Three AI model releases landed in the same news cycle this week. Together they point to a shift that matters more to humanoid robot buyers than any single spec sheet update: the model running inside a robot is becoming as consequential a purchase decision as the robot itself.
Google, Nvidia, and Meta each released multimodal models aimed, directly or indirectly, at robotics and embodied AI. The releases differ in architecture and openness, but they share a common thread: the software layer that interprets a robot's cameras, plans its actions, and gets trained on new tasks is now a separate, competitive market from the hardware it runs on.
Gemini 4 Argon: built for robot control
According to The Decoder, Google's Gemini 4 Argon is positioned to control robots, generate simulations from photos or sketches, and evaluate training videos. That is a different pitch from a general-purpose chatbot retrofitted for robotics. Argon is built around the loop a robot manufacturer actually runs: it captures what a robot's cameras see, simulates variations of that scene to generate more training data, reviews footage of attempted tasks, and issues control commands based on what it has learned.
On Google's own ERQA benchmark, Gemini 4 Argon scores 87 percent, per The Decoder. That figure comes from a benchmark Google itself designed and ran, not an independent test, so it is best read as a vendor claim rather than a verified result.
For a buyer, the question of which foundation model sits behind a robot's perception and planning stack is now as relevant as the reach or payload figures tracked on Compare Robots' humanoid spec sheet.
The open-weight alternative: Nvidia and Meta
Gemini 4 Argon is closed and proprietary. A manufacturer that adopts it ties its product's future capability growth to Google's roadmap and commercial terms. Nvidia and Meta released alternatives that let manufacturers keep that control in-house.
Nvidia's Nemotron 3.5 Super VL is an open 120-billion-parameter multimodal mixture-of-experts model with 12 billion active parameters and a 1 million-token context window, according to MarkTechPost. Its NVFP4 weight format lets it run on a single DGX Spark. The Decoder reports it ships alongside two compact Bolt models built to bring AI agents to local machines rather than the cloud, and that dozens of organizations are already fine-tuning Nemotron 3.5 Super VL for enterprise agents. ZDNet Robotics notes the release joins a wave of open-weight models from US labs following the Hugging Face hack.
Meta's release is larger and more specialized. Muse Spark Open is an 816-billion-parameter open-weight mixture-of-experts model with 42 billion active parameters and a 1 million-token context window, per MarkTechPost, and it runs on two M5 Ultra Mac Studios rather than data-center infrastructure. MarkTechPost also reports that Muse Spark Open leads open models on the MCP-Atlas benchmark.
What this means for a buying decision
None of these three releases is a humanoid robot, and none has been demonstrated running a commercially shipped humanoid platform end to end, on the facts available here. The relevance to buyers is structural rather than a direct product claim: the model ecosystem a robot's software stack can draw on affects how fast that robot's capabilities improve after purchase.
A closed model like Gemini 4 Argon gives a manufacturer a capable, maintained system without the cost of training one from scratch, at the price of dependency on Google's release schedule and terms. Open-weight models like Nemotron 3.5 Super VL and Muse Spark Open let a manufacturer fine-tune, audit, and run the model on its own infrastructure, at the cost of needing the in-house expertise to do that well. Neither path is obviously better. The trade-off is real, and it is still playing out.
For anyone comparing platforms on Compare Robots' best humanoid robots list or tracking cost on the humanoid robot price trackerthe practical move is to ask a vendor directly which model, or family of models, powers perception and control, and whether that choice is open or closed. A robot's hardware is fixed at purchase. Its software is not. That is where the next round of capability gains will come from.