Artificial Analysis finds GPT-6 Astra leads its first embodied benchmark, but every model remains far slower than a human
Large language models can write essays, debug code and summarize earnings calls. Now someone has handed them a robot arm and asked them to write “hello world” with a pen.
On October 14, 2026, Artificial Analysis released AA-Robotics v0.1. The research preview tests how well frontier language models understand, plan and act in the physical world.
How the benchmark works
The setup is intentionally simple. Each model gets control of a Hiwonder NexArm, a 6-axis robot arm. It also gets a single, plainly stated objective.
The models operate through a coding-agent interface connected to a simple HTTP API. For eyes, the system relies on four cameras that feed visual information back to the model.
The tasks run zero-shot. Each model attempts every task without prior practice or task-specific training.
Artificial Analysis is publishing results for its first eight tasks. Examples include pouring water, stacking blocks and writing “hello world.” A complete success earns 100 points, and partial credit rewards progress on attempts that stop short of the finish line.
The leaderboard
OpenAI’s GPT-6 Astra sits at the top with an average score of 69.4/100 across the eight tasks. Another OpenAI model, GPT-6.1 Sol, came second at 64.6.
Google’s Gemini 4 Argon placed third with 55.3.
The “hello world” task offers the clearest look at how differently these models approach the same problem. GPT-6 Astra finished the writing exercise in 514 arm moves and 13 minutes.
Anthropic’s Claude Opus 5.5 got there too, eventually. It needed 3,270 moves and 34 minutes. That is more than six times as many moves for the same two words.
The speed problem
Humans complete these tasks in about 20 seconds on average. The AI models take approximately 30 minutes.
Artificial Analysis puts the gap at roughly 100 times slower than people. The models are also substantially more expensive than human labor for the same work.
Why this benchmark exists
Most AI benchmarks measure what models can do on a screen. They test reasoning, coding, math and reading comprehension. Physical tasks add a layer that text benchmarks miss entirely: a model has to translate an abstract goal into a sequence of real movements, deal with imperfect sensor data and recover when a block slips or a pen skids.
Artificial Analysis says progress on these embodied capabilities has been accelerating in 2026. It also says efficiency and reliability still need substantial improvement before deployment in real-world settings becomes practical.
What this means
OpenAI holds the top two spots in the first release. Google sits third, and Anthropic’s flagship trails noticeably on at least one task.
AA-Robotics shows that using general-purpose language models as the brain in a zero-shot setting can work. It also shows the cost of that generality is measured in minutes per task rather than seconds.
A system that is around 100 times slower and costlier than a person is not replacing anyone on a production line yet.
