TL;DR

• While video generation models are technically capable of producing high-definition videos up to 60 seconds long.
• As scaling laws confirm that quality improves in proportion to computational resource input. The ability to select superior outputs—rather than mere generation—has become a core competitive advantage.
• Users aiming to produce high-quality video should adopt a workflow that involves generating multiple candidates and selecting the optimal version free of physical errors.

Example: A scene where waves crash onto a beach and recede is depicted with precision. Instead of hoping for a lucky result from the AI, the creator focuses on the process of controlling quality by investing significant computational resources.

Current Status: 60 Seconds of Technology vs. 20 Seconds of Reality

Video generation technology has moved beyond precise depiction to a stage where it can implement consistent narratives. According to OpenAI's technical document 'Video generation models as world simulators,' Sora has the capability to generate videos up to 60 seconds long in various resolutions and aspect ratios. This result overcomes previous limitations where models could only produce short clips of a few seconds. Sora has secured flexibility by training on video data decomposed into patches, allowing aspect ratios—such as widescreen, vertical, or square—to be determined at the time of generation.

However, a gap exists between technical possibility and actually deployed services. Operational guidelines updated since December 2024 indicate that the actual permitted video length for Sora Turbo is restricted to 20 seconds at 720p resolution, depending on the model version and the subscription plan. This is analyzed as a strategic choice to manage the GPU resources and inference time required to generate 60-second videos.

Improvements are also observed in visual quality. The level of simulating background changes during camera movement or physical interactions between objects has advanced. While some materials from 2026 claim that generation times for paid plans have expanded to 90 seconds, OpenAI's official technical report figures are still based on 60 seconds, necessitating verification regarding actual implementation.

Analysis: 'Cherry Picking' is Now a Part of the Technology

Quality improvements in video AI are focusing not only on the number of model parameters but also on 'inference-time scaling laws.' It has been proven that allocating more computing resources and increasing sampling iterations during the actual generation process improves physical realism and visual consistency within the video.

These characteristics offer important implications for practitioners. To obtain high-quality AI video, a 'cherry picking' process—generating dozens of results and selecting those without defects—is more essential than the ability to write a single prompt. This signifies that AI video generation has transformed from automated creation into a process involving the investment of capital and time.

From a critical perspective, this approach is capital-intensive. This is because the structure favors companies or creators with abundant computing resources to obtain superior quality. Furthermore, since the model simulates based on the statistical probability of data rather than a complete understanding of physical laws, unrealistic errors are still likely to occur in movements involving complex causal relationships.

Practical Application: Strategies for Extracting High-Quality AI Video

Users should now control quality through a probabilistic approach. Rather than attempting to extract a long video in one go, it is more effective to connect short, high-quality clips or repeatedly generate specific segments to minimize physical errors.

Example: A creator sits in front of a screen, repeating the same explanation to generate multiple results. To choose a scene where physical movement is natural, they repeat the computation dozens of times.

Checklist for Today:

• Execute repeated generations at least five times with the same prompt to check for variance in physical consistency.
• To prevent errors that may occur during high-resolution generation (1080p or higher), verify short 20-second clips first.
• Check the maximum allowable generation time of your current subscription plan and design the project's production schedule accordingly.

FAQ

Q: What is the maximum resolution Sora can generate? A: According to OpenAI's official announcement, it supports up to 1080p resolution and can generate in various aspect ratios, including widescreen, vertical, and square.

Q: Does increasing the number of samples often improve quality? A: While increasing inference computing resources tends to improve quality and consistency, this is a probabilistic improvement. Since resource consumption may eventually grow faster than the rate of quality improvement beyond a certain threshold, it is important to set an appropriate number of repetitions.

Q: How can I create a long-form video exceeding 60 seconds? A: Exceeding 60 seconds in a single generation is currently a limitation of the official model. Therefore, it is recommended to complete a long narrative by using 'video extension' features that expand the next scene based on a specific point in a previously generated video.

Conclusion

Video generation AI is finding a balance between video length and physical consistency. While the 60-second potential presented by Sora has the power to change production grammar, it presupposes the availability of resources to handle inference scaling and a meticulous selection process.

The point to watch in the future is the efficiency of this generation process. When technology that can suggest optimal samples or pre-detect and filter out physical errors is integrated—beyond simple repeated generation—AI video will finally establish itself as a standard tool in professional production fields.

References

• 🛡️ Video generation models as world simulators
• 🛡️ Sora is here - OpenAI