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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
This hub publishes comparison pages designed for direct answers, explicit trade-offs, and source-backed verification.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-06-21.
Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.
Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. See the editorial policy.
Static overview of the comparison catalog.
Editorial provenance and refresh policy preserved directly in static HTML.
Published by world-models.io editorial board.
Lead editor Bernard Grenat.
This hub publishes comparison pages designed for direct answers, explicit trade-offs, and source-backed verification.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-06-21.
Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.
Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. See the editorial policy.
Representative comparisons rendered directly in static HTML.
| Comparison | Item A | Item B | Summary |
|---|---|---|---|
| 3D-VLA vs I-JEPA | 3D-VLA | I-JEPA | Two approaches to learning representations for embodied intelligence: 3D-VLA combines 3D perception with language-conditioned action planning, while I-JEPA learns abstract visual representations through self-supervised prediction in latent space. |
| AMI vs Ha World Model | AMI | Ha World Model | Two pioneering cognitive-inspired world models: Ha's 2018 World Model introduced the VAE+RNN+Controller architecture, while AMI proposes an autonomous machine intelligence framework inspired by biological cognition. |
| Copilot4D vs GAIA-1 | Copilot4D | GAIA-1 | Both target autonomous driving simulation but from different angles: Copilot4D predicts 4D point cloud futures for safety-critical planning, while GAIA-1 generates photorealistic driving video for scenario exploration. |
| Copilot4D vs GAIA-1 | Copilot4D | GAIA-1 | Two autonomous driving world models with different approaches: Copilot4D uses discrete tokenization for LiDAR point cloud forecasting, while GAIA-1 generates photorealistic driving videos from multimodal inputs. |
| DreamerV2 vs DreamerV3 | DreamerV2 | DreamerV3 | The Dreamer lineage's two most impactful iterations: DreamerV2 achieved human-level Atari with discrete representations, while DreamerV3 eliminated hyperparameter tuning entirely with symlog predictions. |
| DreamerV2 vs PlaNet | DreamerV2 | PlaNet | Both use the RSSM architecture for latent dynamics, but DreamerV2 introduced discrete representations that dramatically improved performance. PlaNet pioneered the approach; DreamerV2 perfected it for Atari-scale environments. |
| DreamerV3 vs DIAMOND | DreamerV3 | DIAMOND | DreamerV3 and DIAMOND are both model-based RL agents that train policies via imagination, but they use fundamentally different dynamics models: RSSM latent dynamics vs. pixel-space diffusion models. |
| DreamerV3 vs IRIS | DreamerV3 | IRIS | Two model-based RL agents using fundamentally different world model architectures: DreamerV3's RSSM with actor-critic vs. IRIS's autoregressive Transformer with VQ-VAE tokens. |
| DreamerV3 vs MuZero | DreamerV3 | MuZero | Both are landmark world model systems, but with fundamentally different architectures. DreamerV3 uses latent imagination with actor-critic learning, while MuZero uses abstract learned dynamics with Monte Carlo tree search. |
| DreamerV3 vs PlaNet | DreamerV3 | PlaNet | DreamerV3 represents the evolution of PlaNet's core ideas. Both use the RSSM architecture, but DreamerV3 adds discrete representations, symlog predictions, and fixed hyperparameters to achieve state-of-the-art performance across diverse domains. |
| DreamerV3 vs PlayWorld | DreamerV3 | PlayWorld | Two green-index leaders for acting under learned dynamics, but with different centers of gravity. DreamerV3 is the canonical imagination-based general RL agent, while PlayWorld is a manipulation-centric robot simulator trained from autonomous play data. |
| DreamerV3 vs TD-MPC2 | DreamerV3 | TD-MPC2 | Two leading model-based RL agents with different philosophies: DreamerV3 uses imagination-based actor-critic learning, while TD-MPC2 combines temporal-difference learning with model-predictive control for multi-task mastery. |
| Emu Video vs Sora | Emu Video | Sora | Both are frontier video generation models, but with different ambitions: Emu Video focuses on efficient, high-quality short-form generation, while Sora pushes toward long-form, physically coherent world simulation. |
| GAIA-1 vs Copilot4D | GAIA-1 (Wayve) | Copilot4D (Waabi) | Both are world models designed for autonomous driving, but they operate on different sensor modalities: GAIA-1 generates camera video, while Copilot4D predicts LiDAR point clouds in 4D. |
| GAIA-1 vs NVIDIA Cosmos | GAIA-1 | NVIDIA Cosmos | Both are video-based world models for autonomous driving and physical AI, but GAIA-1 is a domain-specific driving world model from Wayve while Cosmos is a general-purpose foundation platform from NVIDIA. |
| GAIA-1 vs Sora | GAIA-1 | Sora | Two generative world models that approach video generation from different angles: GAIA-1 focuses on autonomous driving simulation, while Sora aims to be a general-purpose visual world simulator. |
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| Comparison | Item A | Item B | Dimensions | FAQs |
|---|---|---|---|---|
| 3D-VLA vs I-JEPA | 3D-VLA | I-JEPA | 7 | 1 |
| AMI vs Ha World Model | AMI | Ha World Model | 6 | 1 |
| Copilot4D vs GAIA-1 | Copilot4D | GAIA-1 | 7 | 1 |
| Copilot4D vs GAIA-1 | Copilot4D | GAIA-1 | 6 | 1 |
| DreamerV2 vs DreamerV3 | DreamerV2 | DreamerV3 | 6 | 2 |
| DreamerV2 vs PlaNet | DreamerV2 | PlaNet | 7 | 2 |
| DreamerV3 vs DIAMOND | DreamerV3 | DIAMOND | 7 | 2 |
| DreamerV3 vs IRIS | DreamerV3 | IRIS | 7 | 1 |
| DreamerV3 vs MuZero | DreamerV3 | MuZero | 7 | 1 |
| DreamerV3 vs PlaNet | DreamerV3 | PlaNet | 7 | 1 |
| DreamerV3 vs PlayWorld | DreamerV3 | PlayWorld | 7 | 2 |
| DreamerV3 vs TD-MPC2 | DreamerV3 | TD-MPC2 | 7 | 2 |
| Emu Video vs Sora | Emu Video | Sora | 7 | 1 |
| GAIA-1 vs Copilot4D | GAIA-1 (Wayve) | Copilot4D (Waabi) | 7 | 2 |
| GAIA-1 vs NVIDIA Cosmos | GAIA-1 | NVIDIA Cosmos | 7 | 1 |
| GAIA-1 vs Sora | GAIA-1 | Sora | 7 | 1 |
| GameNGen vs DIAMOND | GameNGen | DIAMOND | 7 | 1 |
| Genie 2 vs UniSim | Genie 2 | UniSim | 7 | 2 |
| Genie 3 vs Genie 2 | Genie 3 | Genie 2 | 7 | 2 |
| Genie 3 vs NVIDIA Cosmos | Genie 3 | NVIDIA Cosmos | 7 | 2 |
| Genie 3 vs V-JEPA 2 | Genie 3 | V-JEPA 2 | 7 | 2 |
| Genie vs Genie 2 | Genie (v1) | Genie 2 | 7 | 1 |
| Ha & Schmidhuber World Model vs DreamerV3 | Ha & Schmidhuber World Model | DreamerV3 | 7 | 1 |
| I-JEPA vs MAE (Masked Autoencoders) | I-JEPA (Meta FAIR) | MAE (Meta / He et al.) | 7 | 1 |
| IRIS vs DIAMOND | IRIS | DIAMOND | 6 | 1 |
| IRIS vs DreamerV3 | IRIS | DreamerV3 | 7 | 1 |
| LeWorldModel vs DreamerV3 | LeWorldModel | DreamerV3 | 7 | 2 |
| LWM vs V-JEPA | Large World Model (LWM) | V-JEPA | 6 | 1 |
| MILE vs GAIA-1 | MILE | GAIA-1 | 6 | 1 |
| Model-Based RL vs Model-Free RL | Model-Based RL | Model-Free RL | 7 | 2 |
| MuZero vs DreamerV3 | MuZero | DreamerV3 | 8 | 2 |
| MuZero vs TD-MPC2 | MuZero | TD-MPC2 | 7 | 2 |
| NVIDIA Cosmos vs DreamerV3 | NVIDIA Cosmos | DreamerV3 | 7 | 1 |
| NVIDIA Cosmos vs Genie 2 | NVIDIA Cosmos | Genie 2 | 7 | 2 |
| NVIDIA Cosmos vs V-JEPA 2 | NVIDIA Cosmos | V-JEPA 2 | 7 | 2 |
| OASIS vs DIAMOND | OASIS | DIAMOND | 7 | 2 |
| OASIS vs GameNGen | OASIS (Decart) | GameNGen (Google Research) | 7 | 2 |
| OASIS vs Pandora | OASIS | Pandora | 6 | 1 |
| Pandora vs Genie 2 | Pandora | Genie 2 | 7 | 2 |
| Pandora vs OASIS | Pandora | OASIS | 6 | 1 |
| PixVerse R1 vs Sora | PixVerse R1 | Sora | 7 | 2 |
| PlayWorld vs TD-MPC2 | PlayWorld | TD-MPC2 | 7 | 2 |
| PlayWorld vs V-JEPA 2 | PlayWorld | V-JEPA 2 | 7 | 2 |
| Predictron vs MuZero | Predictron | MuZero | 7 | 1 |
| Predictron vs MuZero | Predictron | MuZero | 6 | 1 |
| RT-2 vs 3D-VLA | RT-2 | 3D-VLA | 6 | 1 |
| Sora vs Emu Video | Sora | Emu Video | 7 | 1 |
| Sora vs Gen-3 Alpha | Sora | Gen-3 Alpha | 7 | 2 |
| Sora vs Genie 2 | Sora (OpenAI) | Genie 2 (DeepMind) | 7 | 2 |
| Sora vs Genie 3 | Sora | Genie 3 | 8 | 2 |
| Sora vs NVIDIA Cosmos | Sora (OpenAI) | NVIDIA Cosmos | 7 | 2 |
| Stable Video Diffusion vs Emu Video | Stable Video Diffusion | Emu Video | 6 | 1 |
| STEVE-1 vs DreamerV3 | STEVE-1 | DreamerV3 | 6 | 1 |
| UniSim vs Genie 2 | UniSim | Genie 2 | 7 | 1 |
| UniSim vs Genie 2 | UniSim | Genie 2 | 6 | 1 |
| V-JEPA 2 vs I-JEPA | V-JEPA 2 | I-JEPA | 8 | 2 |
| V-JEPA 2 vs V-JEPA | V-JEPA 2 | V-JEPA | 6 | 1 |
| V-JEPA vs I-JEPA | V-JEPA | I-JEPA | 7 | 2 |
| V-JEPA vs NVIDIA Cosmos | V-JEPA | NVIDIA Cosmos | 7 | 1 |
| V-JEPA vs Video Generation Models | V-JEPA (Meta) | Video Generation Models (Sora, Cosmos) | 7 | 2 |
| World Models vs LLMs | World Models | Large Language Models | 7 | 1 |
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