One place for supported models
Build AI integrations with SeeAPI’s model catalog and public APIs. Check each model’s supported capabilities, inputs, outputs and limits for your application.
Build AI integrations with SeeAPI’s models and APIs. Copy one prompt to guide your coding assistant through a new project or migrate supported workflows from an existing platform.
Copy the prompt to inspect your project and map the changes.
Generate a key and add it to your environment.
Find models for your AI integrations and explore their official API guides.
Build AI integrations using official model guides and your coding assistant. Explore supported capabilities in one place, then connect a new application or adapt an existing workflow.
Build AI integrations with SeeAPI’s model catalog and public APIs. Check each model’s supported capabilities, inputs, outputs and limits for your application.
Use Cursor, Claude Code or Codex to inspect your current calls and identify the integration code that needs changing, while preserving unrelated features.
Compare model IDs, parameters, results and pricing against official documentation. Review unclear mappings before replacing a working integration.
Before starting an AI migration, compare the exact models, parameters, results and billing units your application relies on. Use the platform examples below to identify what needs to change.
Match the exact model or version and prediction inputs to a documented SeeAPI model and Endpoint. Review output formats, task states and any custom-model dependencies.
Confirm support for each operation your application needs. Prefer the same documented model and version; a similar name is not proof of an equivalent result.
Check required inputs, aspect ratios, resolution, formats and result URLs. An accepted parameter may still have a different default or limit on another route.
Review authentication, idempotency, polling, terminal states and the documented billing unit. Migrating your API entry point does not necessarily change the underlying Provider.
Trace the model path and SDK or queue calls your project uses. Map request fields, status handling and result parsing to the chosen SeeAPI operation instead of copying the old request unchanged.
Identify the model-specific API path and parameters. Compare submission, result retrieval and asset requirements with the SeeAPI guide, then update the full request lifecycle.
Build AI integrations for a new application or migrate existing workflows. Choose one capability, follow its official guide and validate before launch or switching traffic.
Copy the prompt into your coding assistant. For a new app, describe your goal and preferred framework. For an existing project, ask it to identify current models, versions and API calls.
For a new app, choose a documented model and capability. For a migration, map each existing operation and keep unsupported ones unchanged. Check required inputs, defaults and limits.
Configure a server-side key, then implement or update authentication, request parameters and result handling. Follow the documented task flow and keep keys out of the AI chat.
Run local checks first. After you authorize a live test, verify results, task states and costs. Launch a new integration when ready, or compare with the existing workflow before switching traffic.
SeeAPI may fit if the AI model and capability you need are available in its published API documentation. Compare inputs, outputs, limits and pricing for your actual workload. Do not assume a custom model, fine-tune or pinned version can be moved without a documented equivalent.
Open your existing project in Cursor, Claude Code, Codex or another coding assistant that can read your code and online documentation. Copy the prompt above into its chat. Ask it to identify your current models, map them to SeeAPI’s official guides, update the integration and run local checks. Review the changes before any live test or deployment.
Check the exact model, version and operation in SeeAPI’s official model guide. Keep a documented equivalent where available. If a mapping is unsupported or unclear, retain the existing integration until you choose an alternative. The API platform and the underlying Provider are separate choices.
The prompt starts an AI-assisted workflow; this page does not edit your code. Your assistant still needs to inspect the project, resolve model differences and validate its changes. It should preserve unrelated features and report anything it cannot verify.
Compare current API prices for the same model, Provider, parameters and output quantity. Billing units and supported settings can differ. This page does not promise universal savings; check API Pricing and validate the cost of your own workload before switching.
Start with type checks and local tests using mocked API responses. Verify request fields, task-state handling, result parsing and duplicate-submission protection. These checks do not prove live model access, output quality, speed or billing. Run a billable test only when you are ready and have authorized it.
Update result handling alongside request submission. Follow the selected operation’s documented status flow and use callbacks only where documented. Keep the task ID and check its status before submitting again; a timeout does not prove failure or cancel the task.
Create a key from the API key page and configure it on your application server, for example in SEEAPI_API_KEY. Never paste a real key into the AI chat or expose it in browser code, logs or a repository. Saving a key does not verify model access.
Copying only copies text and does not submit a generation. Your coding tool may have its own fees, and real SeeAPI requests can consume credits. For a new app, tell the assistant your requirements and preferred framework; the prompt also includes a new-project path.
Ready to build or migrate your AI integrations? Find your model in the official guides and use the same integration prompt to get started.