Testing MiniMax-H3's Physical Intuition: A Leap Forward for Open-Source Video Models

An Ongoing Experiment in "Physical Intuition"

When evaluating video generation models, we tend to focus on image clarity, duration, and motion smoothness. But a deeper question is often overlooked: does the model truly "understand" the physical world? A Reddit user recently published a second round of physics knowledge tests targeting MiniMax-H3, attempting to answer this question from a refreshingly straightforward angle — does it actually understand the physical properties of matter?

The methodology is clever in its restraint: no external reference material is introduced. Instead, the tester takes a publicly available "pouring water" video and replaces the water with various different substances, observing how the model handles each material's behavior when poured. The first round of experiments focused on "liquid-for-liquid" substitutions. This time, the author escalated to solid objects — and even living creatures.

Why Use "Pouring Water" as a Physics Benchmark?

The pouring water scenario makes for an excellent physics test precisely because it depends heavily on a model's implicit understanding of fluid dynamics, gravity, viscosity, and surface tension. When water is replaced with another substance, the model must "infer" how the new material should move: honey should flow slowly and viscously, sand should scatter in granular form, and if the substance is a living creature, it should theoretically display autonomous movement rather than passively falling.

The value of this testing method lies in stripping away the distraction of texture and appearance to probe directly at the model's core "world model." A model that merely interpolates frames cannot pass this kind of test. Only a model that has genuinely internalized physical laws during training can produce a plausible dynamic response.

From Liquids to Solids: A Meaningful Jump in Difficulty

The most significant change in this round of experiments is the shift from liquids to solids and living objects. This transition may seem minor, but it substantially raises the bar.

The Challenge of Simulating Solid Physics

Liquids behave with continuity and consistency when poured, while solids exhibit far more varied and discrete behavior. Rigid solids maintain their shape, collide, and stack; granular solids scatter; flexible objects deform. The model must select an entirely different physical response mode based on the object's implied material properties. This requires the model not only to recognize "what this is," but also to understand "how it should move."

The Complexity of Modeling Living Behavior

Upgrading the substituted object to a living creature pushes the problem into yet another dimension. Living things don't just have physical properties — they have biological behavior. They struggle, move autonomously, and react to their environment. This goes beyond pure physical simulation and touches the boundary of a model's understanding of "living behavior."

Test Results: Progress and Limitations Side by Side

According to the Reddit user's observations, MiniMax-H3 presented a complex but encouraging picture in this round of physical cognition testing.

A Clear Lead Over Other Open-Source Models

The author explicitly noted that compared to other open-weight models, MiniMax-H3's performance represents "a big leap forward." This means the model has established a meaningful lead within the open-source camp when it comes to modeling physical common sense. For developers and researchers hoping to deploy locally or build on top of the model, this is a positive signal — open-source video generation models are closing the gap with top closed-source alternatives.

Multi-Object Overlap Remains a Hard Problem

However, the author also candidly identified the model's weakness: when multiple objects overlap in the scene, the model's performance drops noticeably, and it "still struggles a lot."

This phenomenon actually reveals a systemic challenge facing current video generation models. Multi-object overlap involves inferring occlusion relationships, sorting by depth, and simultaneously maintaining the independent motion trajectories of each object. When objects occlude one another, the model must maintain a "memory" of the hidden portions and preserve consistency — a demanding requirement for spatiotemporal modeling. Most mainstream video generation models share this weakness, and MiniMax-H3 has not fully escaped it.

Why These Experiments Matter

Though small in scale and simple in method, this series of experiments offers a genuinely valuable perspective for model evaluation.

An Evaluation Approach That Goes Beyond Benchmark Scores

Traditional model evaluations rely on standardized benchmarks and quantitative metrics, but these metrics struggle to capture something as hard to quantify as "physical intuition." By carefully designed comparative experiments — keeping the scene constant while swapping only the key object — evaluators can observe a model's reasoning abilities under controlled conditions. This approach deserves wider adoption among AI researchers.

A Bellwether for World Model Capabilities

Video generation models are increasingly being viewed as early-stage "world simulators." The degree to which they grasp physical laws has direct implications for whether they can be used in serious applications like robot training, autonomous driving simulation, and scientific modeling. As such, MiniMax-H3's progress in physical cognition carries significance far beyond entertainment video generation — it points toward the larger goal of AI that can genuinely understand and simulate the real world.

Conclusion: Open-Source Video Models Keep Pushing Forward

The author notes that they are still exploring the model in depth and plan to publish more experiments (though next time, "not with water again"). This kind of sustained, community-driven exploration is among the most valuable aspects of the open-source AI ecosystem.

The physical cognition capabilities MiniMax-H3 has demonstrated both confirm that open-source video models are rapidly approaching practical viability, and remind us that even the most advanced models still have clear limitations in complex scenarios involving multi-object interaction. True "physical understanding" remains a long road that still demands sustained effort. For practitioners following generative video technology, closely tracking these kinds of hands-on community experiments may offer a more accurate read on the real frontier of the technology than any official benchmarks or promotional materials.