Terminal-Bench Lite
Lite variant of Terminal-Bench (terminal coding agent evaluations) as reported on the KRAFTON Raon-1 mini preview card; evaluated with judge gpt-5.6-luna. Distinct from Terminal-Bench 2.0/2.1/3.0/4.0 records.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
5
What is Terminal-Bench Lite?
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 Lite 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 Lite 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 Lite test?
Terminal-Bench Lite 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 Lite 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 Lite data and scores?
The official source is https://huggingface.co/KRAFTON/Raon-1-mini-preview. 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-lite/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 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 5 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
70.7%
|
21.10.2026 | Report | Unverified |
| 2 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
67%
|
21.10.2026 | Report | Unverified |
| 3 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
63.3%
|
21.10.2026 | Report | Unverified |
| 4 |
Gemma4-26B-A4B
Google
|
52%
|
21.10.2026 | Report | Unverified |
| 5 |
Raon-1 mini
KRAFTON AI
|
47%
|
21.10.2026 | Report | Verified |