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GlobalAI OPC gives you access to a curated set of best-in-class AI models across three categories: video generation, image generation, and text & chat. All models are available through a single unified API gateway using standard Bearer token authentication, so you can switch between providers without changing your integration structure.

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Video Generation Models

The table below summarizes every video generation model available through GlobalAI OPC, along with supported resolutions, durations, and key capabilities.

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Image Generation Models

GlobalAI OPC supports two image generation model families, both capable of high-resolution output from text prompts and optional reference images.

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Text & Chat Models

Text and chat APIs use a synchronous request/response pattern — no polling required. GlobalAI OPC exposes three API protocols to maximize compatibility with existing SDKs and tools.

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Choosing the Right Model

Use videos_fast, viduq3-turbo, or seedance_2_0_fast for the lowest latency. These fast-tier variants sacrifice some output quality in exchange for significantly shorter generation times — ideal for prototyping or high-throughput pipelines.
Use sd2_manxue_4k for 4K output, veo_3_1_pro for Google’s highest-quality tier, or seedance_2_0_pro for ByteDance’s professional tier. These models produce the best results but take longer to generate.
All of the following model families support image-to-video (providing one or more reference images to guide the generated video): Sora, VEO, Seedance 1.5 & 2.0, Vidu, and Grok. Grok supports up to 7 reference images in a single request. For face-based or sensitive imagery, use Seedance 2.0 or SD2 Manxue with the asset management workflow.
Use Grok (grok_video3, up to 30 seconds) for the longest possible output. Vidu supports up to 16 seconds with flexible per-second duration control. Most other models cap out at 12–15 seconds.
Point your SDK’s base_url at the GlobalAI OPC endpoint and use the OpenAI Chat API protocol. This gives you access to GPT, Claude, and Gemini models all through the familiar openai.chat.completions.create() interface without changing your existing code structure.