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.

curl -LsSf https://api.factorialcompute.com/install.sh | sh -s -- inv_...   # once per machine
pip install factorial-compute                                               # in your project

from factorial_compute import Client

f = Client()  # the key `factorial login` saved, or FACTORIAL_API_KEY if set

# 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.

In a coding agent

The install line sets up Claude Code: a skill that tells it what Factorial does, and factorial mcp - five tools (find_models, describe_model, quote, run, get_result) it can call without writing code, running as its own key. factorial install sets it up again later.

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. f.quote(model=..., input=...) says it before anything runs; max_cost_usd= on run() or submit() refuses a call that would cost more, raising CostCapExceeded.
• Answers chain. Pass an earlier answer's file as an input - {"image": result.images[0]} - and it goes by reference, never downloaded and uploaded again.
• Several models as one workload: with f.workflow() as w: - each w.run(...) returns at once, an input can name an earlier step's answer (photo.image, seen.text), and the server runs steps as their inputs become ready, in parallel, with one status, one cost and the whole graph.
• Submitted work can call back: submit(..., webhook_url=...) posts the id and status when it finishes; fetch the execution for the answer.
• Masks and boxes draw onto the photo: 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.