2026-10-14 (9:00 AM ET, Hackread, single outlet): FLock.io unveils an edge decision model built on Qwen — the writeup names Qwen3.5-2B and claims sub-31ms local inference for fast, low-cost offline classification with no cloud data egress (hackread.com). Development: the category gets an edge/on-device entrant — small-weight Qwen running locally pairs the decisions-not-text wedge with the on-device privacy frame (the same no-egress pitch Gemma × GovTech carries). Single-source; model sizing and the "System One" attribution unverified — read the primary before quoting. Watch: independent latency/quality comparison vs Jev's $0.042/M-input economics, whether FLock's model distributes through agent/harness plumbing.
2026-10-13 (~1:00 PM ET, Crypto Briefing — from the ai digest): Artificial Analysis launches a
Decision Index — new decision models test faster and cheaper than LLMs, less accurate on
knowledge. The index layer arriving into the thread's standing gap (an independent run on
identical labeled work beside the vendor tables — the runner's Oct 3-5 premise). Sourcing
flag: topline-only in the reporting. Watch: the published rows vs TypeSafe/Cloudflare tables, and
whether it becomes a runner-of-record read for the field.
cryptobriefing.com
2026-10-11 (marktechpost): vLLM Semantic Router ships Decision 3.0 — five open multimodal
decision models, 0.8B–27B, d3 scoring 64.1 (marktechpost.com/2026
new-open-decision-models/). The category's open-weights tier appears, at sizes that make local deployment trivial. No shared
benchmark vs Jev/pplx (the runner's gold-set binaries measure a different task shape). Watch: runner-format
viability (the format:json lesson — Qwen3.5:9b failed 10/10 on response parsing), an independent 64.1 check, the 0.8B tier at
the embedded decision-point role.
October 2026 · 3 updates
2026-10-09→10Microsoft ships Microsoft-Decision-1 (Nadella X,
~2:37 PM ET Oct 9; Register Oct 10): fast
decision-making model, "top performance on structured decision
tasks, outperforming both LLMs and other decision models in latency
and quality," in testing across Microsoft (incident response,
quality control, scientific discovery); first version based on
Qwen3.5-9B (the GLM-Flash-in-Codex rhyme — a US lab shipping the
category on a Chinese open base), homegrown v2 promised; live in
Foundry, "soon through OpenRouter." X layer from Perplexity the
same window: decider-v1.1 on OpenRouter (multimodal, output free),
DecisionBench #1 claims (94.5% acc/1,071 cases, $0.017/1k
decisions, 534ms median — vendor-run), EVE inference-stack
demos ("4× faster than JEV"; provenance unclear — new component,
unvetted), a clinical claim (643/669), and pplx-embed-v2-late
(9B/0.6B open multi-vector embeddings, shared text/image space).
The distribution map is now two-way (Foundry/OpenRouter vs
OpenRouter), the runner field has grown to Jev vs d1 vs OpenAI
Decisions API vs pplx-decider vs Clef vs Decision-1 (plus
AnyJev/Strands/Laya) — and the head-to-head on identical workloads
still doesn't exist. Watch: Foundry metrics at second source, the
OpenRouter slot, someone finally running the comparison.
2026-10-07→09 three deltas. (1) TypeSafe closes $870M at
$7.5B (SiliconANGLE Oct 9): the category's first mega-round, an
order of magnitude past the eval vendor's round — capital now
prices decision models ahead of the independent runner that still
doesn't exist. (2) Liquid opens d1's weights — d1-3B +
d1-omni-600M, multimodal, "zero output tokens" (HF blog Oct 7:
"Multimodal open d1 decision models for the edge"; marktechpost
syndication): the open-weights flanks gain a first-party lab
entrant with weights. (3) OpenAI's Decisions API enters public
beta, "10× faster typed answers" at $0.10/1M input (marktechpost
Oct 9 — single syndicated source, unbenchmarked claim). Runner
question unchanged: Jev vs AnyJev vs d1 vs Decisions API vs
pplx-decider vs Clef — still no independent run.
2026-10-05 (first third-party domain eval — and an entrant
identified): Cisco's blog ran the domain comparison, in content
safety: decision models (zero-shot) vs a 1B encoder fine-tuned on
the Cisco AI Security Framework taxonomy (24 harm categories, six
datasets, model-generated reference labels): the fine-tuned model
wins — best AUC on 5/6, best recall at a 0.5% FP budget on all six
(Cisco multi-turn 18k: 61% vs Jev 34%, Laya almost none); Jev
"clearly the stronger" zero-shot; Laya had "no useful operating
point within budget on any dataset." Two resolutions: Laya =
open-weight, from ConvAI Innovations (the mystery entrant flagged
Oct 3 in Cloudflare's table), and the category's pitch meets
evidence — decision models are a labeling-free tier below purpose-
trained classifiers, not a replacement. Caveats: Cisco trained on
its own taxonomy, labels are LLM-generated — a domain benchmark,
not the independent runner. Watch: TypeSafe/Cloudflare answers;
workloads where zero-shot actually wins.
September 2026 · 2 updates
2026-09-29 (evening): a fast-follower answers the watch — Liquid
AI shipped d1 (marktechpost, single outlet): "a
decision model that returns calibrated probabilities with zero
output tokens" — one call for classification/routing/scoring. The
category's second named vendor, and the first from an established
lab (Liquid's edge-architecture line, not a TypeSafe bootstrap).
Same caveats: no independent evals, no runner for Jev vs AnyJev
vs d1 — the testable set just grew by one.
2026-09-23 the category's openness axis inverts — Nokia
open-sourced AnyJev (marktechpost, single outlet, flag): a
training-free layer that "turns any open LLM into a calibrated
decision model," claiming 6.8× automatable-traffic lift. Same
window: GLiClass (Knowledgator's open-source classifier,
RT'd: "open-source Jev, before Jev existed") landed in OpenClaw
via ONNX — local lightweight decisions instead of full LLM calls.
Reading: Jev's closed-API scarcity is being attacked from both
ends (a telecom giant's training-free layer + an OSS predecessor
getting harness-native distribution). The moat to watch: if open
calibration layers replicate the API's function at zero cost, the
decision-model category's pricing power evaporates. Independent
comparisons (Jev vs AnyJev vs GLiClass on routing) now exist as a
testable question — still nobody has run them.
Background
The thread: the "System-One" model category — AI returning
calibrated decisions (app state + typed query in, no text
generation) instead of prose, pitched at automation economics
(hallucination and cost cuts).
Watch for: independent evals of decision-mode vs text-gen on
classification/routing workloads, pricing, whether agent plumbing
(routing, tool calls) is the real wedge, fast-follower entrants.