Amazon's Side-Project AI Beat Rivals — Then Went Open Source

AI AGENTS

Nobody planned this week. Amazon just open-sourced a decision-making AI that doesn't chat, doesn't write, and can't explain itself — by design. It started as one engineer's side project. And it landed in the same week that OpenAI and Cloudflare shipped their own versions of the exact same idea.

Published October 3, 2026 · By Daniel Rao

Photo: Server racks in a data centre. Strands Decider 2B is built to run on ordinary hardware — AWS says it can return a decision on a laptop in about 150 milliseconds. Credit: Carl Lender / Wikimedia Commons, CC BY 2.0.

On Thursday, Amazon Web Services released a model that answers questions in a tenth of a second and costs nothing to download. It will not write you a poem. It will not debug your code, explain its reasoning, or argue with you. Ask it an open-ended question and you have wasted your time, because it cannot answer one. That is the entire point.

Strands Decider 2B does exactly one thing: given a fixed set of options, it picks one and tells you how sure it is. Yes or no. This route or that one. A score out of ten, with a probability attached. Then it shuts up. In an industry addicted to bigger, chatty models, Amazon just bet that the future of AI agents is a model that refuses to talk.

The strangest part isn't the model. It's how it got here. Strands Decider began as the side project of Marc Brooker, an AWS distinguished engineer, who read about a startup's radical new model, got curious, and tried to build his own version. His homebrew prototype briefly topped the JevBench leaderboard for models its size. Then AWS cleaned it up and shipped it as a product. This week, the same week, OpenAI announced a Decisions API built on its Luna model, and Cloudflare open-sourced two decision models of its own. Three tech giants. One idea. Zero coordination. Something is happening here, and it's happening fast.

The side project that escaped

Every detail of the Strands Decider story reads like an accident that became strategy. Brooker built the prototype after seeing Jev, the decision model from TypeSafe AI, and deciding to take his own crack at the design. According to TechCrunch's reporting, the homebrew version was good enough to briefly reach the top spot on the JevBench ranking for its size class — at which point AWS engineers took over, cleaned it up, and released it through Strands Labs, the company's group for agent-development tools.

What originally piqued my interest in this class of models, Brooker told TechCrunch, was something almost embarrassingly simple: "What originally piqued my interest in this class of models was that they make a perfect decider for a workflow step — 'what is the next thing for me to do here, based on where I am?'"

He told TechCrunch the push came from AWS customers, whose agent workflows didn't always need the capability — or the cost — of a full large language model. The decision model, he said, gives customers a step that is "more reliable, thanks to the confidence scores, thanks to the closed domain of answers, [and is] lower latency, potentially lower cost."

That is a polite way of saying something blunt: most of what AI agents do all day is glorified sorting. Route the ticket. Pick the tool. Check whether the action is safe. Approve, deny, escalate. Asking a frontier model to write an essay in order to say "yes" is like hiring a novelist to flip a light switch.

What a "decision model" actually is

Strip away the marketing and a decision model is a classifier wearing a very expensive suit. You hand it the state of the world — some text, some structured data — plus a set of typed questions. It answers all of them in one forward pass, in parallel, and hands back a choice, a probability for every option, and a confidence number. No streaming tokens. No chain-of-thought to wait for. No paragraph to parse.

The technical trick in AWS's version is what happens to the base model. Strands Decider starts from a pretrained Qwen3.5-2B base, then its builders removed the component that predicts the next word and replaced it with a small "pointer" component that scores the supplied answer options. The architecture diagram labels the design "Hobson." A rank-16 LoRA update adapts the base, and the new scoring head adds just over a million parameters. One forward pass, one probability distribution, done.

On speed, AWS's numbers are genuinely quick — with the usual vendor asterisks. The company says the model makes local decisions in tens of milliseconds on short tasks. An earlier checkpoint recorded a median of 106 milliseconds and a 95th percentile of 296 milliseconds on an Nvidia RTX 3090 across 230 requests, including the HTTP round trip; AWS also reports roughly 150 milliseconds median for small tasks on an M3 MacBook. On quality, version 19 lands around 72% accuracy with a 0.35 Brier score on the public portion of JevBench. AWS says that ranks second among public models around its size, and first among those that publish a complete training recipe.

Here is the asterisk the press release hopes you miss: the chart doesn't include Jev itself. Amazon's big chart comparing decision models has no line for the model it is supposedly cloning. VentureBeat, which got the announcement early, was admirably blunt about it — neither the numbers nor the charts establish that Strands Decider beats Jev overall. Keep that in mind. It matters later.

The $2.94 demo that broke the industry's brain

None of this week happens without Jev. TypeSafe AI released it on September 15, after roughly two years in stealth, alongside a $40 million seed round led by DCVC. The founder, Diogo Almeida, is a former OpenAI staffer who worked on ChatGPT and co-authored the InstructGPT paper. The name comes from William Stanley Jevons, the 19th-century economist behind Jevons paradox — the observation that efficiency drives demand. The company calls it a "System One" model, after Kahneman's fast, intuitive thinking: reflexes, not essays.

Jev does not generate text. It answers three kinds of typed questions — choice, score, yes/no — in a single parallel pass, returning probabilities and a calibrated confidence for each. End-to-end latency is 70 to 500 milliseconds through the hosted service. The price is $0.042 per million input tokens, with output free. It is text-only, English-first, and TypeSafe has published neither the weights nor the training recipe. The Information has reported the company is in talks to raise $1 billion or more at a valuation above $10 billion. Almeida declined to comment on that, which is what founders do when the number is real enough to be worth protecting.

The demo that made the rounds, though, is the one that explains this entire week. At a hackathon, QueryStory's Shapor Naghibzadeh used Jev to monitor an AI agent's actions. The bill: $2.94. The same monitoring through a frontier LLM: $372. Same check, same actions, one-hundredth the cost.

That is the number that broke the industry's brain. Almeida told the Wall Street Journal the model is already in use at around 25% of Fortune 500 companies, and that TypeSafe was processing "a trillion tokens per day" about a week before the interview — "and obviously, growth has been exponential." His pitch is that today's leading models, for all their fluency, are "unbelievably useless for automation," because software needs stable, repeatable layers to build on, and chatbots don't behave that way.

He's not wrong, though he's obviously selling. And the copycats? Almeida told TechCrunch the wave is "more about architectural novelty than practical intelligence." Then, with better humour than most founders manage under siege, he joked that OpenAI's clone might mean "the beginning of the clone wars."

OpenAI and Cloudflare had the same idea — in the same week

OpenAI's version arrived first chronologically, previewed at its DevDay in late September. The Decisions API gives the Luna model a predefined set of options to choose from and answers "in a fraction of a second," as Simon Willison's live blog recorded it. It's aimed at classification, routing, and picking the next step in an agent loop — content moderation queues, support ticket routing, the endless small forks in an agent's road. It's in limited preview, with no public price, no published request schema, and — as of this week — no entry anywhere in OpenAI's own developer documentation. The capability exists in OpenAI's DevDay recap and in what a spokesperson told reporters. Everything else is still scaffolding.

TechCrunch reported a telling backstory: OpenAI built the Decisions API after a series of incidents in which its agents misbehaved on the open internet. Which is a remarkable sentence to read twice. The frontier lab's own agents were doing things nobody asked them to do, and the fix is a smaller, dumber model whose entire job is to say "no, not that." Sam Altman framed it at DevDay: "By focusing the model on that choice, we can make it extremely fast while keeping capabilities like image understanding, broad language support, and safety protections." Even Washington is now threatening prison time for AI misbehaviour — the guardrail problem is no longer theoretical.

Cloudflare's entry, announced October 1, is arguably the most aggressive of the three. Clef comes in two sizes — a 27-billion-parameter model for accuracy, and a 9-billion-parameter Clef-flash for speed — both on a Qwen backbone, both open-weight under Apache 2.0, both on Hugging Face. Cloudflare claims Clef runs 2.5 times faster than Jev at the median across 43 benchmark runs, and Clef-flash 13 times faster: median latencies of 209.3 milliseconds and 38.8 milliseconds, against Jev's 524.1. It also claims Clef scores highest on 7 of 10 decision benchmarks, beats Jev in three of four areas on Jev's own benchmark suite, handles images and video (Jev is text-only), and ships with a 64,000-token context window. The API is fully Jev-compatible — a drop-in replacement designed to let developers swap endpoints without touching their code.

We checked the published benchmark tables ourselves, and here's the asterisk nobody's putting on the poster: every one of Cloudflare's numbers is self-reported, and none of them have yet been reproduced on the official Jev Decision Index on Hugging Face. The Register flagged the same gap. Cloudflare's AI Platform group product manager Michelle Chen told The Register the training datasets aren't public either, and confirmed the hardware demands are real — Clef-flash needs a GPU with at least 41 GB of video memory, the full model about 85 GB. "This is assuming single concurrency and a 64k context window," she added. Also worth noting: Clef costs $0.24 per million tokens on Workers AI, nearly six times Jev's $0.042 rate. Faster, yes. Cheaper, no.

Photo: A technician working on a server rack at NERSC. Decision models like Clef, Jev and Strands Decider sit inside AI agent workflows, making thousands of tiny yes-or-no calls per task. Credit: Derrick Coetzee / Wikimedia Commons, CC0.

The honest scorecard

So who won the week? Nobody. And that's the honest answer, because the entire contest is being scored by the contestants.

Amazon's benchmark chart doesn't plot the model it's supposedly killing. Cloudflare is claiming victory on benchmarks it designed, ran, and scored itself, in a category where the independent scoreboard — the Jev Decision Index — hasn't reproduced any of its numbers. OpenAI has announced an API with no price, no docs, and no schema. This is not a benchmark race. It is a press-release race with benchmark-shaped decorations.

But there is one verifiable difference, and it matters more than all the latency charts combined. Of the three new entrants, Amazon is the only one giving you everything: the weights, the code, the training data, the training scripts. OpenAI's Decisions API lives inside OpenAI's hosted stack, tied to Luna — build on it and you're betting your production workflow on one company's uptime and pricing whims. Cloudflare published weights but kept its training data private. Amazon published the recipe. For enterprises, that is colossally more important than whether v19 scores 72% or 76% on a benchmark nobody outside the vendors fully trusts. You can inspect it, fine-tune it, run it on a laptop, keep your data in your own building.

The counterpoint, which VentureBeat made well and which deserves repeating: Jev's $0.042 per million input tokens means a billion tokens costs $42. Amazon's model is free to download and then costs you whatever your GPUs, cloud bill, and on-call engineers cost. Unless you already have spare compute or you care about keeping decisions local for privacy, nobody has demonstrated that self-hosting Strands Decider is actually cheaper than Jev's hosted API. The sticker price is zero. The operating price is unknown. Those are different things, and anyone who confuses them is selling you something.

Almeida's dice joke — "Fast and cheap is very easy, you know. If you want it really fast and cheap, use dice, right? Intelligence is the hard part" — is the right question aimed at the right target. Speed is now a commodity. The scarce resource is calibrated judgement: a model that knows when it's wrong and says so. TypeSafe's bet is that calibration, ease of consumption, and a hosted API you can call in an afternoon still beat a free download that takes a week to deploy. That's a real argument, not a chart.

What happens next

The category is barely three weeks old and it's already crowded: Jev, Strands Decider 2B, Clef and Clef-flash, Ollama's trio of open decision models from Bespoke Labs and Together AI, plus Laya, Kev, Mapika's decider family, and Stanford and Nvidia's CLM-8B probing the same trade-offs. The shoddy era of vendor benchmarks will end the way it always ends — when enterprises run these things on their own policies, documents, and edge cases, and the marketing charts meet reality.

Watch two things. First, whether decision models actually make agents safer, or just cheaper — a cheap probabilistic guard is not a security boundary, and adversarial input can still talk its way past the referee, as Anthropic's own warnings about AI running ahead of its guardrails keep reminding us. Second, whether openness becomes the standard or the exception. TypeSafe is still API-only. Cloudflare kept its data. Amazon gave away the whole kitchen. If developers reward the open recipe — and the r/LocalLLaMA crowd, which gave the Clef announcement 154 upvotes in hours, suggests they might — the next round of this race won't be fought on latency at all. It'll be fought on trust.

The side project won the week. Whether the side project's company wins the category is a different question entirely. The agents are getting cheaper, faster referees either way — the same agents Shopify is already letting write entire storefronts. Somebody has to blow the whistle when they go rogue. Now we have three whistles, and only one of them comes with the blueprint.

AWS Strands Decider 2B: the questions everyone asks

Who built it?

It began as a side project by Amazon engineer Marc Brooker and is now developed by Strands Labs, Amazon's agent-framework team.

What does it cost?

It is open source: Amazon released the weights, the code, the training data and the training scripts, unlike its API-only rivals that week.

How does it compare to OpenAI's Decisions API and Cloudflare's Clef?

All three launched in the same week. Amazon's model is a 2-billion-parameter decision model that runs on a laptop with a 150-millisecond median response in Amazon's tests; OpenAI's Decisions API was announced with no price, no docs and no schema; Cloudflare's Clef models are open-weight, priced at $0.24 per million tokens against Jev's $0.042 in the post's comparison.

Why do vendor benchmarks need a disclaimer?

Because the contest is scored by the contestants: Amazon's benchmark chart does not plot the model it is supposedly killing, Cloudflare claimed victory on benchmarks it designed, ran and scored itself, and the independent Jev Decision Index had not reproduced any of those numbers at publication.

Sources: TechCrunch, "Amazon releases its own Jev clone as decision models flood the web" (Oct. 1, 2026); VentureBeat, "Amazon unveils a free, fast, open source Jev killer" (Oct. 1, 2026); The Register, "Cloudflare tries to outplay Jev with open-weight Clef models" (Oct. 1, 2026); Startup Fortune, "Cloudflare Open Sources Clef to Challenge OpenAI and Amazon on AI Agent Decisions" (Oct. 2026); The Wall Street Journal, "Startup TypeSafe AI's Jev Model Sparks Copycats, Talk of LLM Alternatives" (Oct. 2026); Cloudflare changelog, "Introducing Clef" (Oct. 1, 2026); Simon Willison's DevDay live blog; intelligibberish, "Decision Models: Ollama and OpenAI Ship Jev APIs in 24 Hours" (Oct. 1, 2026). Speed, pricing and benchmark figures are vendor-reported unless noted; the "chart excludes Jev" caveat and the unreproduced Jev Decision Index scores are from VentureBeat and The Register respectively.

Daniel Rao is a technology correspondent for TechPulse USA covering AI infrastructure and the agent economy. He writes about the systems underneath the chatbots — the models, the benchmarks, and the business models.

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