Terminal-Bench 2.1 (Terminus-2)
Terminal-Bench 2.1 evaluated with Harbor/Terminus-2 framework, parser=json, temp=1.0, top_p=1.0, 128K context, 4h timeout, 32 CPU/48GB RAM, avg of 5 runs.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
28
What is Terminal-Bench 2.1 (Terminus-2)?
Terminal-Bench tests whether a model can operate a computer through a terminal as an agent: it gets a Linux container and has to solve hands-on tasks (installing packages, inspecting files, writing scripts) purely via shell commands within a time limit. The score is the share of tasks completed successfully, averaged over several runs.
What does it test?
Terminal-Bench 2.1 (Terminus-2) measures how well a model works as an autonomous coding agent. In detail, it checks:
- planning and executing multi-step shell workflows
- installing, configuring and debugging software in a container
- handling timeouts and failing commands
- finishing the task within the given compute and time budget
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Frequently asked questions
What is the Terminal-Bench 2.1 (Terminus-2) benchmark?
Terminal-Bench tests whether a model can operate a computer through a terminal as an agent: it gets a Linux container and has to solve hands-on tasks (installing packages, inspecting files, writing scripts) purely via shell commands within a time limit. The score is the share of tasks completed successfully, averaged over several runs.
What does Terminal-Bench 2.1 (Terminus-2) test?
Terminal-Bench 2.1 (Terminus-2) mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: planning and executing multi-step shell workflows; installing, configuring and debugging software in a container; handling timeouts and failing commands.
How is the Terminal-Bench 2.1 (Terminus-2) score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get Terminal-Bench 2.1 (Terminus-2) data and scores?
The official source is https://artificialanalysis.ai/evaluations/terminalbench-v2-1. The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/terminal-bench-2-1-terminus-2/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 28 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Kimi K3
Moonshot AI
|
88.3%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) [provider 88.3; Tencent rerun 85.7] | Verified |
| 2 |
GLM-5.3
Zhipu AI
|
88.2%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) [provider 88.2; Tencent rerun 88.3] | Verified |
| 3 |
Qwen3.8-2.4T-A95B
Qwen
|
86.6%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) [provider 86.6; Tencent rerun 85.8] | Verified |
| 4 |
Hy4 preview
Tencent
|
85.4%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 5 |
GLM-5.2
Zhipu AI
|
81%
|
17.06.2026 | Report | Verified |
| 6 |
Nanbeige4.3-33B
Nanbeige
|
80.9%
|
24.10.2026 | Report | Verified |
| 7 |
Ripple-65B Preview
Ricursive Intelligence
|
80.5%
|
— | Report | Verified |
| 8 |
Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
|
76.4%
|
15.10.2026 | Report | Verified |
| 9 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
73%
|
18.08.2026 | Report | Verified |
| 10 |
Hy3
Tencent
|
70.8%
|
28.08.2026 | Tencent Hy4 preview model card benchmark appendix (https://huggingface.co/tencent/Hy4-preview#benchmark-appendix) | Verified |
| 11 |
Ornith-1.5-35B-A3B
Ornith AI
|
67.8%
|
19.08.2026 | Report | Verified |
| 12 |
Kimi K2.6
Moonshot AI
|
67.2%
|
04.06.2026 | Report | Unverified |
| 13 |
MiniMax-M3
MiniMaxAI
|
65%
|
17.06.2026 | Report | Unverified |
| 14 |
Ornith-1.0-35B
DeepReinforce AI
|
64.2%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 15 |
Ornith-1.0-35B-A3B
Ornith AI
|
64.2%
|
19.08.2026 | Report | Unverified |
| 16 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
63.4%
|
18.08.2026 | Report | Unverified |
| 17 |
Xing4.0-29B-A4B
China Telecom AI
|
57.5%
|
16.09.2026 | Report | Verified |
| 18 |
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
|
56.4%
|
04.06.2026 | Report | Verified |
| 19 |
Flint-40B-A2.5B
Recursive
|
54.9%
|
— | HF card https://huggingface.co/RecursiveSI/flint - Terminal-Bench 2.1 with Terminus 2 harness - temp 1.0, top-p 0.95, top-k 20; web retrieval counted as unsuccessful; avg pass@1 over 5+ attempts; measured by Recursive | Verified |
| 20 |
DeepSeek-V4-Flash
DeepSeek
|
54.2%
|
04.06.2026 | Report | Unverified |
| 21 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
52.5%
|
25.06.2026 | DeepReinforce Team (official model card - comparison) | Unverified |
| 22 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
51.7%
|
01.08.2026 | Meta Superintelligence Lab (official model card) | Verified |
| 23 |
DeepSeek-V4-Pro
DeepSeek
|
49.2%
|
04.06.2026 | Report | Unverified |
| 24 |
Ornith-1.0-9B
DeepReinforce AI
|
43.1%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 25 |
Gemma4-31B-it
Google
|
42.1%
|
25.06.2026 | DeepReinforce Team (official model card - comparison) | Unverified |