Veo 4.0 Video Generation Real-World Test: Two Videos Consumed 86% of Compute Quota

Gemini 3.5 and Veo 4.0: Google AI's Surprises and Controversies

Google has been making aggressive moves in the AI space recently. The sudden launch of the Gemini 3.5 model impressed many users, and its companion Veo 4.0 video generation capability has sparked widespread discussion. However, what caught Pro subscribers off guard is that generating just two short videos consumed a staggering 86% of their compute quota — a pricing strategy that quickly ignited heated debate within the community.

Veo 4.0 Video Generation: Real-World Results

The Veo series is a video generation model developed by Google DeepMind based on the Diffusion Transformer architecture. Unlike earlier video generation models built on U-Net architectures, the Diffusion Transformer introduces Transformer attention mechanisms into the diffusion process, enabling the model to better capture temporal dependencies between video frames and long-range semantic consistency. Veo 4.0 further strengthens physical world modeling capabilities over its predecessors, including implicit understanding of fluid dynamics, light propagation, and human kinematics — which is the fundamental reason behind its impressive performance in details like skin texture and ocean wave dynamics.

From actual generation results, Veo 4.0's video quality has indeed reached an impressive level. In a test video themed around a tropical beach, the model demonstrated excellent understanding and rendering of complex scenes.

Character movements in the video are smooth and natural, with realistic lighting effects. Veo 4.0 shows capabilities far exceeding its predecessors, particularly in handling skin texture, sunlight reflections, and other fine details. In the scene of a person walking on the beach, sand texture, wave dynamics, and light variations are all handled remarkably well.

The transition from daytime to nighttime scenes is equally impressive. The dancing-in-the-evening-wind sequence demonstrates exceptional production quality in lighting transitions and atmosphere creation, nearly approaching professional music video-level visual effects.

Scene Diversity and Dynamic Expression Analysis

Veo 4.0's performance in scene diversity also deserves attention. The test videos covered multiple environments including beaches, urban settings, and tropical vibes, with the model maintaining consistent output quality across different scenarios.

Dynamic expression in urban scenes is particularly outstanding — the character's energy and sense of freedom are effectively conveyed. The coordination between background architecture, street atmosphere, and character movement indicates that Veo 4.0 has made significant progress in understanding the emotional tone of text descriptions.

Veo 4.0's breakthrough in complex human motion generation is closely tied to its deep integration of human pose prior knowledge. Modern video generation models typically incorporate human skeletal keypoint detection data during pretraining, enabling the model to implicitly follow human kinematic constraints during generation, thereby avoiding common artifacts like joint distortion and limb interpenetration. Complex movements like waist twisting have historically been difficult for video generation because they involve multi-joint coordinated motion coupled with cloth dynamics simulation. Through larger-scale high-quality dance and motion video training data, combined with more refined temporal attention mechanisms, Veo 4.0's handling of complex human movements like waist twisting in dance sequences is notably superior to previous video generation models, with qualitative improvements in both motion coherence and physical plausibility.

Two Videos Consuming 86% of Quota: The Compute Dilemma for Pro Users

Behind the stunning results lies jaw-dropping compute consumption. To understand this, one must first grasp the computational cost of video generation: generating an 8-second video at 24 frames per second means the model needs to maintain semantic consistency across 192 frames simultaneously on the time axis, with computational demands roughly tens of times that of a single image at the same resolution. Additionally, video generation typically requires tens to hundreds of denoising iterations in Latent Space, with each step requiring a complete forward propagation computation. Using an A100 GPU as a benchmark, the compute consumption for generating a single high-quality short video is approximately equivalent to generating thousands of text tokens.

According to user feedback, under the Gemini Pro subscription plan, generating just two Veo 4.0 videos consumed approximately 86% of the daily compute quota. In other words, Pro users can only generate about two to three short videos per day before needing to wait for quota refresh or pay extra.

This pricing strategy has raised questions on multiple fronts:

• Questionable value for money: The monthly subscription fee Pro users pay yields an extremely limited number of video generations
• Creative workflow disruption: For creators who need to iterate and adjust repeatedly, such high per-generation consumption is nearly impossible to sustain for daily work
• Clear gap with competitors: In the AI video generation market, platforms like Runway, Pika, and Kling generally adopt Credits-based billing systems, where users can flexibly purchase based on actual needs rather than being tied to fixed quotas within subscription packages. For example, Runway Gen-3's subscription plans offer tiered options ranging from 125 to 2,250 credits per month, with each credit corresponding to approximately 1 second of video generation, giving users more intuitive perception and control over costs. In contrast, Google bundles Veo 4.0 into Gemini Pro's general compute pool without dedicated quota explanations for video generation, making it difficult for users to predict consumption before use. This lack of transparency is itself a significant factor driving community dissatisfaction.

High Quality vs. High Cost: The Core Contradiction of AI Video Generation

Google's strategy here reflects a core contradiction in the current AI video generation field — the balancing act between high-quality output and affordable cost. Veo 4.0's technical prowess is undeniable, but if its pricing strategy puts off most users, its market competitiveness will be significantly undermined.

From a technical perspective, the inference cost of video generation models is indeed far higher than text and image generation — this is determined by model complexity and computational requirements. However, as a cloud computing giant with proprietary TPU (Tensor Processing Unit) infrastructure, Google theoretically has the capability to offer video generation services at lower marginal costs. Looking at historical precedent, Google dynamically adjusted free quotas multiple times during Gemini's early rollout phase. This strategy is essentially about finding the optimal balance between user growth and revenue monetization. Setting a high per-generation consumption threshold for Pro users at this stage may be aimed at guiding high-frequency users toward higher-priced enterprise-tier plans, rather than simply passing through technical costs — a business strategy question worth deeper consideration.

For regular users, the practical advice at this stage is: plan daily compute usage carefully, reserve Veo 4.0 for final output generation, use lower-consumption alternatives during creative ideation and initial testing phases, and avoid wasting precious video generation quota on trial-and-error iterations.

Key Takeaways

• Google's Veo 4.0, based on the Diffusion Transformer architecture, achieves near-professional-grade video generation quality with outstanding performance in character motion, lighting effects, and scene diversity
• Pro users consuming 86% of their compute quota on just two videos has triggered strong community backlash against the pricing strategy
• Veo 4.0 shows qualitative improvements over predecessors in complex human motion and scene transitions, thanks to implicit modeling of human kinematic constraints
• The high inference cost of video generation (roughly tens of times that of equivalent image generation) is the underlying technical reason for aggressive quota consumption
• Compared to competitors like Runway with their credits-based system, Google's insufficient quota transparency may impact its competitiveness in video generation