Agentic Coding Benchmark

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.

Model scores 28 models tested

Reset
# 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
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What now?

You've seen how models score on Terminal-Bench 2.1 (Terminus-2) — here's what to do next.