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Cicero

Cicero is a decision model: it reads a state (text, a chat, or up to 4 images) and up to 64 typed questions, and returns a calibrated probability for every option of each question. No text is generated: each question is answered in one forward pass instead of a generated answer.

• 71.33 on the public JDI (Jev Decision Index), with 3B active parameters.
• 41 ms median latency.
• Ahead of Decider v1.2 in every area.
• Best on Arts & Human Taste of the models compared below.
• Reads images (up to 4) and contexts up to 131,072 tokens.

Model Details

Property	Value
Base	Qwen3.5-35B-A3B, 3B active
Questions	yes/no, choice, score; up to 64 per request
Output	a probability per option
Context	131,072 tokens
Inputs	text, chat, up to 4 images

How it works

A request is a state and typed questions. We lay each question out as plain text (the context, then the question, then its options lettered A, B, C) and the model reads that prompt in a single forward pass. The answer is read off the model's scores for the option letters at the end of the prompt, turned into probabilities and calibrated for each question type.

Question type	Asks	Answer
noul	a yes/no question	probability of yes and no
choice	pick one of up to 255 options	a probability per option
score	rate on a scale, e.g. 1-5	a probability per score

Examples

Two real rows of the public JDI suite, with the probabilities Cicero returned.

BANKING77, one choice question over 77 intents: "I still have not received my new card, I ordered over a week ago."

Intent	Probability
card_arrival	0.973
card_delivery_estimate	0.021
card_not_working	0.001
card_linking	0.001

BFCL, one conversation, five yes/no questions: "Can you find me the recent news articles about Son's latest goal in the sports category?" Question: invoke tool?

Tool	Probability
HNA_NEWS.search	0.999
HNA_WQA.search	0.001
OpenWeatherMap.get_current	0.000
ControlAppliance.execute	0.000
cookbook.search_recipe	0.000

Performance

Jev Decision Index (JDI) 0.4.1, public suite: chance-corrected skill (0 = chance, 100 = perfect). Cicero's scores are from our own run of the public suite with the official kit. Best in each row in bold.

Area	Cicero	mammoth-1	elephant-1	Decider v1.2	Drex Max
Tools & Automation	87.0	88.6	88.0	85.4	81.9
Retrieval & Classification	76.1	77.4	76.2	68.1	68.8
Language Understanding	74.6	76.1	73.8	71.9	72.2
Arts & Human Taste	64.4	64.1	61.6	56.2	53.4
Knowledge & Reasoning	56.1	68.0	60.6	55.3	57.1
Public JDI	71.33	75.34	72.24	67.74	67.52

The chart shows the board's decision models that are available on OpenRouter.

Training

Cicero is a post-train of Qwen3.5-35B-A3B, a mixture-of-experts model with 35B parameters and 3B active. The vision tower is the base model's.

Citation

@misc{cicero2026,
  title  = {Cicero},
  author = {{Butler Compute, Inc.}},
  year   = {2026},
  note   = {Decision model},
  url    = {https://butlercompute.com/research}
}