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Mynth is already task-based, so batch generation does not require a special bulk API for many common workflows. If you want several independent generations, you can call generate() or POST /image/generate in parallel.
Mynth does not impose API concurrency limits. If the generations are independent, you can parallelize them and handle task coordination in your own application.

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Generate across multiple models

import Mynth from "@mynthio/sdk";

const mynth = new Mynth();

const models = [
  "google/gemini-3.1-flash-image",
  "black-forest-labs/flux.2-dev",
  "black-forest-labs/flux.2-pro",
  "recraft/recraft-v4",
];

const tasks = await Promise.all(
  models.map((model) =>
    mynth.generate(
      {
        prompt: "Hero illustration for a fintech landing page",
        model,
        size: "landscape",
      },
      { mode: "async" },
    ),
  ),
);

console.log(tasks.map((task) => task.id));

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Generate many prompts in parallel

const prompts = [
  "Packaging photo for a botanical tea brand",
  "Packaging photo for a specialty coffee brand",
  "Packaging photo for a skincare brand",
];

const tasks = await Promise.all(
  prompts.map((prompt) =>
    mynth.generate(
      {
        prompt,
        model: "google/gemini-3.1-flash-image",
      },
      { mode: "async" },
    ),
  ),
);

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Why this works well

Each request creates its own task, and Mynth processes those tasks asynchronously. That means you can:
  • fan out across models
  • fan out across prompts
  • store task IDs immediately
  • poll or handle results with webhooks later

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Concurrency limits

Mynth does not impose API concurrency limits. However, you should size your parallelism based on:
  • Budget: each request incurs cost. Multiply your per-request cost by the number of concurrent tasks.
  • Downstream capacity: if your system processes results (e.g. saving to a database, triggering a pipeline), make sure it can keep up.
  • User experience: too many simultaneous requests from a single client can cause long wait times for all of them.