factorial-compute

One API for AI compute that returns typed answers — text, images, masks, detections, depth, documents, transcripts, speech, video, 3D models, tracks — in the same shape whichever model or supplier produced it.

pip install factorial-compute
export FACTORIAL_API_KEY=fc_...

from factorial_compute import Client

f = Client()  # reads FACTORIAL_API_KEY

# Text, and JSON on request.
print(f.run(model="gpt-oss-120b", input="Name three uses for a forklift.").text)

# An image from a prompt.
result = f.run(model="flux-schnell", input={"prompt": "a red forklift in a warehouse"})
result.images[0].save("forklift.jpg")

# Masks: one per object, each with a box measured from the mask.
result = f.run(model="sam2-segment", input={
    "image": "warehouse.jpg",                      # a local path: uploaded for you
    "objects": [{"id": "pallet", "box": [40, 60, 300, 280]},
                {"id": "cone", "point": [512, 240]}],
})
for mask in result.masks:
    print(mask.id, mask.box)
    mask.save(f"{mask.id}.png")

# A transcript, with timestamps.
result = f.run(model="whisper", input={"audio": "meeting.mp3"})
print(result.transcript.text)
for segment in result.transcript.segments:
    print(f"{segment.start:6.1f}s  {segment.text}")

That is the whole surface for most work: run() with a model id and an input, then read the typed answer.

Rules worth knowing

• Files go in by path, URL or bytes under the keys image, images, audio and video - for vision chat models too. The client uploads them and sends a reference.
• Answers are typed. .text, .images, .masks, .detections, .depth, .document, .transcript, .audio, .video, .mesh, .tracks. Asking a result for the wrong kind raises, rather than returning something empty.
• Boxes are [x0, y0, x1, y1] in pixels everywhere, so a box from a vision model or a detector can be sent straight to a segmenter.
• Files come back as handles. .save(path) writes one; .read() returns the bytes. They stay on the server until you ask.
• Retries are safe. Every run() carries an idempotency key and is retried on connection failures and on 429/502/503/504, so a retry never runs — or bills — the work twice.
• Errors say what to change. A refused input raises InvalidInput whose message names the field and a value that works.
• Reasoning models think before they answer, and the thinking counts against max_tokens. Too low a cap is spent thinking: .text then raises IncompleteAnswer rather than returning "". Give reasoning models a few thousand tokens, or send reasoning_effort="low" to think less.
• Every result says what it cost: result.cost.usd, alongside result.timing.duration_ms.
• Masks and boxes draw onto the photo with pip install 'factorial-compute[images]': result.masks.draw(photo, "out.png") tints and numbers every mask, mask.layer(photo, colour) gives one see-through layer for stacking in HTML, result.detections.draw(...) boxes.
• Usage is in the model's own unit: tokens, images, audio_seconds, characters, video_seconds_generated, object_seconds, meshes. result.usage.units has it.

Which models exist

for model in f.models.catalog():
    print(model["id"], model.get("output"), model["unit"])
    print("  ", model["example"])

The catalogue lists the models you can call right now (catalog(all=True) for everything, with availability saying which are offline). Each entry says what kind of answer it returns (output), what every input field means (input), and gives an example input that works as written.

Longer work

handle = f.submit(model="flux-schnell", input={"prompt": "..."})
result = handle.wait()

submit returns before the work runs and survives your process exiting.