Liquid AI claims its released open-weight d1 decision models (d1-3B, d1-omni-600M) — topping the sub-10B Decision Index and returning typed answers with zero output tokens from datacenter to Jetson — become adopted decision/routing components for edge, browser, and agent workflows; sustained adoption beyond launch week confirms it, a post-launch fade refutes it.

state: watchingheat: mediumuncertainty: highconvergesscott: highone-pass-decision-models edge-inference liquid-ai local-inferenceLiquid AI

What is this?

Liquid AI released two open-weight 'decision models' (d1-3B and d1-omni-600M) on October 7, 2026, claiming they top the sub-10B Decision Index and return typed answers with zero output tokens across datacenter-to-Jetson deployments. Day-one third-party adoption signals appeared: a WebGPU browser port, GGUF conversions, and llama.cpp PRs from Liquid-affiliated authors. One independent benchmark (reddit.post.1x0jsdv) tested the models against embedding models for a ~10k customer-service routing task; embeddings outperformed, though the user noted classifiers may generalize better. Sustained adoption beyond launch week is unproven — the evidence is 1–2 days post-release. The supplied material does not include the vendor's own benchmark methodology for the Decision Index claim, nor independent verification of the zero-output-token claim.

Why it matters to Scott

Liquid AI’s open typed-decision models independently converge with Scott’s cheap-model-front-door and TypeSafe jev doctrine: a near-free structured decision call ahead of expensive agent work. The independent routing result, where embedding models beat d1 on one customer-service task, makes this materially relevant to Scott’s active routing stack rather than merely another small-model launch: it is a candidate to evaluate by task, hardware placement, and fallback path rather than accept on vendor Decision Index claims.

dev:concept.cheap-model-front-doordev:technology.typesafe-jevdev:project.jevdev:concept.task-aware-model-routingdev:concept.hardware-aware-local-inference

queries asked of Scott's wikis

one-pass decision model architecture and routing use cases
edge inference economics for sub-3B models on Jetson-class hardware
agent routing patterns: learned routing vs embedding-based classification
local inference sovereignty and open-weight model adoption curves
model distillation for routing/decision tasks vs full generative models
cheap-model-front-door pattern: small models as gatekeepers to larger ones

Measured heat

now 0 pts/hpeak 90 pts/hcomments 0/hpeers p16momentum: steady3 platformsage 123h

points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

10-06 13:00	⭐ origin echo-reconstructed	"Today we release Open d1: two open-weight multimodal models in our d1 decision model family. d1-3B: text + vision; d1-omni-600M: text + ima Liquid AI (@liquidai) on x (echo) · attributed from reddit.post.1x02pzb, reddit.post.1x03mrh, reddit.post.1x01zg6, reddit.post.1x01z7m, reddit.post.1x01xg3	—
10-07 17:00	first on r/LocalLLaMA · published · +28.0h	LiquidAI/d1-3B · Hugging Face iamn0	—
10-08 19:05	first on hacker news · published · +54.1h	Can Decision Models Abstain? elmsec	—
10-07 17:00	amplified on r/LocalLLaMA 👑	reddit.post.1x01xg3 iamn0	peak 159 · 68 comments · 56% of case engagement
10-07 17:02	amplified on r/LocalLLaMA	reddit.post.1x01z7m Nota_ReAlperson	peak 31 · 15 comments · 11% of case engagement
10-07 17:02	amplified on r/LocalLLaMA	reddit.post.1x01zg6 jacek2023	peak 47 · 17 comments · 16% of case engagement
10-07 17:30	amplified on r/LocalLLaMA	reddit.post.1x02pzb RespectJaded15	peak 3 · 4 comments · 2% of case engagement
10-07 18:04	amplified on r/LocalLLaMA	reddit.post.1x03mrh FinancialAd1961	peak 33 · 7 comments · 10% of case engagement
10-08 06:35	amplified on r/LocalLLaMA	reddit.post.1x0jsdv Infinite-Local5435	peak 3 · 16 comments · 5% of case engagement
1 more amplifiers in ainews.case_chain
10-07 18:20	our radar first saw it · +29.3h	discovery anchor: reddit.post.1x02pzb	—

pace: p83 vs 1247 stories at the 96h mark (now 123h old) — ahead of astra-zerobench-human-baseline (1.0x), behind mlxfast-agent-engine-rewrite (1.0x)

Evidence (8) — ⭐ canonical anchor

source	object	author	score	comments
🟠 reddit	Liquid AI just dropped Open d1: open-weight decision models LocalLLaMA Retrieved article excerpt Open article · Retrieved 2026-10-07T18:32:54.502223+00:00 [@liquidai](https://x.com/liquidai) [Liquid AI](https://x.com/liquidai)[@liquidai](https://x.com/liquidai) Today we release Open d1: two open-weight multimodal models in our d1 decision model family. > d1-3B: text + vision > d1-omni-600M: text + image or text + audio > Real-time decision making anywhere, from data centers such as [@nvidia](https://x.com/nvidia) DGX to RTX workstations to Jetson at the edge. 1/ [5:01 PM · Oct 7, 2026](https://x.com/liquidai/status/2107878924831379676)·[29.2K Views](https://x.com/liquidai/status/2107878924831379676) [35](https://x.com/compose/post?in_reply_to=2107878924831379676) 72 623 286	RespectJaded15	3	4
🟠 reddit	omni-d1 600M by Liquid AI running in the browser using WebGPU LocalLLaMA	FinancialAd1961	33	7
🟠 reddit	d1-3B and d1-omni from LiquidAI LocalLLaMA	jacek2023	43	16
🟠 reddit	LiquidAI release LocalLLaMA	Nota_ReAlperson	30	15
🟠 reddit	LiquidAI/d1-3B · Hugging Face LocalLLaMA	iamn0	159	68
🟧 echo.x ⭐	"Today we release Open d1: two open-weight multimodal models in our d1 decision model family. d1-3B: text + vision; d1-omni-600M: text + ima	Liquid AI (@liquidai)	—	—
🟠 reddit	Some recent decision models <=3B on internal benchmarks vs fine-tuned embedding model LocalLLaMA	Infinite-Local5435	2	16
🟧 hn	Can Decision Models Abstain?	elmsec	3	0

Interpretation history

2026-10-09T04:08:44Z
First independent benchmark (reddit.post.1x0jsdv) is negative: a fine-tuned Qwen Embedding 0.6B beat both d1 models on a ~10k customer-service routing task (0.786 vs 0.705/0.557 accuracy). Day-one adoption signals are real (WebGPU port, GGUFs, llama.cpp PRs from Liquid affiliates, HN discussion on abstention) but all within launch week. Sustained adoption beyond launch week — the hypothesis's confirmation condition — remains unproven.
2026-10-08T23:06:44Z
evidence attached: hn.story.50010480 — Blog post examines abstention capability in decision models, relevant to adoption of typed decision models like Liquid d1.
2026-10-08T17:34:07Z
grounded: converges/high — Liquid AI’s open typed-decision models independently converge with Scott’s cheap-model-front-door and TypeSafe jev doctrine: a near-free structured decision cal
2026-10-08T07:30:55Z
First independent benchmark appeared (user tested d1 models vs embedding models for routing; embeddings won on their task, though classifier generalization noted). Day-one third-party ports (WebGPU, GGUF/llama.cpp PRs from Liquid affiliates) confirm developer adoption signal, but sustained adoption beyond launch week remains unproven — still early.
2026-10-08T07:01:41Z
evidence attached: reddit.post.1x0jsdv — User benchmarks Liquid AI's d1 decision models (D1-Omni-600M, D1-3B) against embedding models for routing, directly testing the case's claim about adoption as decision/routing components.
2026-10-07T18:35:21Z
case created — A distinct vendor release in the fast-consolidating one-pass-decision-model category — second vendor after Intern-Decision — already showing day-one third-party adoption (WebGPU browser port, GGUFs, mechanism writeups), so it earns its own case alongside, not inside, the Intern episode.

Decision trace

10-09 15:08repriceFirst independent benchmark (reddit.post.1x0jsdv) is negative: a fine-tuned Qwen Embedding 0.6B beat both d1 models on a ~10k customer-service routing task (0.786 vs 0.705/0.557 accuracy). Day-one ado
10-09 10:15attention_routeThe editor compared this story and chose to keep watching.
10-09 10:06attention_candidateattach
10-09 10:06attachBlog post examines abstention capability in decision models, relevant to adoption of typed decision models like Liquid d1.
10-09 09:59propose_attachBlog post examines abstention capability in decision models, relevant to adoption of typed decision models like Liquid d1.
10-09 04:35sensor_dirtycomment_update
10-09 04:34groundLiquid AI’s open typed-decision models independently converge with Scott’s cheap-model-front-door and TypeSafe jev doctrine: a near-free structured decision call ahead of expensive agent work. The ind
10-08 23:33sensor_dirtycomment_update
10-08 21:37feedback_briefingScott vote via UI
10-08 21:34sensor_dirtyvelocity_spike
10-08 18:43attention_communicatedLiquid AI released two open-weight decision models Oct 7: d1-3B (text + vision, 48.57 on Decision Index 0.2.1, ahead of every 4B and 9B model) and d1-omni-600M (text + image or 30s audio), both return
10-08 18:43attention_routeSecond established vendor in a fast-consolidating category relevant to agent routing; the release facts plus the first negative benchmark belong together in the same read as the llama.cpp decision-end
10-08 18:37attention_routeSecond vendor in fast-consolidating one-pass decision category; directly relevant to Scott's jevkit/cheap-model-front-door work. Day-one adoption signals warrant briefing mention; evidence still
10-08 18:30attention_candidatematerial_reprice
10-08 18:30repriceFirst independent benchmark appeared (user tested d1 models vs embedding models for routing; embeddings won on their task, though classifier generalization noted). Day-one third-party ports (WebGPU, G
10-08 18:09attention_routeSecond vendor (after Intern‑Decision) in the fast‑consolidating one‑pass decision category; directly relevant to Scott's jevkit/cheap‑model‑front‑door work. Day‑one third‑party adoption warrants
10-08 18:01attention_candidateattach
10-08 18:01attachUser benchmarks Liquid AI's d1 decision models (D1-Omni-600M, D1-3B) against embedding models for routing, directly testing the case's claim about adoption as decision/routing components.
10-08 17:59propose_attachUser benchmarks Liquid AI's d1 decision models (D1-Omni-600M, D1-3B) against embedding models for routing, directly testing the case's claim about adoption as decision/routing components.
10-08 15:29sensor_dirtyvelocity_spike
10-08 12:22attention_routeThe editor compared this story and chose to keep watching.
10-08 10:21sensor_dirtycomment_update
10-08 10:21sensor_dirtycomment_update
10-08 07:22sensor_dirtyvelocity_spike
10-08 06:02attention_routeCompletes the decision-model picture he already holds from OpenAI's Decisions API and llama.cpp's decision endpoint: this is the open-weight, runs-anywhere version of the same category and t
10-08 05:56attention_candidatecreate
10-08 05:35createA distinct vendor release in the fast-consolidating one-pass-decision-model category — second vendor after Intern-Decision — already showing day-one third-party adoption (WebGPU browser port, GGUFs, m