{"id":80515,"date":"2026-04-01T14:48:23","date_gmt":"2026-04-01T14:48:23","guid":{"rendered":"https:\/\/youzum.net\/google-ai-releases-veo-3-1-lite-giving-developers-low-cost-high-speed-video-generation-via-the-gemini-api\/"},"modified":"2026-04-01T14:48:23","modified_gmt":"2026-04-01T14:48:23","slug":"google-ai-releases-veo-3-1-lite-giving-developers-low-cost-high-speed-video-generation-via-the-gemini-api","status":"publish","type":"post","link":"https:\/\/youzum.net\/ja\/google-ai-releases-veo-3-1-lite-giving-developers-low-cost-high-speed-video-generation-via-the-gemini-api\/","title":{"rendered":"Google AI Releases Veo 3.1 Lite: Giving Developers Low Cost High Speed Video Generation via The Gemini API"},"content":{"rendered":"

Google has announced the release of Veo 3.1 Lite<\/strong>, a new model tier within its generative video portfolio designed to address the primary bottleneck for production-scale deployments: pricing. While the generative video space has seen rapid progress in visual fidelity, the cost per second of generated content has remained high, often prohibitive for developers building high-volume applications.<\/p>\n

Veo 3.1 Lite is now available via the Gemini API<\/strong> and Google AI Studio<\/strong> for users in the paid tier. By offering the same generation speed as the existing Veo 3.1 Fast model at approximately half the cost, Google is positioning this model as the standard for developers focused on programmatic video generation and iterative prototyping.<\/p>\n

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https:\/\/blog.google\/innovation-and-ai\/technology\/ai\/veo-3-1-lite\/<\/figcaption><\/figure>\n<\/div>\n

Technical Architecture: The Diffusion Transformer (DiT)<\/strong><\/h3>\n

The most significant aspect of the Veo 3.1 family is its underlying Diffusion Transformer (DiT)<\/strong> architecture. Traditional generative video models often relied on U-Net-based diffusion, which can struggle with high-dimensional data and long-range temporal dependencies.<\/p>\n

Veo 3.1 Lite utilizes a transformer-based backbone that operates on spatio-temporal patches<\/strong>. In this architecture, video frames are not processed as static 2D images but as a continuous sequence of tokens in a latent space. By applying self-attention across these patches, the model maintains better temporal consistency<\/strong>. This ensures that objects, lighting, and textures remain coherent across the duration of the clip, reducing the artifacts commonly seen in earlier models.<\/p>\n

The model performs its computation in a compressed latent space<\/strong> rather than pixel space. This allows the model to handle the high computational demands of video generation while maintaining a lower memory footprint. For developers, this translates to a model that can generate high-definition content without the exponential increase in compute time that usually accompanies resolution scaling.<\/p>\n

Performance and Output Specifications<\/strong><\/h3>\n

Veo 3.1 Lite provides specific parameters for resolution and duration, allowing AI devs to integrate it into structured workflows. Unlike the flagship Veo 3.1 model, which supports 4K resolution, the Lite version is optimized for high-definition (HD) outputs.<\/p>\n