Amazon's strands-labs team open-sourced Strands Decider 2B on October 1. It writes no code, chats, or summarizes — it only picks from options or rates things on a scale. Weights, training data, and scripts are all on Hugging Face and GitHub under Apache 2.0, running on a local CPU or GPU.

The category first: what is a decision model

The wave started with TypeSafe AI's Jev earlier this month. Decision models (also called "system one" models) split the labor with LLMs: LLMs generate arbitrary text, while decision models handle three question types — noul (yes/no), choice (pick one of N), and score (rate on a rubric). In exchange, they are faster at a given size, always answer from the given options, and attach a calibrated confidence to every decision — something the official post stresses that frontier LLM inference APIs do not offer.

The blog gives a concrete example: given "Help! My payouts have been failing for 3 days!" and asked "Which team should handle this? billing/sales/retail", the model answers billing at confidence 0.768, listing probabilities for all three options.

Architecture: cut the LM head, add a pointer head

The approach is surgical: take the Qwen3.5-2B pretrained torso, discard the language-modelling head — the model loses its ability to generate text — and replace it with a pointer head of about a million parameters. The head scores each option by comparing the hidden state at the <answer> position against each option's hidden state. One forward pass, no decoding loop. The torso is adapted with a rank-16 LoRA.

Because the head holds no per-option parameters, nothing can learn "the first option is usually right", and nothing caps how many options a question may carry — label sets are defined by the request, not baked into the weights. All three question types come out of the same masked softmax.

Numbers: on the JevBench public set of 231 tasks, accuracy is 0.723 (167/231); by tier, easy 1.000, standard 0.875, hard 0.505; answers at confidence 0.9 or higher are right about 95% of the time. On latency, an RTX 3090 delivers a 115 ms median and 299 ms 95th percentile; an M3 MacBook handles small tasks in a 153 ms median. Per the third-party board the post cites, it ranks 3rd of 33 in the 2B class, and 1st of 30 when just-over-2B models are excluded.

One bonus: Qwen3.5-2B-Base is natively multimodal. With --vision the server keeps the vision tower, and the same v19 checkpoint answers over images with no retraining — on NaturalBench it is better calibrated (ECE 0.014) than an image-trained 2B decider (0.080).

The full recipe is open — failures included

The repository's most unusual part is not the model but the process. Training cost is pressed to a reproducible level: about 11 hours on one RTX 3090 retrains the whole recipe, or 1 hour 10 minutes on eight H100s with FAST mode. Data source licences are listed item by item, and a reproduction contract is stated in the repo.

The research discipline is more interesting: since v9, every training run states its predictions and failure conditions before training, and the outcome is appended after the run without editing. The reference model today is v19; the latest experiment v20 scored 169/231, within retrain noise of v19's 168, and did not displace it. The repo notes that six retrains of the v17 recipe had a standard deviation of 3.2 tasks — a gap under 10 tasks between single runs is unresolved. Keeping failed experiments honestly on record is rare among model releases.

So what

Decision models sit at a specific spot: the judgments inside agent workflows that "don't deserve a frontier LLM call" — should this tool run now, are these arguments grounded in what the user said, where should this request route, did the output pass the guardrail. The official use-case list is exactly that: model routing, tool selection, argument checking, triage, guardrails, low-cost evaluations, and hybrid agents — LLMs take the hard decisions, deciders take the rote ones, cutting cost and latency together.

The lane is getting crowded: on the same day Strands Decider shipped, Cloudflare released its own open decision model Clef; a third-party release tracker summed up October's opening as "no frontier launch, a cluster of small specialist models instead". Decision, speech, transcription — these "small and specialized" categories are becoming a new layer of agent infrastructure.

Strands Decider 2B's differentiation is not the score but the fully open production line — you can retrain it on your own 3090 in 11 hours and fork your own version. If you build agents, spend an afternoon: pip install strands-decider, serve it locally, keep the expensive judgments for the big model, and hand the rest over.

References: Strands Agents blog, GitHub repository.