Haiku vs. GPT Tera: which neural network is better at creating 3D scenes and dioramas

In this comparison, Haiku and GPT Tera are neural networks for generating interactive 3D scenes, models, and small game prototypes. According to the test described, Haiku handled visually complex tasks better, while GPT Tera was noticeably faster at producing the first result.

The choice depends on the goal. For a detailed diorama and an expressive character, the comparison's author chose Haiku. For a quick rough prototype that will later require manual refinement, GPT Tera appears more practical.

Conditions of the Haiku and GPT Tera comparison

Both models were run in X High mode and completed the same tasks under subscriptions costing 20 dollars. The comparison tested the creation of an 3D zombie, a diorama of a gloomy city with zombies, and a game in 2,5D format. The article's main conclusion concerns the first two tasks: they make it possible to compare model quality, animation, scene atmosphere, and interaction with the environment.

The test was not a standardized benchmark. The author used custom prompts, evaluated the result visually, and recorded quota usage within specific runs. A different prompt, interface version, editing order, or duration of autonomous operation could have changed the outcome.

Creating an 3D zombie

In the first test, GPT Tera finished in 2 minutes 39 seconds and used 2% of the five-hour quota. To the author, this result seemed suspiciously fast: the task required three iterations of model improvement. Fast completion alone does not confirm that all requested steps were actually carried out with the same level of thoroughness.

The Tera model created a fairly primitive zombie. The character had facial errors and improperly attached fingers, while the animation remained relatively simple. The author noted that the movement contained fewer individual problems than the model's geometry, but the visual refinement did not match the level expected from a complex 3D task.

Haiku worked for about an hour and used 6% of the five-hour quota. The author stopped the process because, in his estimate, further refinement could have taken another half hour or so. During that time, the model created a more detailed zombie with bodily injuries, a skull, blood, dirt, and a shadow. The visual design was more expressive and conveyed the horror mood better.

The author also subjectively considered Haiku's animation more interesting and frightening. This is an assessment of a specific test result, not a universal advantage of the model across all types of animation. The author considered the impression created by the movement, the character's appearance, and how cohesively these elements worked together.

Following the first 3D test, the author awarded Haiku the victory by a wide margin.

His interim assessment was that Haiku's result looked two to three times better than Tera's. This wording reflects a personal impression and is not a measurement based on an established methodology.

Diorama of a gloomy city

The diorama tested not only the models' ability to create individual objects but also their ability to assemble them into a coherent scene. GPT Tera worked for approximately 10 minutes and used 6% of the five-hour quota. Haiku continued refining the already completed project for more than an hour, using 12% of the quota.

Tera's scene featured buildings, cars, and zombies, including lying characters and a zombie rummaging through trash. However, the author did not see a convincing noir style, and some characters were less refined than the surrounding objects. The scene looked small and chaotic, so the individual elements did not come together into a cohesive image of a city after a catastrophe.

Errors also appeared in the characters' movement. Crawling zombies moved while supporting themselves only with their arms, with their legs hanging in the air. There were no collisions between the characters, so they passed through one another. Such an error is unpleasant in a demonstration prototype, while a game scene would require separate technical work on the physics or collision logic.

Haiku created a richer diorama. It featured bricks, leaves, reflections in windows, illuminated windows, wrecked cars, barriers, trash, and rain. These details did not merely increase the number of objects. They formed an environment that better matched the scenario of a gloomy city during a zombie apocalypse.

The movement of the crawling zombies in Haiku's scene could not be called perfect either. Collisions worked between the characters, so they did not pass through one another, but individual zombies could get stuck inside each other. This result appears more thoughtfully designed, but it still does not replace testing gameplay behavior in a real engine.

According to the author's assessment, Haiku's diorama clearly surpassed Tera's result in atmosphere, detail, and fidelity to the scenario. Its main advantage was the combination of environment, lighting, weather effects, damaged cars, and more convincing characters, rather than any single model.

Why GPT Tera's speed does not mean higher quality

GPT Tera completed the tasks faster, but some of that speed came with simplifications. When a model creates a scene in a few minutes, the user gets an early prototype that is convenient to review and quickly revise. This approach suits a developer who values the speed of testing an idea, basic composition, and the ability to fix the geometry, animation, and collisions independently.

Haiku spent more time on subsequent changes. The author called the prolonged work on the already completed diorama excessive overthinking: after reaching an acceptable result, the model continued making revisions. For a production task, this is not always a drawback. Additional iterations can increase detail, but they also consume more quota and make it harder to determine when the result is ready to accept.

What matters more for a team: ten minutes to the first prototype or an hour to a more expressive scene? The answer depends on the project stage. During preproduction, speed is valuable, while when preparing a demonstration diorama, vertical slice, or visual concept, environmental cohesion and the absence of obvious artifacts are more important.

Overall comparison

Test limitations

The comparison of Haiku and GPT Tera is based on several runs rather than a series of repeatable measurements. It contains no independent assessment of topology, scene performance, source code correctness, compatibility with a specific engine, or asset readiness for commercial production. The conclusions about the zombie and urban diorama cannot be applied to vehicle modeling, architecture, realistic physics, or a complex combat system.

The five-hour quota usage also cannot be considered a universal generation cost. It applies to the specific subscriptions, X High mode, and individual runs described by the author. Another project with more files, edits, or tool calls would require a different amount of resources.

Conclusion: which to choose for 3D scenes

For a visually complex 3D scene, Haiku performs better in the comparison described. It worked longer and consumed more quota, but it created a more expressive zombie and a more atmospheric diorama in which the environment supported the zombie apocalypse scenario.

GPT Tera is worth choosing when speed is the priority. It quickly produced the foundation of a scene, used less quota, and may be useful for rough composition, testing an idea, or a project that the developer plans to revise extensively by hand.

The comparison's author stated the final conclusion unambiguously: in the first tests, Haiku won on quality, while Tera won on speed. The assessment that Haiku was two to three times better applies only to the subjective impression from these runs and does not replace a full standardized benchmark.