reasoning benchmark
Terminus is a neutral test-bed agent designed to work with Terminal-Bench in real terminal environments.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score25.00% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis terminus AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Kimi K2 Instruct currently leads Terminus with 25.00%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What Terminus measures and how its scores work.
Terminal-Bench is a benchmark for testing AI agents on real-world, end-to-end tasks in real terminal environments, and Terminus is a test-bed agent for it.
It measures how well agents handle coding, system administration, security, data science, model training, file operations, version control, and web development tasks autonomously, using Score with unit ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Terminus.
Kimi K2 Instruct is currently ranked first with 25.00%.
The current leaders are Kimi K2 Instruct (25.00%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures how well agents handle coding, system administration, security, data science, model training, file operations, version control, and web development tasks autonomously, using Score with unit ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.