The AI arena is free today

Open Superagent

Terminal-Bench 4.0-AA

PaperImplementation

Progress Over Time

Interactive timeline showing model performance evolution on Terminal-Bench 4.0-AA

State-of-the-art frontier
Open
Proprietary

Terminal-Bench 4.0-AA Leaderboard

5 models
ContextCostLicense
1———
2———
2———
4———
4—1.0M$4.00 / $20.00
Notice missing or incorrect data?
About this benchmark

What is Terminal-Bench 4.0-AA?

Artificial Analysis reported accuracy on terminal tasks with executable final-container-state verifiers.

Terminal-Bench 4.0-AA is a text benchmark evaluating models on agents, code, and tool calling tasks. LLM Stats tracks 5 models on this benchmark, scored on a 0–1 scale. The current average is 0.6, with the leader at 0.7.

Compare leaders on the best AI for agents, best AI for code and best AI for tool calling leaderboards.

Current leaders

Gemini 4 Carbon from Google currently leads the Terminal-Bench 4.0-AA leaderboard with a score of 0.652 across 5 evaluated AI models.

1Gemini 4 CarbonGoogle65.2%

FAQ

Common questions about the Terminal-Bench 4.0-AA benchmark and leaderboard.

What is the Terminal-Bench 4.0-AA benchmark?

Artificial Analysis reported accuracy on terminal tasks with executable final-container-state verifiers.

What is the Terminal-Bench 4.0-AA leaderboard?

The Terminal-Bench 4.0-AA leaderboard ranks 5 AI models based on their performance on this benchmark. Currently, Gemini 4 Carbon by Google leads with a score of 0.652. The average score across all models is 0.567.

What is the highest Terminal-Bench 4.0-AA score?

The highest Terminal-Bench 4.0-AA score is 0.652, achieved by Gemini 4 Carbon from Google.

How many models are evaluated on Terminal-Bench 4.0-AA?

5 models have been evaluated on the Terminal-Bench 4.0-AA benchmark, with 0 verified results and 5 self-reported results.

Where can I find the Terminal-Bench 4.0-AA paper?

The Terminal-Bench 4.0-AA paper is available at https://research.meta.ai/static/muse-wonder-model-card. The paper details the methodology, dataset construction, and evaluation criteria.

Where can I find the Terminal-Bench 4.0-AA dataset?

The Terminal-Bench 4.0-AA dataset is available at https://research.meta.ai/blog/muse-wonder.

What categories does Terminal-Bench 4.0-AA cover?

Terminal-Bench 4.0-AA is categorized under agents, code, and tool calling. The benchmark evaluates text models.

What's the difference between Terminal-Bench 4.0-AA and Terminal-Bench 4.0?

Terminal-Bench 4.0-AA is a variant of Terminal-Bench 4.0. See the Terminal-Bench 4.0 leaderboard for the broader benchmark and per-model comparison.

How recent are the Terminal-Bench 4.0-AA leaderboard results?

The Terminal-Bench 4.0-AA leaderboard was last updated in October 2026 and currently includes 5 evaluated models.