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Compare models

The main robot “brains” (VLA models) compared across the 7 capabilities of an autonomous robot. For each capability, we use the model's best published success rate on the same reference test. “Not measured” means no paper has published a result: it is not a zero.

Choose up to 3 models:

Capability by capability

For a like-for-like comparison, each capability is measured on the same reference test for every model. End of the gray bar = ideal robot (100%); vertical line = current record on this test. “≈”: reference test missing, average of the capability's other tests.

Basic skills
Can it carry out a simple instruction?
Test: Instruction following (LIBERO)
π0.5
98%
OpenVLA-OFT
97%
GR00T N1.5
97%
Robustness
Does it hold up when conditions change?
Test: Robustness to the unexpected (LIBERO-Plus)
π0.5
87%
OpenVLA-OFT
75%
GR00T N1.5
not measured
Realism
Would its results hold on a real robot?
Test: Small arm: realism (SimplerEnv · WidowX VM)
π0.5
73%
OpenVLA-OFT
42%
GR00T N1.5
68%
Two arms
Can it coordinate two hands?
Test: Two arms, cluttered scene (RoboTwin 2.0 · Hard)
π0.5
87%
OpenVLA-OFT
78%
GR00T N1.5
not measured
Home
Can it manage in an unfamiliar home?
Test: Virtual kitchen (mobile arm) (RoboCasa · Panda)
π0.5
62%
OpenVLA-OFT
44%
GR00T N1.5
66%
Memory
Does it remember what it has seen or done?
Test: Working memory (RoboMME)
π0.5
18%
OpenVLA-OFT
≈ 28%
GR00T N1.5
not measured
Long missions
Can it carry out a long mission on its own?
Test: Long, unpredictable missions (RoboCerebra)
π0.5
not measured
OpenVLA-OFT
not measured
GR00T N1.5
not measured

π0.5

Physical Intelligence · 2025 · ≈3 billion parameters

Open-source

The most versatile of the open models: designed to work in homes it has never seen.

Autonomy level
1 / 5 Learns a skill
Capabilities measured
6 / 7

On real robots (RoboArena), wins about 32% of its head-to-head matchups against the top-ranked model on the official leaderboard.

Test breakdown (12)
  • Instruction following98%
  • Two arms, cluttered scene87%
  • Two arms, clean scene87%
  • Robustness to the unexpected87%
  • Small arm: realism73%
  • Google robot: realism73%
  • Google robot: varied scenery68%
  • Common sense and general knowledge62%
  • Virtual kitchen (mobile arm)62%
  • Traps and perturbations62%
  • Virtual kitchen (humanoid)37%
  • Working memory18%

OpenVLA-OFT

Stanford · 2025 · 7 billion parameters

Open-source

OpenVLA retrained with a better recipe: much more accurate and about 25 times faster.

Autonomy level
1 / 5 Learns a skill
Capabilities measured
6 / 7
Test breakdown (10)
  • Instruction following97%
  • Two arms, clean scene80%
  • Two arms, cluttered scene78%
  • Robustness to the unexpected75%
  • Google robot: realism63%
  • Google robot: varied scenery54%
  • Virtual kitchen (mobile arm)44%
  • Small arm: realism42%
  • Short-term memory28%
  • Grasping varied objects19%

GR00T N1.5

NVIDIA · 2025

Open-source

NVIDIA's model designed for humanoid robots, trained at scale in simulation.

Autonomy level
1 / 5 Learns a skill
Capabilities measured
3 / 7
Test breakdown (6)
  • Instruction following97%
  • Small arm: realism68%
  • Virtual kitchen (mobile arm)66%
  • Google robot: realism52%
  • Google robot: varied scenery52%
  • Virtual kitchen (humanoid)48%