AWS Strands Labs Releases Strands Decider 2B: An Open Source Decision Model That Picks Options in About 115 ms

AWS Strands Labs has unveiled Strands Decider 2B, an innovative open-source decision model designed to streamline binary and multi-option decision-making in AI applications. Built on Qwen3.5-2B-Base and released under the Apache-2.0 license, this compact model represents a significant advancement in efficient AI inference, delivering structured decision outputs with remarkable speed and accuracy.

Strands Decider 2B operates fundamentally differently from traditional large language models by returning structured decisions rather than generating text. The model delivers choices, yes/no probabilities, and confidence scores in a single forward pass, eliminating unnecessary computational overhead. Performance testing demonstrates impressive efficiency: the model achieves a median inference time of approximately 115 milliseconds on an RTX 3090 GPU, while scoring 0.723 on the JevBench evaluation standard—a benchmark designed to assess decision-making capabilities.

• Enhanced Speed and Efficiency: The 115 ms inference time enables real-time decision-making applications previously impractical with larger models
• Accessibility: As an open-source model under Apache-2.0 licensing, organizations can deploy Strands Decider 2B without restrictive licensing constraints
• Reduced Computational Requirements: Operating effectively on consumer-grade hardware like RTX 3090 cards lowers infrastructure costs and democratizes AI deployment
• Structured Output Advantage: Non-text output eliminates hallucinations and post-processing overhead common in traditional LLMs
• Production-Ready Calibration: Built-in confidence scores provide quantifiable reliability metrics for downstream applications

Strands Decider 2B addresses a critical gap in the AI ecosystem: the need for lightweight, specialized models that prioritize speed and reliability over generalization. As organizations increasingly adopt AI systems for decision support across industries—from supply chain optimization to healthcare diagnostics—having access to fast, accurate, open-source decision models proves invaluable. This release signals AWS's commitment to democratizing advanced AI capabilities while empowering developers to build efficient, responsible AI applications without expensive computational infrastructure. The model's performance metrics suggest a new paradigm where specialized, smaller models can outperform larger generalists in specific domains.