SubQ Snap

SubQ Snap is a System One model from Subquadratic, a research company that builds long-context models on its own sparse attention method. The first release came out on 12 October 2026 and SubQ Snap 1.1 on 22 October. You send text, structured data or up to four images with a set of typed questions, and it returns a probability for each outcome. It is a hosted API with closed weights, in public preview.

The endpoint is POST https://api.subq.ai/v1/systemone, with subq-snap-1.1 as the model. The request has Jev’s state and named questions. A Noul returns the probability of yes. A Choice returns a probability per option and a confidence. A Score returns a probability per level and the probability-weighted level. SubQ’s docs do not say whether TypeSafe’s SDKs work against it, and they list no SubQ SDK, so the examples call the endpoint with fetch or requests.

Its main difference is context. One request can hold 384K tokens, against 64,000 for Jev, so a whole contract or a long agent history fits in one call. Subquadratic says Snap answers a 100,000-token request in about a second. That is a vendor figure. Subquadratic’s examples include picking files and tools inside coding agents, a form of intent and model routing, and support inbox triage. On the Artificial Analysis Decision Index, checked on 23 October 2026, SubQ Snap 1.1 scores 65.0. That is second of 17 decision models, 0.25 behind d1-ultra and just ahead of Elephant 9B at 64.7. Subquadratic’s site calls it tied for first. Its non-hallucination rate on AA’s knowledge questions is 85.7, second of the 17, so it often answers “I don’t know” rather than guess. Its When2Call tool-calling accuracy is 94.6%, also second. Its calibration error, which this site computed from AA’s published bins, is 0.082. AA measured $30 per million tasks and a median of 190 ms per task. Benchmark Heaven’s JevBench measured the first release on 13 October 2026 on its hosted API board: a Capability Score of 81.5, against 75.0 for Jev 1.14. It has not yet measured 1.1. Subquadratic’s own runs give 1.1 a chance-corrected 65.7 on the Jev Decision Index 0.2.1 public suite, against 60.6 for Jev 1.14, and 72.6 against 50.7 on its own long-context test. These are vendor-run. The multimodalart Jev Decision Index has no row for SubQ Snap.

It writes no text, so it cannot explain an answer. Its accuracy on AA’s knowledge questions is low, at 20.2%, so it is a poor fit for questions that depend on recalled facts. Subquadratic’s own tables put it 22 points behind Jev 1.14 on PhishNChips phishing detection, which it blames on its default threshold. The rate limit is not published.

Sign up at console.subq.ai, buy credit, and call the API with Authorization: Bearer. It is in public preview, which SubQ says is open to every account and can change before general availability. Inputs and outputs are deleted within 60 days, and zero data retention is available.

System One Score97.7 from 2 of 3 boardsArtificial Analysis Decision Index 99.7, JevBench 95.7. How the score works Question types Choice Score Noul Max Choice options255 Score levelsUp to 10 Questions per call128 Total context393,216 tokens State budgetNot documented Rate limitNot published as of 23 October 2026. Going over the limit returns HTTP 429. EndpointPOST https://api.subq.ai/v1/systemone LicenceNot published as of 23 Oct 2026 SDKs The docs give a 384K-token (393,216) context window for the whole request; a request over the limit, or close to it, fails with 400. A request takes 1 to 128 questions, a Choice 2 to 255 options and a Score 2 to 10 levels. Up to 4 PNG, JPEG or WebP images go in an images array as plain base64, at most 4 MiB each and 8 MiB in total, and each uses about 64 to 256 tokens. The body can be up to 13 MiB. Video is not supported.

subq-snap-1.1, 22 Oct 2026, The current model and the default decision model in SubQ's docs, in public preview. Released with the launch post of 22 October 2026 at $0.04 per million input tokens. Closed weights. Release notes subq-snap-preview, 12 Oct 2026, The first SubQ Snap, launched in public preview on 12 October 2026 at $0.05 per million input tokens, with a waitlist once capacity ran out. SubQ's models docs list only subq-snap-1.1 as of 23 October 2026. Release notes

What people use SubQ Snap for, one page per pattern. Workflow controlIntermediate

One Jev call reads an incoming request and returns its intent as a label plus a difficulty rating on a scale you wrote. Your router reads both numbers and picks the handler: deterministic code, a cheap model, an expensive one, or a human queue. Choice Score Workflow controlStarter

Send a support ticket to Jev once with every question attached. Category comes back as a selected label, severity and frustration as numbers on scales you wrote, refund intent as a probability. Your code reads those values and decides what happens to the ticket. Choice Score Noul

Published scores, each linked to its source. A row marked vendor-run was measured by the vendor itself, so read it as one side's result. The compare hub sets these beside the other models. BenchmarkMetricScoreRun byDate Artificial Analysis Decision Index 1.0Index score65 scoreIndependent23 Oct 2026 Artificial Analysis Decision Index 1.0Expected calibration error0.0819 ECEIndependent23 Oct 2026 Artificial Analysis Decision Index 1.0Cost per 1M tasks30 USDIndependent23 Oct 2026 Artificial Analysis Decision Index 1.0Time per task190 msIndependent23 Oct 2026 Decision LCR 1.1 (Subquadratic)Accuracy72.6 %vendor-run22 Oct 2026 Jev Decision Index 0.2.1 public suite (Subquadratic run)Skill score65.7 scorevendor-run22 Oct 2026 JevBench 1.7.47Capability Score81.5 scoreIndependent13 Oct 2026 Artificial Analysis Decision Index, Index score: Row: SubQ Snap 1.1, hosted by Subquadratic, 2nd of 17 decision models as of 2026-10-23, 0.25 behind d1-ultra. Sub-scores: Omniscience accuracy 20.2, non-hallucination 85.7, HLE-MultiChoice 17.5, When2Call 94.6, WildGuardTest F1 94.9. The row was missing from one copy of the page fetched on 2026-10-23 and back in the next. Artificial Analysis Decision Index, Expected calibration error: Row: SubQ Snap 1.1. Computed by this site from AA's published calibration bins (all answers pooled); AA does not publish an ECE. Lower is better. Artificial Analysis Decision Index, Cost per 1M tasks: Row: SubQ Snap 1.1. AA's measured cost at list prices on its four evals. Artificial Analysis Decision Index, Time per task: Row: SubQ Snap 1.1. Median time per task. Decision LCR 1.1 (Subquadratic), Accuracy: Subquadratic's long-context decision test, Snap 1.1 reading each request in one pass against Jev 1.14 at 50.7 with the input split into chunks. Vendor-run. Jev Decision Index 0.2.1 public suite (Subquadratic run), Skill score: Run by Subquadratic on SubQ Snap 1.1, chance-corrected. It gives Jev 1.14 60.6 and the first SubQ Snap 61.8. The 12 October launch post gave the first release 61.7 against 57.9 for Jev. JevBench, Capability Score: Row: SubQ Snap (preview), model subq-snap-preview, measured 2026-10-13 on the first release, not 1.1. Intelligence 72.3, Calibration 90.7. Fourth by Capability Score on the hosted API board. VendorSubquadratic TypeCommercial, hosted API StatusEarly access Announced12 Oct 2026 AccessOpened 12 Oct 2026 I

01Introducing SubQ Snap 1.1 (Subquadratic, 22 October 2026), checked 2026-10-23 subq.ai 02Introducing SubQ Snap (Subquadratic, 12 October 2026), checked 2026-10-23 subq.ai 03SubQ home page, Snap pricing and benchmarks, checked 2026-10-23 subq.ai 04Decisions API (SubQ docs), checked 2026-10-23 docs.subq.ai 05Models (SubQ docs), checked 2026-10-23 docs.subq.ai 06Data retention (SubQ docs), checked 2026-10-23 docs.subq.ai 07Artificial Analysis Decision Index v1.0, checked 2026-10-23 artificialanalysis.ai 08JevBench by Benchmark Heaven, board v1.7.47 (hosted API board), checked 2026-10-23 benchmarkheaven.com