Sora 2 Review: OpenAI's Next-Generation AI Video Model Tested
Introduction to Sora 2
OpenAI officially unveiled Sora 2, its next-generation AI video and audio generation model, on September 30, 2025—marking a major leap forward in text-to-video technology and storytelling capabilities. Just a few weeks later, on October 16, the company introduced two major updates: a new "Storyboard" tool for Pro users on the web, and expanded video generation limits for all users. With this rollout, anyone can now create videos up to 15 seconds long on the app or web, while Pro users gain the ability to generate videos up to 25 seconds directly from the web interface.
Compared to the original Sora, launched in early 2024, Sora 2 represents a significant breakthrough — it can natively generate synchronized video and audio in one seamless process. This advancement moves beyond visual physics simulation, allowing creators to build immersive, multi-sensory worlds where sound and motion evolve together.
Model Comparison: Sora 2 vs VEO 3.1 vs Kling 2.5 Turbo
We evaluated three leading AI video generation models—OpenAI's Sora 2, Google's Veo 3.1, and Kling 2.5 Turbo—across three key dimensions:
- Physical realism & physics compliance
When comparing the three models in terms of physical realism, Sora 2 delivered the most accurate and convincing physics, with natural object motion and well-grounded interactions. However, its paired audio did not align well with the visuals, using a narration-style soundtrack rather than realistic impact or collision sounds. Veo 3.1 followed closely, automatically generating scene-adapted sound effects, though its audio-video synchronization was imperfect and the bounce sounds were not always precise. Kling 2.5 Turbo lagged slightly behind in material collisions and bounce realism. Notably, Sora 2 also exhibited slightly lower visual clarity compared to the other two models.
- Temporal consistency & visual fidelity
In terms of consistency and realism, Sora 2 demonstrated the strongest performance. It maintained remarkable consistency in the dog's visual details and delivered highly coherent motion and lighting effects across the scene. Kling 2.5 Turbo also handled the dog's appearance fairly well, but its motion logic and the way certain objects—such as the bone—appeared were less convincing. Veo 3.1, on the other hand, struggled to maintain visual consistency, preserving only basic traits like the golden retriever's color and orange collar. Still, its motion dynamics and environmental realism were commendable, and the auto-generated audio was better aligned with the visuals compared to Sora 2.
- Aesthetics & creative direction
Each model brings its own take on the same prompt. Sora 2 delivers the most polished result, with beautiful lighting, smooth camera work, and a clear narrative feel. Veo 3.1 goes for a grander, sci-fi cinematic vibe, while Kling 2.5 Turbo trails slightly in texture and lighting. The absence of audio generation also makes Kling's output feel less creative and immersive.
How to Use Sora 2 for Free
Artflo currently supports both Sora 2 and Sora 2 Pro.
To create a video with Sora 2 in Artflo, start by adding an Input Node in the editor. Enter your text prompt and upload a reference image if needed. Then, create a Video Node and select your preferred video generation model.
Extended Reading: The Evolution of AI Video Generation Models
The journey of text-to-video technology began with equal parts promise and limitation. In February 2023, Runway introduced Gen-1, the world's first text-to-video product. While revolutionary in concept, its results were far from production-ready—often lacking detail, coherence, and realism. During this early stage, AI video generation was limited to narrow use cases: simple character motions, basic style transfers, and basic human animations like lip-syncing or facial expressions.
A major leap forward came in February 2024, when OpenAI published its research paper Video generation models as world simulators and released a series of Sora demo videos. The event captivated global audiences and reignited debates over the blurry boundary between the virtual and the real. However, Sora itself was not publicly released. Two major barriers—extreme GPU requirements and a high error rate that led to visual inconsistencies—kept it confined to internal demonstrations.
The real paradigm shift arrived in June 2024, driven by a wave of breakthroughs from multiple research teams worldwide. Around this time, Luma introduced a Sora-like video model, while Kling released a similar system—both built on the Diffusion Transformer (DiT) architecture. This framework proved to be a game-changer, dramatically improving three pillars of AI video generation:
- Motion dynamics — enabling more fluid and dynamic scenes
- Physical realism — reducing violations of real-world physics
- Human expressiveness — enhancing the subtlety of digital facial emotions
This advancement ignited an industry-wide surge. Companies such as Kling, Luma, Runway, Pixverse, Minimax (Hailuo), ByteDance (Seedance), and Vidu joined the race to perfect next-gen video generation models. Their competition centered around key benchmarks: physical law compliance, motion smoothness, visual fidelity, text-to-video accuracy, long-clip consistency, and user controllability.
By May 2025, Google pushed the field even further with Veo3. Unlike earlier models, Veo3 integrated a full post-production pipeline—automatically generating special effects, lip-sync, voiceovers, background music, and sound effects in a single pass. That same year, Veo3 and OpenAI's Sora 2 emerged as the two defining AI video generation models, each showcasing a new frontier: reasoning ability. Veo3 introduced the concept of "Chain of Frames (CoF)", enhancing temporal consistency and logical flow across longer videos.
As these models grow increasingly sophisticated, the next challenge is no longer how realistic they look, but how flexibly they can be used. The most promising path forward lies in combining three forces: workflow automation, AI agents, and advanced base models. Together, they enable users to customize end-to-end video workflows, offload repetitive creative tasks (like prompt optimization or editing), and generate scalable, high-quality content—unlocking the true potential of AI-driven storytelling across industries.