The decision-model lane has gotten crowded in half a month, with Microsoft, Cloudflare, Liquid AI and Shanghai AI Lab all entering. On October 21, infrastructure company Baseten jumped in too: its internal research team Base Labs open-sourced two decision models, mammoth-1 (27B) and elephant-1 (9B), and took the top spots on both major decision indexes (launch post).
What a decision model is: no writing, just probabilities
Unlike a generative LLM, a decision model does not write prose. Generative models doing classification have two chronic flaws: the compute is overkill for a single label, and the reply carries no reliable measure of certainty. Decision models target exactly these flaws — Baseten's definition is blunt: understand the request, make a decision, don't spend time writing a response. Given a question and a set of options, one forward pass returns a probability distribution over the options, with all scores summing to one. The docs describe three question types: choice — "which team owns this ticket: infrastructure, billing, security, or support?"; score — "how urgent is this ticket, low or high?"; boolean — "is this email a phishing attempt?" Its sweet spot is high-volume structured judgment: ticket routing, spam detection, prompt guardrails, routing between small and frontier LLMs. Baseten's comparison: versus a reasoning model, a decision model can be up to about 10x faster. More importantly, confidence is threshold-able — auto-execute at 90% confidence, escalate to a human below it — and the models can be post-trained so that confidence tracks actual accuracy, a reliability promise generative models struggle to make.
Qwen3.5 as the backbone, again
The two models were post-trained from Qwen3.5-27B and Qwen3.5-9B on 6.21 million decision examples — public plus synthetic data — across five domains: knowledge and reasoning, language, tools and automation, retrieval and classification, and arts and human taste. Training explicitly taught the models to answer "I don't know" when uncertain. Notably, Microsoft's Microsoft-Decision-1 from early October is also built on Qwen3.5-9B, and Shanghai AI Lab's Intern-Decision is Qwen-family too. The open-source decision-model stack is de facto converging on Qwen. Which to pick? Baseten suggests mammoth-1 for general-knowledge tasks and elephant-1 for quick reasoning.
The numbers and where to get them
Per Baseten's published numbers, mammoth-1 ranks #1 among 208 decision models on the Jev Decision Index 0.4 with a score of 71.24; elephant-1 scores 62.6, tied for #8 overall and the strongest 9B model on the board. On Baseten's replication of the Artificial Analysis Decision Index, elephant-1 ties for #1 with the previous best, d1-ultra. Weights are on Hugging Face (org baselabs), with two accompanying evaluation datasets, mammoth-1-decision-index and elephant-1-decision-index. The models are free to try for a week on Baseten Model APIs with zero data retention by default; training details are in the Base Labs technical report. Baseten's own evaluation checklist: accuracy, confidence calibration, and speed-and-cost at your traffic level.
So what
Judgment-type tasks are a huge share of AI deployment, yet they have long been hammered by models built to write essays. Decision models carve them out and optimize separately — a direct descendant of BERT-era classifiers, but with probability calibration and the ability to abstain. With Microsoft, Cloudflare, Liquid and Baseten all entering within a month, the category has moved from novelty to standard component. The question worth asking: in the AI apps around you, how much of that "generation" is really classification in disguise?