Nano Banana is available on Pixapi — one endpoint, usage-based credits, ready for production.
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AI model catalog

Leading visual AI models through one API

Browse production-ready image and video models with unified auth, consistent request patterns, and developer-first pricing.

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AI model catalog guide

Use the AI model catalog to choose the right visual route

The Pixapi AI model catalog helps teams compare image and video routes before they write production code.

The AI model catalog groups image and video models by capability so builders can start from product intent.
The AI model catalog links model discovery to pricing, endpoint examples, and production documentation.
The AI model catalog keeps provider names, model ids, tags, and starting credit costs visible in one place.
The AI model catalog helps a team switch models without losing the surrounding Pixapi account workflow.

A practical AI model catalog should help a developer choose a model, not simply display a long list. Pixapi uses the AI model catalog to organize image generation, image editing, and video generation routes by provider, capability, and starting credit cost. When a team opens the AI model catalog, it can compare what each route is best for before creating an API key or writing backend code.

The AI model catalog matters because visual AI workflows often start broad. A product team may know it needs image generation, but not whether Gemini, GPT Image, or Nano Banana is the first route to test. Another team may need video generation, but still has to decide between Veo, Wan, and other routes. The AI model catalog narrows that research into a structured product decision.

Pixapi keeps the AI model catalog connected to model detail pages, pricing rules, and docs. That means the AI model catalog is not a disconnected gallery; it is the entry point into a working integration. A visitor can filter the AI model catalog, open a model page, read the endpoint pattern, compare credit cost, and then create an API key without switching to another vendor workflow.

For engineering teams, the AI model catalog reduces integration risk. Model ids, provider names, and capabilities appear together, so the AI model catalog becomes a reference for backend configuration and product documentation. When a model changes or a new route is added, the AI model catalog gives teams a single place to confirm naming, category, cost hints, and related alternatives.

For product teams, the AI model catalog makes tradeoffs visible. A faster route may be better for previews, while a higher-quality route may be better for campaign images or user-paid outputs. A video route may require async task design. The AI model catalog helps teams turn those differences into launch rules, pricing rules, and UX decisions before customers start generating assets.

Use the Pixapi AI model catalog whenever you need to compare visual AI models under one account. The AI model catalog is designed for discovery, but it also supports execution: it points to API docs, pricing, model pages, and related routes. A focused AI model catalog lets teams move from research to production with fewer provider-specific surprises.