Amazon open-sources Strands Decider 2B: a small decision model for agent workflows, runnable locally
TechCrunch and the Strands blog say AWS released Strands Decider 2B, an open-source decision model that picks among preset options with confidence scores. Built on Qwen3.5-2B, it can run on a local CPU or GPU; the team cites median latency on the order of tens to about a hundred milliseconds and has open-sourced weights and training data.
TechCrunch reports that Amazon Web Services released an open-source decision model inspired by TypeSafe’s Jev. The same week OpenAI announced a similar offering. Amazon’s Strands Decider 2B is described as a high-speed, low-cost way to choose among pre-decided options and report confidence; it is fully open-sourced, available now, and small enough to run locally. AWS distinguished engineer Marc Brooker built a homebrew after seeing Jev, briefly topped the Jevbench ranking for models of its size, and Strands Labs cleaned it up for release.
The Strands blog says decision models (also called system one models) do not generate arbitrary text like LLMs; they answer within given options (yes/no, multiple choice, 0–1 scores) in exchange for speed and low latency, always landing on a selected option. The trade-off is weaker complex problem-solving than reasoning models, and unsuitability for coding, chatbots, or summarization. They also supply a reliability score the blog says frontier LLM inference APIs typically lack. Architecturally, the team takes a pretrained LLM torso (Qwen3.5-2B), removes the LM head, and replaces it with a pointer head of just over a million parameters, with the torso fine-tuned via a rank-16 LoRA adapter.
Official runtime figures: about 2 billion parameters, suitable for local CPU or GPU; median decision latency around 115ms on a local Nvidia RTX 3090, and about 153ms median for small tasks on an M3 MacBook. On JevBench’s public set, the team says accuracy and calibration are competitive in the 2B class (3rd of 33 in class; 1st of 30 excluding just-over-2B models). Early uses include model routing, tool selection, evaluations, guardrails, memory, policy classification, and hybrid agents that reserve LLMs for hard decisions. Install via pip install strands-decider; weights and code are on Hugging Face and GitHub. Brooker told TechCrunch such models fit a workflow “next step” with more reliability from confidence scores and a closed answer domain, plus potentially lower latency and cost. Latency and benchmark figures are vendor or interviewee claims, not independent tests by this site.
Sources
- Amazon releases its own Jev clone as decision models flood the web — TechCrunch AI
- Introducing Strands Decider 2B: a small, open source, decision model — strandsagents.com