Two big families dominate the conversation around “AI that understands the world”:

• LLMs (Large Language Models), optimized to model token sequences (text first, and increasingly multimodal).
• World models, built to learn dynamics (how a state evolves when actions are applied) so they can simulate, plan, and control.

So, “which one is best?” doesn’t have a single answer.

Definition: what do we call a World Model?

A world model learns a latent representation of an environment and a transition function: if I’m in state X and I take action A, what happens next?

The goal is not only to predict, but to imagine possible futures and pick actions accordingly.

A few well-known examples (in the broad sense):

• DreamerV3, which learns a world model and “imagines” trajectories to optimize a control policy across many environments. It trains by trying actions inside the simulated world, instead of relying only on real-world trial and error.
• Foundation-style interactive world models like Genie 2 and Genie 3 at DeepMind, aiming to generate playable, actionable environments. For example: generate a mini interactive game from a video, and when you press “right” it outputs the next consistent state, as if you were actually playing.
• Non-generative video representation approaches like V-JEPA, which learn by predicting in latent space rather than generating pixel-perfect frames. The model learns to anticipate what happens next in a video, focusing on the important concepts.

Definition: what do we call an LLM?

An LLM is a model that learns statistical patterns from huge amounts of data, historically text. Modern LLMs extend into multimodal inputs (image, audio, video) while keeping a token-centric approach.

One important point: an LLM can “reason” in the sense that it produces useful inference chains, but it’s not inherently a simulator of an environment. That said, it can emulate simulation when the task can be captured well through language.

World Models vs LLMs: formal evaluation criteria

1) Accurate dynamics prediction: advantage World Model

If the task requires predicting the consequences of actions inside a system (physics, UI, games, robotics, logistics), world models are usually a better fit.

• DreamerV3 shows how imagination (planning inside a learned model) can solve many different tasks with one core algorithm.
• Genie 2/3 focus on generated interactive environments, with an agent-first perspective.

💡 What this means in practice: when “getting dynamics wrong” is expensive (robots, operational optimization, agentic UX inside software), you want a model that truly links action → consequence, not just something that produces plausible text.

2) General knowledge + instruction following: advantage LLM

For answering questions, summarizing, coding, and explaining, LLMs often have a huge edge because they benefit from:

• broad knowledge coverage,
• a natural interface (language),
• strong alignment through instruction tuning.

💡 In practice: if the environment is not the core difficulty (or if it’s stable and easy to describe), an LLM is often the most cost-effective solution. On the other hand, LLMs struggle more when the environment is hard to describe with text alone.

3) Long-horizon planning: it depends

• World models naturally plan by simulating forward, but they can suffer when the model drifts (errors accumulate) or when the real environment is too complex.
• LLMs plan through heuristics and reasoning chains, which can be surprisingly effective, but without guarantees of dynamic consistency.

The “world simulator” angle on video generation (for example, the way OpenAI presents Sora) shows this convergence: generative models are pushing toward enough spatio-temporal consistency to start feeling like simulations.

A simple decision framework

World Models win when you need control, interaction, and autonomy

• Control (choosing optimal actions under constraints)
• Interaction loops (perception → action). Meaning: a system observes the world state (what it “sees”: a screen, sensors, data), decides what to do (selects an action), acts (click, typing, movement, API call), re-observes the result, and repeats.
• Action robustness (actions change the world, not just the text)

LLMs win when you need language, knowledge, and productivity

• Language understanding and generation
• Programming and tool orchestration
• Conversational interfaces (ChatOps, internal copilots)

The limitations of each approach

World Model limitations

• Cost and complexity: learning a realistic, stable, controllable dynamic model is hard.
• Generalization: a world model trained on one environment type may transfer poorly to another.
• Evaluation: measuring “world understanding” is less straightforward than benchmarking NLP tasks.

LLM limitations

• Hallucinations: sounding plausible does not mean being correct.
• Causality and action: an LLM can explain causality without being able to simulate it accurately.
• Agent reliability: once autonomous, execution mistakes get expensive fast (clicks, purchases, irreversible actions).

Real-world use cases: choosing based on what you’re building

If you’re building a CRM, ERP, or SaaS

• LLM-first if your value is search, writing, customer support, ticket routing, and similar workflows.
• World model / simulator-first if your value is sequential decision-making and process optimization (planning, supply chain, dynamic pricing), or if you want an agent operating a complex UI with feedback.

If you’re building an agent that acts in the world

“My agent needs to click, navigate, test scenarios, and reduce risk.”
➡️ A hybrid approach is usually the best option: use an LLM to plan, and a simulator (or sandbox environment) to validate before executing in the real world.

If you’re targeting robotics, industry, XR, or games

World models become central, because action, friction, collisions, and time are the core of the problem.

Conclusion

The right answer isn’t “world models vs LLMs.” It’s “world models + LLMs.”

• LLMs are the best general-purpose engine for language, knowledge, orchestration, and interface.
• World models are the strongest candidates for simulation, planning, and control in dynamic environments.
• Hybrid systems are where truly useful agents emerge, because they can talk, reason, and test before acting.

A simple rule of thumb:

• If your product is mostly text and productivity-driven, start with an LLM.
• If it’s mostly interaction, action, and consequences, think world model (at least as a simulator/sandbox), then layer an LLM on top.