reasoning benchmark
Terminal-Bench
Terminal-Bench is a benchmark for testing AI agents in real terminal environments. It evaluates how well agents can handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, security tasks, data science workflows, and cybersecurity vulnerabilities. The benchmark consists of a dataset of ~100 hand-crafted, human-verified tasks and an execution harness that connects language models to a terminal sandbox.
Updated Aug 11, 2026
Models25
Model coverage25
MetricScore
EvidenceB
Terminal-Bench Ranking
Higher score ranks better on this benchmark.
| 01 | AN | 50.0% | 100.0% | 25 | C | |
| 02 | MI | 47.9% | 95.8% | 25 | C | |
| 03 | MA | 47.1% | 91.7% | 25 | C | |
| 04 | MI | 46.3% | 87.5% | 25 | C | |
| 05 | AN | 43.3% | 83.3% | 25 | C | |
| 06 | AM Nova 2 ProAmazon | 41.3% | 79.2% | 25 | C | |
| 07 | AN | 41.0% | 75.0% | 25 | C | |
| 08 | ZA | 40.5% | 70.8% | 25 | C | |
| 09 | ME | 39.5% | 66.7% | 25 | C | |
| 10 | AN | 39.2% | 62.5% | 25 | C | |
| 11 | DE | 37.7% | 58.3% | 25 | C | |
| 12 | ZA | 37.5% | 54.2% | 25 | C | |
| 13 | AN | 35.5% | 50.0% | 25 | C | |
| 14 | AN | 35.2% | 45.8% | 25 | C | |
| 15 | ME | 33.8% | 41.7% | 25 | C | |
| 16 | ZA | 33.3% | 37.5% | 25 | C | |
| 17 | AMNova 2 LiteAmazon | 32.5% | 33.3% | 25 | C | |
| 18 | DE | 31.3% | 29.2% | 25 | C | |
| 19 | XI | 30.5% | 25.0% | 25 | C | |
| 20 | ZA | 30.0% | 20.8% | 25 | C | |
| 21 | MA | 30.0% | 16.7% | 25 | C | |
| 22 | NV | 25.8% | 12.5% | 25 | C | |
| 23 | MA | 25.0% | 8.3% | 25 | C | |
| 24 | NV | 8.5% | 4.2% | 25 | C | |
| 25 | DE | 5.7% | 0.0% | 25 | C |
Terminal-Bench Score Distribution
A closer view of the leading scores on this benchmark.
Terminal-Bench Highlights
The leading models and scores on this benchmark.
What is Terminal-Bench?
What Terminal-Bench measures and how its scores work.
Terminal-Bench is a benchmark for testing AI agents in real terminal environments. It evaluates how well agents can handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, security tasks, data science workflows, and cybersecurity vulnerabilities. The benchmark consists of a dataset of ~100 hand-crafted, human-verified tasks and an execution harness that connects language models to a terminal sandbox.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Terminal-Bench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- terminal-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Terminal-Bench.
Which model scores highest on Terminal-Bench?
Claude Sonnet 4.5 is currently ranked first with 50.0%.
What does Terminal-Bench measure?
Terminal-Bench is a benchmark for testing AI agents in real terminal environments. It evaluates how well agents can handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, security tasks, data science workflows, and cybersecurity vulnerabilities. The benchmark consists of a dataset of ~100 hand-crafted, human-verified tasks and an execution harness that connects language models to a terminal sandbox.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
25 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.