Terminal-Bench 2.1 (Claude Code)
Terminal-Bench 2.1 evaluated using Claude Code 2.1.126, parser=json, temp=1.0, top_p=1.0, max_new_tokens=131072, avg of 5 runs.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
6
What is Terminal-Bench 2.1 (Claude Code)?
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 (Claude Code) 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 (Claude Code) 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 (Claude Code) test?
Terminal-Bench 2.1 (Claude Code) 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 (Claude Code) 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 (Claude Code) 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-claude-code/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 6 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Ornith-1.5-35B-A3B
Ornith AI
|
68.5%
|
19.08.2026 | Report | Verified |
| 2 |
Ornith-1.0-35B
DeepReinforce AI
|
62.8%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 3 |
Ornith-1.0-35B-A3B
Ornith AI
|
62.8%
|
19.08.2026 | Report | Unverified |
| 4 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
49.2%
|
25.06.2026 | DeepReinforce Team (official model card - comparison) | Unverified |
| 5 |
Ornith-1.0-9B
DeepReinforce AI
|
40.6%
|
25.06.2026 | DeepReinforce Team (official model card) | Verified |
| 6 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
38.9%
|
25.06.2026 | DeepReinforce Team (official model card - comparison) | Unverified |