Clef-Flash is a 9B multimodal model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, or video, and returns a probability for every allowed option of every question in a single forward pass. There is no free-form text generation and no output parsing.
The Clef-Flash API is fully compatible with Jev and SystemOne.
Clef-Flash is post-trained from Qwen/Qwen3.5-9B. See Clef for the larger variant.
Backbone: Qwen/Qwen3.5-9B with its vision encoder, stored as standard sharded safetensors.
Joint schema head: a small transformer head that reads the backbone's final hidden states, routes evidence from the state to each question, and scores all options of all questions jointly.
Output: one logit per allowed option for each question. Apply a softmax per question to get probabilities.
systemone takes a Jev/SystemOne POST /v1/systemone request body and returns the same response body: model, answers keyed by question ID, and usage. A choice answer has choice, confidence, and probabilities; a score answer has the expected score, confidence, legend, and probabilities; a noul answer has the probability of true. instructions is optional, and images and videos may be added to the request.
from joint_schema_model import systemone
response = systemone(model, processor, {
"model": "clef-flash",
"state": "Our checkout started returning errors and orders are blocked.",
"questions": {
"department": {
"type": "choice",
"instructions": "Which team should handle the message?",
"criteria": {"billing": "Payments or invoices", "technical": "Bugs or outages"},
},
"urgency": {"type": "score", "criteria": ["Can wait", "This week", "Today"]},
"outage": {"type": "noul", "instructions": "Is a service down?"},
},
})
print(response["answers"])
Add images (PIL images) or videos (frame arrays) to the record and pass the processor to encode_record. Optional processor arguments go in media_kwargs.
from PIL import Image
record = {
"state": {"task": "Review the attached receipt."},
"images": [Image.open("receipt.jpg")],
"questions": {
"legible": {"type": "noul", "instructions": "Is the receipt total legible?"},
},
}
encoded = encode_record(processor.tokenizer, record, processor=processor)
Text-only and multimodal records can be mixed in the same batch.
Any string or JSON value describing the situation to decide on
images, videos
Optional lists of images or video frame arrays
media_kwargs
Optional keyword arguments for the image/video processor
questions
Mapping of question ID to question
Each question has:
type: noul (true/false), choice (named options), or score (ordered options)
instructions: what to decide; optional, and the question ID is used when it is omitted
criteria: for choice, a mapping of option ID to description; for score, a list of option descriptions indexed from 0; for noul, optional descriptions for true and false
encode_record accepts max_length (default 16,384 tokens) and max_state_tokens to bound the input.
Per-benchmark results from our internal run of the Decision Index 0.2.1 suite. Scores are percentages; ForecastBench is a Brier score, where lower is better. The last two rows are request latency in milliseconds, where lower is better. The best value in each row is in bold.
Decision accuracy on four end-to-end business workflows from Typesafe Evals, scored against consensus reference labels. All models are scored on the same dataset revision and case cohort.
