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Google's official Developers Blog post confirms Gemini 4 Argon scores 87% on the Embodied Reasoning (ERQA) benchmark and details its robotics capabilities.

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Home/Tech/Gemini 4 Argon scores 87% on embodied reasoning benchmark
VERIFIEDBy Xavier Rivera· ·1 min read

Gemini 4 Argon scores 87% on embodied reasoning benchmark

Google's Gemini 4 Argon is a robotics-focused model that scores 87% on the Embodied Reasoning benchmark. The model enables new control, simulation, and self-improvement workflows across the robotics stack.

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Gemini 4 Argon scores 87% on embodied reasoning benchmark
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TL;DRAI · 60 sec read

Gemini 4 Argon scores 87% on the Embodied Reasoning benchmark. The robotics-focused model outperforms every other frontier model on the ERQA test by using pure reasoning to solve physical and spatial problems in 3D environments. It supports four robotics workflows, controls a Boston Dynamics Spot quadruped, and performs zero-shot arm tasks.

Google's Gemini 4 Argon is a model variant focused on robotics applications that improves physical understanding, code generation, and visual-semantic reasoning.
Argon reaches 87% on the Embodied Reasoning benchmark.
Argon reaches 87% on the Embodied Reasoning benchmark. The model uses pure reasoning to solve physical and spatial problems in 3D environments. It outperforms every other frontier model on the ERQA test.
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Argon supports four robotics workflow areas. These include robot control with mobile manipulation and closed-loop steering, Real2Sim reconstruction of simulation twins from camera feeds, physical understanding through multimodal labeling, and research via simulation-based self-improvement.
Argon controls a Boston Dynamics Spot quadruped.
Argon controls a Boston Dynamics Spot quadruped. The model creates an articulated MuJoCo digital twin of a foot-pedal trash bin to practice before executing the task on hardware. It picks up soda cans, verifies foot placement on the pedal with a two-agent swarm, and drops the cans into the bin.
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Argon performs zero-shot robotic arm tasks. It reorients a fallen mug upright and places a crumpled napkin inside using visual coordinate references and action primitives. The system outputs observations followed by commands based on wrist camera images and observation JSON data.
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