Best robotics foundation model
4 models · updated 2026-07-15
The verdict
Gemini Robotics 1.5 leads — 0 of 4 models rank Gemini Robotics 1.5 the top pick.
Not unanimous: ChatGPT picks NVIDIA Isaac GR00T N1.7; Claude picks Physical Intelligence π0.5; Gemini picks OpenVLA; Grok picks NVIDIA Isaac GR00T N1.7.
As of 2026-07-15, ChatGPT, Claude, Gemini and Grok collectively rank Gemini Robotics 1.5 #1 for robotics foundation model on ModelsAgree by aggregate score, though no single model picks it first. The models' case: Strongest demonstrated general-purpose VLA for semantic, visual, action, and task generalization, with cross-embodiment transfer and unusually capable long-horizon. The models' main caveat: Limited partner access and no open weights make it unsuitable for practitioners needing self-service deployment or deep customization. The strongest alternative is NVIDIA Isaac GR00T N1.7 — Best overall practitioner value: an open, commercially usable 3B VLA backed by a complete workflow for teleoperation, synthetic data, simulation. Not unanimous: ChatGPT picks NVIDIA Isaac GR00T N1.7; Claude picks Physical Intelligence π0.5; Gemini picks OpenVLA; Grok picks NVIDIA Isaac GR00T N1.7. Source: https://modelsagree.com/best/best-robotics-foundation-model (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #2Claude #2Gemini —Grok #4
Strongest demonstrated general-purpose VLA for semantic, visual, action, and task generalization, with cross-embodiment transfer and unusually capable long-horizon reasoning when paired with Gemini Robotics-ER
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GPT Strongest demonstrated general-purpose VLA for semantic, visual, action, and task generalization, with cross-embodiment transfer and unusually capable long-horizon reasoning when paired with Gemini Robotics-ER
Claude The highest raw capability in the category — embodied reasoning (ER 1.5 via the Gemini API), agentic multi-step planning, cross-embodiment transfer, and an on-device variant; near-tie with GR00T N1.5, ranked above it on capability, below π0.5 on access.
Grok Excellent long-horizon reasoning, spatial/multi-view understanding, and agentic capabilities via Gemini backbone; strong partnerships (Boston Dynamics Atlas, Apptronik) for industrial/general deployment.
Where it falls shortper GPT Limited partner access and no open weights make it unsuitable for practitioners needing self-service deployment or deep customization
per Claude The full VLA is gated behind a trusted-tester program rather than open weights, so most teams can only touch the ER reasoning layer via API — not for anyone who needs to own and modify the policy.
per Grok Limited public access (partner-only); higher latency/compute demands vs. compact on-device options.
- 2GPT #1Claude —Gemini —Grok #1
Best overall practitioner value: an open, commercially usable 3B VLA backed by a complete workflow for teleoperation, synthetic data, simulation, post-training, evaluation, ROS, and Jetson deployment; near-tied with Gemini on model quality but substantially easier to obtain and adapt
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GPT Best overall practitioner value: an open, commercially usable 3B VLA backed by a complete workflow for teleoperation, synthetic data, simulation, post-training, evaluation, ROS, and Jetson deployment; near-tied with Gemini on model quality but substantially easier to obtain and adapt
Grok Leading open, commercially viable VLA foundation model for humanoids with massive scaling via EgoScale (20k+ hours egocentric video), Cosmos integration for rapid synthetic data, proven validation on real bimanual/loco-manipulation across Unitree G1, Agility, etc.; broad ecosystem/adopters and simulation-to-real strengths make it highest real-world value for practitioners building/deploying general-purpose systems.
Where it falls shortper GPT Optimized primarily for humanoids and still requires embodiment-specific data, serious NVIDIA compute, and extensive validation
per Grok Still emerging in full production-scale deployments; best for teams with simulation/GPU resources, not pure low-compute edge without fine-tuning.
- 3GPT —Claude #4Gemini #1Grok #5
Provides the strongest open-source zero-shot performance and semantic language grounding for manipulation tasks (near-tied with π0, which we rank second due to deployment compute costs), serving as the standard baseline for custom fine-tuning.
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Gemini Provides the strongest open-source zero-shot performance and semantic language grounding for manipulation tasks (near-tied with π0, which we rank second due to deployment compute costs), serving as the standard baseline for custom fine-tuning.
Claude The fully open, Apache-licensed 7B workhorse — transparent training recipe, huge academic adoption, strong fine-tuned results with the OFT recipe, and the easiest model to inspect, ablate, and publish against.
Grok Best open-source baseline—outperforms larger closed models like RT-2-X on diverse embodiments with efficient fine-tuning/LoRA/quantization; accessible, community-supported for typical practitioners experimenting/customizing on varied hardware.
Where it falls shortper Claude An aging architecture trained largely on Open X-Embodiment data — zero-shot and long-horizon performance clearly trail the 2025-generation models, so it's a research baseline more than a production brain.
per Gemini Its autoregressive token generation limits control frequency to 5-10 Hz, making it unsuitable for highly dynamic, high-speed physical reactions.
per Grok Smaller scale than frontier closed models; requires more task-specific data/fine-tuning for peak real-world dexterity in complex dynamic settings.
- 4GPT —Claude —Gemini #2Grok #2
Leverages flow matching to generate continuous motor commands at 50 Hz, enabling extremely smooth control (near-tied with OpenVLA for manipulation capability, but requires heavier compute) and complex dual-arm manipulation.
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Gemini Leverages flow matching to generate continuous motor commands at 50 Hz, enabling extremely smooth control (near-tied with OpenVLA for manipulation capability, but requires heavier compute) and complex dual-arm manipulation.
Grok Strongest dexterity and open-world generalization (compositional skills, unseen environments like kitchens/laundry via flow-matching + RECAP); real deployments with partners, steerable emergent capabilities position it as top for versatile physical performance across arms/manipulators.
Where it falls shortper Gemini The continuous diffusion sampling process is computationally heavy, requiring high-end local GPU acceleration for real-time edge inference.
per Grok Partnership/access focused (not fully self-hostable for all); more research-oriented for cutting-edge generalization than turnkey production for broad hardware.
- 5GPT #3Claude #1Gemini —Grok —
The strongest openly available generalist VLA — π0/π0.5 weights and the openpi codebase are public, it demonstrated genuine open-world generalization (cleaning unseen homes, long-horizon manipulation), and it has become the de-facto base model practitioners actually fine-tune on their own robots via LeRobot/openpi; rank assumes the practitioner wants a model they can run and adapt, not just admire.
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Claude The strongest openly available generalist VLA — π0/π0.5 weights and the openpi codebase are public, it demonstrated genuine open-world generalization (cleaning unseen homes, long-horizon manipulation), and it has become the de-facto base model practitioners actually fine-tune on their own robots via LeRobot/openpi; rank assumes the practitioner wants a model they can run and adapt, not just admire.
GPT The strongest broadly accessible cross-embodiment manipulation model, with open checkpoints, JAX and PyTorch implementations, 10,000-plus hours of pretraining, strong open-world generalization, and excellent fine-tuned LIBERO results
Where it falls shortper GPT Adaptation remains compute- and data-intensive, and performance can be unreliable on embodiments unlike Physical Intelligence’s training platforms
per Claude Needs serious GPU compute and quality teleop data to fine-tune well, and Physical Intelligence offers no commercial support or hardware ecosystem — you assemble the stack yourself.
- 6GPT #4Claude —Gemini —Grok #3
Optimized end-to-end for humanoid upper-body dexterity (high-rate continuous control, onboard low-power inference, single weights for diverse household/logistics tasks); tight hardware-software integration delivers proven real-robot manipulation without heavy fine-tuning.
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Grok Optimized end-to-end for humanoid upper-body dexterity (high-rate continuous control, onboard low-power inference, single weights for diverse household/logistics tasks); tight hardware-software integration delivers proven real-robot manipulation without heavy fine-tuning.
GPT The most convincing whole-body humanoid system demonstrated in real environments, integrating vision, touch, language, locomotion, balance, dexterous manipulation, and multi-minute task execution; near-tied with the leaders on embodied capability
Where it falls shortper GPT Proprietary and effectively inseparable from Figure 03 hardware, so it is not a practical foundation model for outside robot developers
per Grok Primarily tied to Figure robots/ecosystem; less open or transferable for arbitrary general-purpose hardware compared to broader VLAs.
- 7GPT —Claude —Gemini #3Grok —
Unmatched end-to-end integration with the Isaac Lab simulation pipeline and Jetson Thor compute, accelerating sim-to-real transfer for humanoid whole-body control.
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Gemini Unmatched end-to-end integration with the Isaac Lab simulation pipeline and Jetson Thor compute, accelerating sim-to-real transfer for humanoid whole-body control.
Where it falls shortper Gemini Highly vendor-locked to NVIDIA proprietary hardware and software ecosystem, limiting flexibility and portability.
- 8GPT —Claude #3Gemini —Grok —
Open weights plus the most complete practitioner ecosystem — Isaac Sim/Lab integration, synthetic-data pipelines (GR00T-Dreams), cross-embodiment humanoid support, and commercial backing that de-risks adoption for teams standardizing on NVIDIA hardware.
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Claude Open weights plus the most complete practitioner ecosystem — Isaac Sim/Lab integration, synthetic-data pipelines (GR00T-Dreams), cross-embodiment humanoid support, and commercial backing that de-risks adoption for teams standardizing on NVIDIA hardware.
Where it falls shortper Claude Humanoid-centric and ecosystem-locked — its value drops sharply if you're not on NVIDIA's sim/hardware stack, and out-of-the-box dexterity trails π0.5 and Gemini on unstructured manipulation.
- 9GPT #5Claude —Gemini #5Grok —
A credible commercial omni-bodied foundation model spanning humanoids, quadrupeds, mobile manipulators, and industrial arms, with real deployment partnerships and a useful hierarchy combining high-level behavior with fast low-level control
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GPT A credible commercial omni-bodied foundation model spanning humanoids, quadrupeds, mobile manipulators, and industrial arms, with real deployment partnerships and a useful hierarchy combining high-level behavior with fast low-level control
Gemini Universal commercial model designed to run across diverse morphologies (wheels, arms, quadrupeds) with robust physical recovery capabilities.
Where it falls shortper GPT Closed, engagement-based access and sparse reproducible evaluation make capability and cost difficult for independent practitioners to verify
per Gemini Closed-source and restricted to proprietary API integration, which limits developer autonomy and offline customization.
- 10GPT —Claude —Gemini #4Grok —
Extremely lightweight and modular transformer-based diffusion policy that runs on commodity edge GPUs and easily adapts to custom robot configurations.
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Gemini Extremely lightweight and modular transformer-based diffusion policy that runs on commodity edge GPUs and easily adapts to custom robot configurations.
Where it falls shortper Gemini Lacks the rich visual-semantic grounding of larger models, struggling to process complex, multi-step natural language instructions.
- 11GPT —Claude #5Gemini —Grok —
Purpose-built for the typical practitioner's actual budget — sub-1B parameters, trains and runs on a single consumer GPU, tight LeRobot integration with cheap arms (SO-100/101), making it the lowest-friction on-ramp to real VLA work.
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Claude Purpose-built for the typical practitioner's actual budget — sub-1B parameters, trains and runs on a single consumer GPU, tight LeRobot integration with cheap arms (SO-100/101), making it the lowest-friction on-ramp to real VLA work.
Where it falls shortper Claude Small-model ceiling — it cannot match frontier generalization or dexterity, so it suits learning, prototyping, and narrow tasks rather than a genuinely general-purpose robot.
Just missed the top 5
GPT SmolVLA — exceptional low-cost open option for consumer hardware, but materially weaker on long-horizon and whole-body generality · Physical Intelligence π0.6 — promising experience-driven improvement, but not released as a generally usable practitioner model
Claude Figure Helix — impressive in-house humanoid VLA but fully proprietary — no weights, API, or access for anyone outside Figure
Gemini RT-2 — retains strong semantic reasoning but requires massive cloud-based compute infrastructure, making local real-time control impractical · RFM-1 — commercial roadmap and accessibility became highly fragmented following the core development team transition to Amazon
Grok Octo — strong compact open policy but lags frontier generalization/dexterity
By model
ChatGPT
- 1.NVIDIA Isaac GR00T N1.7
- 2.Gemini Robotics 1.5
- 3.Physical Intelligence π0.5
- 4.Figure Helix
- 5.Skild Brain
Claude
- 1.Physical Intelligence π0.5
- 2.Gemini Robotics 1.5
- 3.NVIDIA Isaac GR00T N1.5
- 4.OpenVLA
- 5.SmolVLA
Gemini
- 1.OpenVLA
- 2.Physical Intelligence π0
- 3.NVIDIA Isaac GR00T
- 4.Octo
- 5.Skild Brain
Grok
- 1.NVIDIA Isaac GR00T N1.7
- 2.Physical Intelligence π0
- 3.Figure Helix
- 4.Gemini Robotics 1.5
- 5.OpenVLA
Common questions
What is the best robotics foundation model according to AI models?
Gemini Robotics 1.5 leads. 0 of 4 models rank Gemini Robotics 1.5 the top pick. The current top 3: Gemini Robotics 1.5, NVIDIA Isaac GR00T N1.7, OpenVLA. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-07-15. Source: modelsagree.com.
Which robotics foundation model did each AI model pick first?
ChatGPT: NVIDIA Isaac GR00T N1.7. Claude: Physical Intelligence π0.5. Gemini: OpenVLA. Grok: NVIDIA Isaac GR00T N1.7.
Do the AI models agree on the best robotics foundation model?
Not unanimous. ChatGPT picks NVIDIA Isaac GR00T N1.7; Claude picks Physical Intelligence π0.5; Gemini picks OpenVLA; Grok picks NVIDIA Isaac GR00T N1.7.
How is this robotics foundation model ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best robotics foundation model” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-07-15. https://modelsagree.com/best/best-robotics-foundation-model (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand