Strands released Strands Decider 2B, a 2-billion-parameter model designed to quickly pick between predefined options rather than generate arbitrary text. It runs locally in ~115ms and ranks third among 2B-class decision models on accuracy and calibration benchmarks.

• Decision models sacrifice flexibility for speed and reliability: they choose from given options and assign confidence scores, making them 100x faster than LLMs for simple classification tasks but unsuitable for text generation, coding, or reasoning.
• The 2B parameter size hits a sweet spot for local experimentation—small enough to run on consumer hardware, large enough to handle meaningful work like tool selection, routing, and guardrails in agentic workflows.
• Strands open-sourced the full model, training data, and 19 iterations of architecture refinements on GitHub and Hugging Face, explicitly inviting community contributions to the decision model space.