A unified batch API for AI providers. Submit thousands of LLM requests at roughly half the cost with a single call — batchwork handles JSONL, file uploads, inline submission, polling, and result parsing across every major provider.
📖 Full documentation: batchwork.dev
npm install batchwork
# plus the provider package(s) you use:
npm install @ai-sdk/openai @ai-sdk/anthropicbatchwork depends only on ai. The @ai-sdk/* provider packages are optional peer dependencies — install only the ones you batch with. Requires Node.js 20 or newer.
import { batch } from "batchwork";
import { openai } from "@ai-sdk/openai";
const job = await batch({
model: openai.chat("gpt-5.5"),
requests: [
{ customId: "a", prompt: "Summarize: …" },
{ customId: "b", messages: [{ role: "user", content: "Translate: …" }] },
],
});
const results = await job.wait().then(() => job.collect());
for (const r of results) {
console.log(r.customId, r.status, r.text);
}Author requests in the same generateText shape you already use, pass the AI SDK models you already use, and get back one normalized result type correlated by customId.
Batch embeddings work the same way — pass a text embedding model and values, and get one vector per request back on result.embedding:
import { batch } from "batchwork";
import { openai } from "@ai-sdk/openai";
const job = await batch.embeddings({
model: openai.embeddingModel("text-embedding-3-small"),
requests: [
{ customId: "a", value: "The quick brown fox." },
{ customId: "b", value: "A lazy dog sleeps." },
],
});
const results = await job.wait().then(() => job.collect());
for (const r of results) {
console.log(r.customId, r.embedding?.length);
}Batch embeddings are available for OpenAI, Mistral, and Google Gemini — the providers whose batch API accepts embeddings. The rest throw a clear error: Anthropic, Groq, and xAI have no embedding model, and Together AI's batch API doesn't accept the embeddings endpoint.
Generate images in bulk — pass an image model and prompts, and get base64 images back on result.images:
import { batch } from "batchwork";
import { openai } from "@ai-sdk/openai";
const job = await batch.images({
model: openai.image("gpt-image-2"),
requests: [
{ customId: "a", prompt: "A red bicycle against a brick wall." },
{ customId: "b", prompt: "A watercolor painting of a sleeping cat." },
],
});
const results = await job.wait().then(() => job.collect());
for (const r of results) {
for (const image of r.images ?? []) {
// Inline base64 (`image.data` + `image.mediaType`), or a hosted
// `image.url` for providers that return one (e.g. xAI batch).
console.log(r.customId, image.mediaType ?? image.url);
}
}Batch image generation is available for OpenAI (/v1/images/generations, e.g. gpt-image-2), Google Gemini image models (e.g. gemini-3.1-flash-image), and xAI (/v1/images/generations, e.g. grok-imagine-image-quality); other providers throw a clear error. Google's Imagen models aren't batch-supported, and Together AI's batch API is chat/audio only. OpenAI and Google return inline base64 on image.data; xAI batch returns signed image.urls that expire ~1h after completion, so download them promptly.
Image editing works too, on OpenAI and xAI, via batch.images.edit() (batch.images.create() is an alias of batch.images()). Source images are passed as JSON references — uploaded fileIds (OpenAI) or hosted imageUrls — with an optional mask on OpenAI:
const job = await batch.images.edit({
model: openai.image("gpt-image-2"),
requests: [
{
customId: "a",
prompt: "Make the bicycle blue.",
images: [{ imageUrl: "https://example.com/bicycle.png" }],
},
],
});Generate videos in bulk — pass a video model and prompts, and get signed video URLs back on result.videos:
import { batch } from "batchwork";
import { xai } from "@ai-sdk/xai";
const job = await batch.videos({
model: xai.video("grok-imagine-video"),
requests: [
{ customId: "a", prompt: "A red bicycle rolling downhill.", duration: 5 },
],
});
const results = await job.wait().then(() => job.collect());
for (const r of results) {
console.log(r.customId, r.videos?.[0]?.url);
}Batch video generation is available for xAI (Grok Imagine via /v1/videos/generations, plus editing and extension through providerOptions.xai); other providers throw a clear error — OpenAI's Videos API (Sora) is deprecated (shutting down September 2026) and Google's Veo models aren't batch-supported. Results are signed URLs that expire ~1h after completion, so download them promptly.
Transcribe audio in bulk — pass a transcription model and hosted audio URLs, and get transcripts back on result.text:
import { batch } from "batchwork";
import { groq } from "@ai-sdk/groq";
const job = await batch.transcriptions({
model: groq.transcription("whisper-large-v3"),
requests: [
{ customId: "a", audioUrl: "https://example.com/interview.wav" },
{ customId: "b", audioUrl: "https://example.com/standup.mp3" },
],
});
const results = await job.wait().then(() => job.collect());
for (const r of results) {
console.log(r.customId, r.text);
}Batch transcription is available for Groq (whisper-large-v3, audio by url), Mistral (Voxtral models, e.g. "mistral/voxtral-mini-latest", audio by file_url), and Together AI (Whisper models, e.g. "together/openai/whisper-large-v3", audio by file); other providers throw a clear error — OpenAI's batch API doesn't accept its audio endpoints. Batch audio endpoints take hosted URLs only (no file uploads), so each audioUrl must stay reachable while the batch processes. Request timestampGranularities: ["segment"] to also get timestamped spans on result.segments.
batch.translations() runs Whisper's translate task instead — audio in any language, English text out — on Groq (whisper-large-v3 only) and Together AI, with the same request shape minus language.
Moderate content in bulk — pass a moderation model and texts (or image URLs, OpenAI omni moderation only), and get verdicts back on result.moderation:
import { batch } from "batchwork";
const job = await batch.moderations({
model: "openai/omni-moderation-latest",
requests: [
{ customId: "a", value: "What a lovely day for a picnic." },
{ customId: "b", value: "…user-generated content…" },
],
});
const results = await job.wait().then(() => job.collect());
for (const r of results) {
console.log(r.customId, r.moderation?.flagged, r.moderation?.categories);
}Batch moderation is available for OpenAI (omni-moderation-latest, text + images) and Mistral (mistral-moderation-latest, text-only); other providers throw a clear error. Models are passed as "provider/model" strings (the AI SDK has no moderation model type). Category names are provider-native; flagged is the provider's own flag (OpenAI) or "any category flagged" (Mistral).
- One API, many providers — OpenAI, Anthropic, Google Gemini, Groq, Mistral, Together AI, and xAI.
- AI SDK native — author requests in the familiar
generateTextshape. - Chat, embeddings, images, video, audio & moderation —
batch()for completions,batch.embeddings()for vectors,batch.images()for image generation,batch.videos()for video generation,batch.transcriptions()for audio transcription,batch.moderations()for content moderation. - ~50% cheaper — every request runs against the provider's batch window.
- Normalized results — unified status, text, usage, and error types regardless of provider.
- Server-ready — optional layers for managed polling, unified webhooks, and Next.js route handlers.
- Durable stores — drop-in Postgres (
batchwork/postgres) and Upstash Redis (batchwork/redis) adapters for the poller, or bring your own.
Guides for models, the job handle, rehydration, the server layer, and Next.js handlers all live at batchwork.dev.