Amazon Ships Strands Decider 2B, an Open-Source Jev Rival
TL;DR
- Amazon's Decider 2B was initiated by a single Distinguished Engineer after TypeSafe shipped Jev in September, moving from prototype to GA in weeks.
- First place on JevBench among models shipping a full training recipe means Amazon open-sourced the pipeline, not just the weights.
- MarkTechPost's four-model benchmark table confirms at least three Jev competitors now exist, validating TechCrunch's framing of a decision-model market flood.
Amazon has published a roughly 2-billion-parameter decision model built on Alibaba's Qwen3.5-2B base, released free under Apache 2.0 on Hugging Face. VentureBeat reports that Strands Decider 2B hits about 72% accuracy on the JevBench public set (v19), with a 106ms median and 296ms p95 latency on an Nvidia RTX 3090 and roughly 150ms median on an M3 MacBook for small tasks.
The model skips text generation entirely. 'Strands Decider begins with the pretrained Qwen3.5-2B base model. According to AWS, its developers removed the component that predicts the next word and replaced it with a small "pointer" component that scores supplied answer options,' VentureBeat's Carl Franzen writes. A rank-16 LoRA plus the scoring head together add about a million trainable parameters, under an internal architecture AWS calls Hobson.
The target is TypeSafe AI's Jev. 'TypeSafe launched Jev on September 15, calling it a "System One" model' that takes application state and typed questions and returns choices, scores or yes/no probabilities without generating prose. Jev charges $0.042 per million input tokens with no output-token fee, with end-to-end latency reported in the 70 to 500ms range.
'The central idea is simple: if a program needs an answer such as "Should this tool run?" or "Which of these three routes fits this request?", asking a large language model to write an explanation can add time and expense,' the piece explains. Amazon's pitch is that teams can self-host the weights, use the model to route requests, select tools, evaluate outputs or review an agent's actions, and inspect training materials and code.
Franzen does not call it a win. 'AWS's distinguishing proposition is therefore less a demonstrated Jev-killer than an open, reproducible decision layer tied to an existing agent framework,' he writes. A competing open model, Mapika decider-2b v11, still scores 76% on the same benchmark. The article flags that 'adversarial text in an agent's input can influence its verdict' and that AWS has not shown self-hosting beats Jev's hosted price once hardware and ops are counted.
The release lands the same day Cloudflare open-sourced its own Clef decision models, a sign the decision-head genre is maturing into its own layer in the agent stack.
What others are reporting
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TechCrunch Read →
Frames the release as part of a broader decision-model market flood and surfaces TypeSafe's skepticism that proliferating architectures translate to real competition.
"What originally piqued my interest in this class of models was that they make a perfect decider for a workflow step" — Marc Brooker
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MarkTechPost Read →
Provides a direct four-model benchmark table and details the pointer-head architecture swap; calibration data shows 0.9-plus confidence answers correct ~95% of the time.
"A decision model only picks between options or rates on a scale"
Originally reported by venturebeat.com
Read the original article →Original headline: Amazon's Strands Labs Ships Strands Decider 2B, Open-Source Jev Rival Scoring 72% on JevBench