Best AI API for Developers: Image & Video Generation

The question developers ask when picking a generation API is "which model is best right now".

The question that actually decides the outcome is "what happens in four months when it isn't".

Because it won't be. Image and video models have turned over every few months for three years straight. If your integration is welded to one vendor's endpoint, every turnover is a migration: new auth, new payload shape, new polling contract, new billing to reconcile. Pick well and you buy four good months. Pick for swappability and you stop having the conversation.

Here is what to actually evaluate, and what the Flixly API does about each part.

Evaluate the integration, not the leaderboard

Five things determine what this costs you over a year. None of them is model quality.

How many models one integration reaches. If switching models means a new SDK, you don't have model choice. You have model lock-in with extra steps.

Whether it tells you or you ask it. Polling a job every two seconds burns your compute to learn something the server already knew. Webhooks invert that.

What a runaway loop costs. Every generation API is one bad while away from a serious bill. Ask what stops it before you ask about latency.

Whether failures are typed. "Something went wrong" forces you to string-match error text. Typed errors let you branch.

Whether the docs are generated or written. A hand-written endpoint list drifts from reality. An OpenAPI document generated from the running service cannot.

One key, 111 models

The Flixly API exposes 111 models through a single authenticated surface — image, video and audio — and switching between them is a string in the request body.

There are five endpoints, and that is the entire API:

Auth is HTTP Bearer. Create a key in API keys and send it as Authorization: Bearer <key>.

That /models endpoint matters more than it looks. Because it's live rather than a documentation page, you can enumerate what exists at runtime and let config choose a model instead of hardcoding one. New models appear there without you shipping anything.

The OpenAI-compatible endpoint

/api/v1/chat/completions speaks the OpenAI Chat Completions format.

If you already have code built on an OpenAI client, you change the base URL and the API key. That's the integration.

This is the cheapest possible migration path, and it's worth knowing about before you write an adapter layer you don't need.

Webhooks, so you stop polling

POST /api/v1/generate accepts an optional webhook_url. Supply one and the result is delivered when the job finishes.

The URL must be a public HTTPS address and is validated before anything is queued — a request pointing somewhere unsafe is rejected at submission with a 400 rather than quietly failing later.

If you'd rather pull, GET /api/v1/generations/{id} still works and is the right choice for scripts and one-off jobs. For anything running continuously, webhooks are less code and less spend. The details are in the webhooks documentation.

The feature that saves you from yourself

Every API key carries scopes and a monthly spending cap.

A key can be limited to what it's allowed to do, and to how much it may spend in a month. Reach the cap and the key stops. There are also spending alerts before you get there.

This is the control most generation APIs don't give you, and it's the one that matters at 3am when a retry loop starts calling generate on a timer. Give each project its own key with its own cap. The blast radius of any mistake becomes one number you chose in advance.

Same pipeline as the product

Worth understanding because it determines how stale the API gets.

/api/v1/generate is an adapter, not a second implementation. It handles what's specific to being a public API — key auth, rate limits, scopes, spending caps, usage logging, a stable response contract — then hands off to the exact code path the dashboard and mobile apps run on.

It didn't always work this way. The route used to carry its own copy of the provider dispatch, forked from an older pipeline. It drifted, the way forks do, and by the time anyone checked it had missed four separate rounds of improvements the main path had received.

Which is the general lesson: when an API is a fork of the product's internals, you get whatever the internals looked like on fork day. When it's an adapter over the same code, a fix in the product is a fix in your integration. Ask any vendor which one they are.

Getting the contract without reading prose

Two artifacts do this better than any guide:

The OpenAPI document is generated from the running service. Point your generator at it and get a typed client in your language, with the real shapes rather than transcribed ones.

The Postman collection gives you working requests to fire immediately, which is usually faster than writing a first script.

Both beat copying snippets out of an article, including this one. The developer docs tie them together, and SDKs covers language-specific setup.

A first integration in four steps

1. Create a scoped key in API keys. Set a monthly cap now, not later.
2. GET /api/v1/models and look at what's actually available rather than what an article claims.
3. POST /api/v1/generate with your chosen model and a webhook_url if you have somewhere to receive it. You get a job back.
4. Collect the result from your webhook, or poll GET /api/v1/generations/{id}.

Check GET /api/v1/account for your balance when you want it. Credit costs are on the pricing page, and the model catalog is at Models.

Also worth knowing

There's an MCP server, so agents can call generation as a tool without you writing a wrapper. That's covered under MCP.

Deeper walkthroughs live in the image generation guide and the video API guide. For high-volume patterns see batch image generation, and for conversational work, building chatbots on the API.

What to actually test before committing

Skip the benchmark tables. Run these three:

Swap a model with a one-line change. If it takes more, you learned the real answer about lock-in.

Kill your listener mid-job. Find out what a dropped webhook does before production finds out for you.

Set a spending cap deliberately low and hit it. Watch how the failure presents itself. That's the behaviour you'll depend on when something goes wrong for real.

Nothing on a comparison page tells you as much as ten minutes of those three.