Strands Decider 2B is a small, open source, decision model optimized for fast experimentation, local development, and innovation.
Strands decider is one of a new class of decision models or system one models, a type of model that has been gaining a lot of attention since TypeSafe AI’s launch of Jev earlier this month. Unlike LLMs that can generate arbitrary output, decision models are designed to pick between sets of options (e.g. “Is the string ‘turn on the lights’ about the coffee machine? Yes or no.”, “What language is the phrase ‘sihamba ngokushesha’ in? English, Zulu, or Dutch.”) and assign simple numerical scores (e.g. “Is the phrase ‘this is the best doc I’ve ever read’ a positive sentiment? Between 0 and 1.”).
In exchange for this reduction in flexibility, decision models are faster and more capable at a given size, always produce an answer from the selected options, and can run with very low latency.
The flip side is that this approach (generating all outputs in a single parallel pass) makes it significantly worse at solving complex problems than reasoning models, and its lack of ability to generate text makes it unsuited for coding, chatbots, document summarization, and other common LLM tasks.
In addition, decision models give each decision a high-quality reliability score (i.e. “how sure can I be that this yes/no is correct?”), which is not available through frontier LLM inference APIs. They also make it highly efficient to ask multiple questions about the same prompt.
Strands Decider 2B is a 2 billion parameter model, suitable for running on a local CPU or GPU, which can return answers to meaningful questions in tens of milliseconds. Its accuracy and calibration is competitive with the other models in this class. Released as open source on GitHub with weights on Hugging Face, including training data and scripts.
Model Architecture
The core idea takes a pre-trained LLM torso (Qwen3.5-2B), removes the LM head, and replaces it with a pointer head which scores the answers offered by the torso for each option. The torso is fine-tuned with a rank-16 LoRA adapter. This is v19 of the architecture.
Performance
Measured on JevBench’s public set for accuracy and Brier score calibration: 3rd of 33 in the 2B class, 1st of 30 excluding just-over-2B models. Median latency around 115ms on Nvidia RTX3090; ~153ms on M3 MacBook for small tasks.
Use Cases
Early success for model routing, tool selection, evaluations, guardrails, memory, context management, and policy classification. Hybrid agents using LLMs for hard decisions and decider models for rote decisions to reduce cost and latency.
Getting Started
pip install strands-decider
strands-decider ask StrandsAgents/strands-decider-2B-hobson-v19 \
--state "Help! My payouts have been failing for 3 days! " \
--choice "Which team should handle this?=billing,sales,retail"Example output: choice_0 -> billing (confidence 0.768)
The repo includes examples using strands-decider-2b inside a Strands agent with intervention handlers for grounding and premature tool-call checks before execution.