Qwen uses Terminus-2 with a three-hour timeout, while the compared leaderboard entry uses little-coder. |
| | 47.6% Terminal-bench 2.0 (Terminus-2 harness) | 64.3% Leaderboard, Junie CLI | −17 pts | Google uses the Terminus-2 harness, while the board pairs Gemini 3 Flash with Junie CLI. |
| | 54.2% Terminal-Bench 2.0 (Terminus-2 agent) | 69.4% Leaderboard, Ante | −15 pts | The lab used Terminus-2, while the board reports the Ante agent. |
| | 57% Terminal Bench 2 (text) | 45.1% Leaderboard, IndusAGI Coding Agent | +12 pts | |
| | 68.5% Terminal-Bench 2.0 (Terminus-2 harness) | 80.2% Leaderboard, TongAgents | −12 pts | |
| | 47.9% Terminal-bench 2.0 | 36.6% Leaderboard, Crux | +11 pts | |
| Claude Opus 4.6 | 65.4% Agentic terminal coding Terminal-Bench 2.0 | 76.4% Leaderboard, Meta-Harness | −11 pts | |
| Claude Opus 4.7 | 69.4% Agentic terminal coding Terminal-Bench 2.0 | 80.2% Leaderboard, WOZCODE | −11 pts | |
| | 51.7% Terminal Bench 2 | 42.7% Leaderboard, cchuter | +9.0 pts | |
| GLM-4.7 | 41% Terminal Bench 2.0 | 33.4% Leaderboard, Terminus 2 | +7.6 pts | |
| Kimi K2.5 | 50.8% Terminal Bench 2.0 | 43.2% Leaderboard, Terminus 2 | +7.6 pts | |
| | 46.4% Terminal Bench 2.0 (Acc) | 39.5% Leaderboard, Terminus 2 | +6.9 pts | |
| Claude Sonnet 4.6 | 59.1% Agentic terminal coding Terminal-Bench 2.0 | 53.4% Leaderboard, Simplai Agent | +5.7 pts | |
| Claude Opus 4.5 | 59.3% Agentic terminal coding Terminal-bench 2.0 (128k thinking budget) | 63.1% Leaderboard, Droid | −3.9 pts | |
| GLM-5 | 56.2% Terminal-Bench 2.0 (Terminus 2) | 52.4% Leaderboard, Terminus 2 | +3.8 pts | |
| GPT-5.2 Codex | 64% Terminal-Bench 2.0 | 66.5% Leaderboard, Deep Agents | −2.5 pts | |
| GPT-5.1-Codex-Max | 58.1% Terminal-Bench 2.0, xhigh | 60.5% Leaderboard, Codex CLI | −2.4 pts | |
| GPT-5.5 | 82.7% Terminal-Bench 2.0 | 84.7% Leaderboard, NexAU-AHE | −2.0 pts | |
| GPT-5.3 Codex | 77.3% Terminal-Bench 2.0 | 78.4% Leaderboard, SageAgent | −1.1 pts |
Versions
We switch the top row to each new version when it comes out. Open it for one row per version. An old version stays in play while people still test it, so whoever first takes it past 85% gets credit for closing it out.
Sources: Official leaderboards and boards copied by Epoch AI (CC BY 4.0)
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