ENPT

Jev

Last updated:

Beta70–500ms$0.042/M input

Quadrant scores

See the full quadrant

Scored with the public quadrant rubric (maturity × capability; bubble = adoption). Revise as evidence lands.

  • Maturity6/10

    Self-serve public beta: signups reopened on 27 Sep 2026, though new accounts get no free credits. Public docs, an official Python SDK and production integrations (LangChain, LiteLLM, OpenRouter, Vercel AI Gateway, Symfony). No public SLA.

  • Capability8.1/10

    Full choice / score / boolean. In-distribution calibration holds (DecisionEval ECE 0.045, Decision Index ECE 0.074), but third-party audits show confident probabilities on chance events and rare-event risk. Hosted p50 ~524–687 ms. DI 0.2.1 #1 (57.91).

  • Adoption73/100

    Broadest awareness in the category (HN 1,890 points, TechCrunch, Forbes) and first-party gateway integrations. Vercel's index: 35.3% of paid teams in 14 days; named OpenRouter apps (Portkey, Context7). Production use is real but small: 0.15% of gateway spend. No SLA.

Vendor claims

News covering this model

Jev is the first publicly available System One model from TypeSafe AI. It is not a chatbot and does not generate free text. You send program state plus a map of typed questions; it returns structured answers with calibrated probabilities — typically in 70–500ms (warm calls often cited near ~100–200ms).

The category name borrows Daniel Kahneman’s dual-process framing: System 1 is fast and automatic; System 2 is slow and deliberative. Frontier LLMs behave like System 2 (token-by-token generation). Jev is positioned as System 1 for software — classify, route, score, gate — and leave essays to an LLM when you actually need one.

“Everybody is arguing about which chat model ‘wins’ 2026. Wrong fight. Jev is not trying to write you a prettier paragraph.” — @juliench82

Specs

Attribute Value
Company TypeSafe AI (San Francisco)
Founder Diogo Almeida (ex-OpenAI; RLHF / InstructGPT lineage)
Co-founders Eric Gafni, Stasa Shishik (reported in launch coverage)
Funding $40M seed led by DCVC ($200M valuation in some reports)
Launch
Status Public beta, self-serve: signups open since 27 Sep 2026 (new accounts get no free credits)
Current model jev-latest = jev-preview = jev-1.14.0 (TypeSafe models docs, checked 27 Oct 2026; no preview build right now). jev-1.13.0 is still callable by name.
Latency 70–500ms
Pricing $0.042/M input tokens; output free
Training story RLCD — Reinforcement Learning for Calibrated Decisions (vendor term)
Architecture Non-autoregressive; answers multiple questions in one parallel pass
I/O shape state + typed questions → typed answers + probabilities / confidence
Docs docs.typesafe.ai

Access update (27 Sep 2026, 19:30 BRT). TypeSafe announced “Jev is back. Capacity has increased and signups are open!”, with a caveat: “new signups no longer get free credits”. Diogo Almeida said free credits for new users are paused because “a few bad actors were making it a bad time for everyone”. Jev is still beta (no GA, no public SLA), but anyone can now sign up at console.typesafe.ai.

How a call works

Unlike chat APIs (messages → streamed tokens), Jev’s contract is decision-shaped:

  1. Provide context / state (text or structured payload your code already has).
  2. Attach one or more typed questions (choice, score, or yes/no / boolean — docs also discuss related shapes such as Noul).
  3. Receive structured answers your code can branch on — no “almost-JSON”, no narrator preamble.

That is why early adopters describe it as removing the chatbot tax: you do not pay the model to narrate a decision you only needed as a branch condition.

Question types

Choice

Pick among labelled options; get a distribution over options.

Typical use: sentiment buckets, tool selection, ticket category, “which agent should handle this?”

Score

Numeric score on a rubric with a confidence measure.

Typical use: urgency 1–10, lead quality, relevance ranking.

Boolean / yes–no

Probability that a proposition holds.

Typical use: spam?, PII present?, evidence supports claim?, allow tool call?

Why “System One” — and Dual-Process stacks

Kahneman’s Thinking, Fast and Slow is the marketing and architecture metaphor: Jev as the fast path; an LLM as the slow path when ambiguity or generation is required.

In the wild (Sept 2026), builders already ship Dual-Process patterns:

  • System 1 (Jev): cheap gate / classify / route in milliseconds.
  • System 2 (Gemini, Claude, GPT, …): fire only for deep reasoning or text generation.

Open examples include safety gates and Discord bots that keep the frontier model off the hot path until Jev says the request needs it (@taro_president).

Why it cannot “hallucinate” (and what that does not mean)

The vendor claim rests on restricted output: Jev only chooses among provided options / schema slots and never invents free text, so it cannot emit a novel false fact as prose.

It can still be wrong about which option is correct. Restricted output ≠ perfect judgment. Treat confidence as a routing signal (auto-handle vs escalate), not as proof.

Jev 1.14 and image input: images anywhere in a request, behind the “danger zone” (14 Oct 2026)

The standout is image input: pictures can now go anywhere in a System One request: in state, in a question’s instructions, or as the criteria it is judged against, and every question type accepts them. Plainly stated: TypeSafe says jev-1.13.0 already supported images (“multimodal all along”), so this is a documented opt-in capability, not a new model, and it sits in the danger zone (not production-ready; formats and limits may change; all models do worse on images than text).

TypeSafe’s post “Jev: For the builders AND dreamers” (14 Oct 2026) and the updated Models docs add the following. Read the next box before the rest.

Claim vs evidence. The only numbers in the post are TypeSafe’s own internal chart (“antibench”), run by TypeSafe on its own private validation set, on its own model, and stamped “Warning: AI benchmarks — please don’t trust them”. It is a UnverifiedClaim. As of 14 Oct jev-1.14.0 was not yet on JevBench, the Decision Index 0.3 board (Jev 1.13 is there at 60.11) or the Artificial Analysis Decision Index; since then JevBench and AA have listed it (see the 17 Oct section below), and neither confirms a step up.

Item What the post and docs say
Images (standout) Composable images in state, instructions or criteria via an assets map (png/jpg, base64 or multipart), header TypeSafe-Danger-Zone-Images: true. TypeSafe says jev-1.13.0 was “multimodal all along”, so both 1.13 and 1.14 accept images. All models do worse on images than on text.
jev-1.14.0 New release under the alias jev-preview. “Small, subtle improvements to everything”, largest gain expected in multilingual. jev-latest stays on jev-1.13.0 and moves to 1.14.0 “in a week” (about 21 Oct). Price unchanged: $42 per billion tokens ($0.042/M), output free.
Deprecation 60 days’ notice before jev-1.13.0 is formally deprecated, 30 days for jev-1.14.0. A deprecation policy is promised but not yet published.
box question type Returns one bounding box (four edges as fractions of the image) plus confidence. Jev 1.14 only (jev-preview or jev-1.14.0).
Long context Header TypeSafe-Danger-Zone-Long-Context: true raises per-request and per-question context to 262k tokens on 1.13 and 1.14, same price per token. Default stays 64k per request / 32k per question. TypeSafe says it is slower and less accurate.
Opt-in data sharing Organisation-level “Help improve TypeSafe models” toggle lets TypeSafe read inputs and outputs to find jaggedness. The post says it still does not train on queries.

Danger-zone features are explicitly not production-ready (“dreamer-ready”): the request format, answers and limits can change between releases.

TypeSafe’s internal chart (UnverifiedClaim)

Model Intelligence (higher better) Calibration error (lower better)
jev 1.14.0 60.0 1.6
jev 1.13.0 58.9 1.8
kev-27b 46.7 17.8
pplx-decider-v1.1-27b 46.0 35.0
clef 44.3 11.2
clef-flash 38.8 19.1
kev-4b 36.9 10.3
d1-3b 33.8 19.9

Read it with care: the post says it includes “only open weight models”, yet Jev itself is closed, and it adds that the ranking is “likely biased towards our models”. The set is private, so nobody can reproduce it. The +1.1 Intelligence step from 1.13 to 1.14 has no confidence interval. Cost per million queries on the chart is about $120 for Jev, which is not comparable with Artificial Analysis’s $42 per million tasks (a different task mix). We did not copy any other rank across boards.

Known jaggedness (jev-1.13)

TypeSafe documents the sharp edges of jev-1.13 in Model jaggedness (reviewed 2026-09-17). Short version: it is fast and good at common-sense System One judgment, but literal, weak on math/numbers/dates, and worse when you add indirection, noisy state, or adversarial text. Many of these are expected to improve in later versions.

“jev-1.13 answers the question you wrote, not the one you meant. Scoping words, negations, and implied conditions are read at face value.”

Practical takeaways

Failure mode Do this instead
Literal reading Write the exact condition; put boundary cases in criteria
Math, counting, hex/RGB, score interpolation Keep arithmetic in code; ask semantic questions only
Date / time ordering Extract parts with Choice; compare in code
Indirection / multi-hop Flatten instructions; name the relevant state fields
Large irrelevant state Filter in code first (context rot is real)
Adversarial / injected framing Precise criteria; test edges before deploy
Contradictory instructions vs criteria Align them — treat criteria as part of the instruction
Assumed invariants (Noul vs Choice, P vs 1−P) Don’t transfer thresholds across question shapes
Generation / free extraction Use a generative model or regex; let Jev choose

Also avoid System Two–style tasks (extra layers of indirection), hiding several judgments in one question, and asking for anything code can compute exactly.

Primary source: docs.typesafe.ai — Jev 1.13 jaggedness.

Reception (first 72 hours)

Signal Detail
Hacker News Launch post ~1,890 points / ~495 comments
Press Coverage from The Register, SiliconANGLE, Forbes, Tech Brew, etc.
Naming “Jev” nods to economist William Stanley Jevons (Jevons paradox) in several explainers
Demand API demand spikes reported after launch; self-serve signups reopened on 27 Sep 2026 with more capacity

JevBench v1.4.2 (September 2026)

Metric Score Rank
Overall 63.29 #2
Intelligence 48.9 #1
Calibration 77.4 #3
Speed 88.2 #5
Cost 58.9 -

Jev dropped to #2 overall when Decider-4B shipped (64.13) — the first open model to beat the hosted reference. Jev still leads on Intelligence (the hardest axis to optimize), which aligns with TypeSafe’s stated focus on decision quality over raw throughput.

Independent evaluation: DecisionEval (typed-decisions)

DecisionEval ran Jev 1.13.0 itself on the frozen test split of LocalLLaMA/typed-decisions on 20 September 2026: 400 cases, 2,000 decisions, questions passed through verbatim. This is a third-party measurement, not a TypeSafe number. DecisionEval also sells custom benchmarking, so treat it as independent of TypeSafe rather than as a neutral standards body.

Metric Value 95% CI (bootstrap)
Accuracy 0.740 0.721–0.759
Brier (vs gold distribution) 0.148 0.139–0.156
ECE (10 bins) 0.045 0.037–0.066
Latency (client-side, 5 decisions per request) p50 687 ms / p95 777 ms —

By type: choice 0.738, score 0.705, noul 0.787. At a ≥0.9 confidence threshold, coverage drops to 24.5% and accuracy on what remains rises to 0.937.

Two notes from the same page. First, the gold labels come from a teacher-model ensemble: where the teachers agreed (1,188 decisions) Jev scored 0.841, where they disagreed (812) 0.591. Second, the ECE of 0.144 that other cards cite for Jev on this dataset (the Laya typed-decisions card, and open peers that copy it) did not reproduce: DecisionEval got 0.045, with Brier matching exactly at 0.148. DecisionEval suspects a different binning or ECE definition. Until someone publishes one, compare calibration claims against Jev with that gap in mind.

Source: decisioneval.dev/models/typesafe-jev, run of 20 Sep 2026, checked 26 Sep 2026.

Independent audits recomputed in Akita’s post (3–4 Oct 2026)

Fabio Akita’s post “Entendendo o Hype do Jev: pra que serve?” (3 Oct 2026, in Portuguese; English version) carries an essay by Paulo Câmara, CTO of LUA Vision. He recomputed four public third-party Jev studies from their raw data. He discloses that his company builds competing models. Akita says he did not run the Decision Index himself. We re-checked the four studies from the published raw files on 4 Oct 2026. These are third-party measurements, not TypeSafe numbers. Each one is a single dataset with one prompt design.

Study (raw data) Setup Result (recomputed) Limits
Fair draws (KantaHayashiAI) 1,000 Choice requests about a hidden fair die, coin or spinner Picked the first listed option in 1,000 of 1,000 cases, with stated probability 0.70–0.94 for events whose real chance is 1/6 to 1/2. Accuracy stays at chance (die 19%, coin 52%) Option order was fixed. A replication (2,000 requests) shows the wording matters too: “heads” and “cat” won even when listed second. In the first repo, asked as one yes/no per option, the outputs move toward the middle instead (about 15% for a 1-in-20 event)
ENEM 2025 (patryckalves) 182 valid questions from Brazil’s national exam, A–E, in Portuguese 103 of 182 (56.6%) vs 102 for Nemotron 3 Super 120B (exact McNemar p = 1.0). Languages 78%, humanities 73%, natural sciences 47%, math 25% (chance is 20%). All 39 answers above 0.9 were correct; ECE 0.078 One run of one exam. With an “invalid” option allowed, Jev abstained on 51% of questions and got 79% of the rest right
Heart-disease risk (rubinagentagi-tech) 5,000 respondents from the CDC BRFSS 2015 survey, base rate 9.0% Mean stated probability 27.4%. Brier 0.189 vs 0.082 for always answering 9% (Brier skill −1.32). AUC 0.77, so the ranking is usable Rare-event risk is outside Jev’s stated use; one prompt design
Claim verification (adorosario) 495 LLM-AggreFact claims, paired with GPT-6 Astra Balanced accuracy 73.3 vs 73.8 (p = 0.49) at about 187× lower cost. But it called unsupported claims “supported” 23.2% of the time vs 14.1% One English dataset

On the official Decision Index 0.2.1 board (snapshot 28 Sep), Jev leads with 57.91, 0.47 ahead of the best open entry (Surogate Rune 26B-A4B, 57.44). Its pooled ECE there is 0.074 over 72,594 decisions, and its hosted median latency is 524 ms. The essay also checks TypeSafe’s own workflow evals, which grade agreement with a frontier-model reference rather than a gold answer key. On the workflow average Jev agrees 67.8% of the time, the same as Sonnet 5 (Opus 5: 73.1%). On invoices it is 17 points behind the best model (61.8% vs 79.1%). According to the essay, the two headline multiples use different opponents: 444.6× is against Opus 5’s cost, 193.6× is against Sonnet 5’s time.

How to read this. When the input carries signal (ENEM language questions, answers above 0.9, the Decision Index), Jev’s probabilities track its accuracy. When there is no signal, or the task is outside its range (pure chance, rare-event risk), it stays confident anyway. Shuffle option order, add a “none of these” option, and recalibrate thresholds on your own labelled data before trusting the number. The essay also cites a recalibration repo that reports ECE 0.117 → 0.008 with 50–300 examples; we did not re-check that one.

Score update (4 Oct 2026). Capability 8.2 → 8.1. Two subscores move. Calibration goes from 8 to 7: the old 8 rested on one in-distribution run (DecisionEval, ECE 0.045), and several reproducible studies with public raw data now show confident wrong probabilities outside that range; the board’s own ECE is 0.074. Accuracy goes from 7 to 8: Jev is #1 on the public, verifiable Decision Index 0.2.1 board (57.91). Types and latency are unchanged.

Distribution (checked)

  • OpenRouter: typesafe/jev-1.13 — $0.042 / $0 per 1M tokens (in/out), 32K context (page checked 19 Sep 2026).
  • OpenRouter Jev Router (NEW): typesafe/jev-router — cache-aware model router that uses Jev to pick the best model for each request (see news).
  • Vercel AI Gateway: changelog id typesafe-ai/jev (see news).
  • Databricks Unity Gateway (16 Oct 2026): Jev 1.14.0, hosted by TypeSafe, behind the admin-enabled “Externally-hosted TypeSafe AI Models” preview and TypeSafe’s terms. Databricks’ release notes give no latency or price for this route; any figure should be read as an UnverifiedClaim (see news).
  • Atlassian Automation, “Classify with Rovo” (early access, announced 15 Oct 2026): a Jira/Confluence automation step with Check, Categorize and Score classifications that returns every option’s probability, an overall confidence and a single answer as smart values. Pricing not announced; Atlassian says it is evaluating other decision models too (see news). Draft, 19 Oct.

Vercel AI Gateway Production Index, October 2026 (checked 17 Oct). Vercel’s report covers anonymised paid-team traffic through September and ranks every September model launch with at least two weeks of data. For Jev (gateway launch listed as 17 Sep; the changelog above is dated 16 Sep):

Metric (first 14 days unless noted) Jev Context in the report
Reach (share of paid teams) 35.3% #1 launch of September; Claude Sonnet 5.5 second with 18.1%; “fastest adoption of any model in AI Gateway history”
Requests most of any model in September despite launching mid-month
Spend share 0.27% (0.15% of the month’s spend; 1% of tokens) Opus 5.5 took 11.03% of spend
Week-1 → week-2 return rate 55.6% 5th of the launch table; GPT-6 Luna 64.8%
Report’s launch scorecard 65.9 (6th) Sonnet 5.5 87.8

Read it as reach, not revenue: a decision model is called often with tiny requests, so it is tried by many teams and still a sliver of the bill. TechCrunch (16 Oct) adds that Vercel itself replaced GPT-6 Luna with Jev for classifying harmful commands, and cautions that a gateway is a limited view that undercounts labs with their own APIs. These are Vercel’s measurements, not ModelSystem.One’s. They do not move the capability score and are separate from the TypeSafe-internal charts quoted earlier on this page. See news.

Big-lab competition (29 Sep 2026). OpenAI announced the OpenAI Decisions API, which picks from developer-defined answers on top of GPT-6 Luna, in limited preview with no public docs or pricing. We list it under runtimes, not as a catalog peer, until OpenAI documents decision-specific weights and a typed API. See news.

First hosted challenger with its own model (29 Sep 2026). Liquid AI launched d1, a decision model on its own API that uses the same /v1/systemone shape and works with TypeSafe’s SDKs via base_url. Liquid claims d1 is “the first model to outperform Jev” on Hugging Face’s Decision Index: 58.9 vs 57.9 in an internal reproduction of Decision Index 0.2.1, ahead on arts, language and retrieval, behind on tools (−1.0) and knowledge (−8.0). UnverifiedClaim: the Jev numbers match the official Space, but d1 is not listed there. See news.

Local runtimes adopt the API (30 Sep 2026). Ollama 0.35 added a local /v1/systemone endpoint “based on TypeSafe’s Jev API”, serving open decision models such as Nimble and Tev1. The TypeSafe SDK works against it unchanged. See runtime and news.

Third-party guardrail and security tests (14–15 Oct 2026)

publicado (OK do Fabricio 18/10 BRT).

  • raxIT Labs, Decision Models as Guardrails v1.0.0 (15 Oct). Among 19 systems and 9,975 checks, Jev 1.14.0 scores 88.7 and Jev 1.13.0 88.5, against 90.4 for Perplexity Decisions, 90.0 for OpenAI Decisions and 79.0 for Amazon Bedrock Guardrails. Jev costs $0.039 per 1,000 checks, below Perplexity ($0.056), OpenAI ($0.110), Bedrock Guardrails ($0.113) and Clef ($0.202) (list prices of 7 Oct). These are raxIT’s numbers, as reported: part of the test set is private, and raxIT notes that the question format may favour Jev. See news.
  • Prompt-injection research (14–15 Oct). HiddenLayer steered an agent harness with an injected ad, and Datadog Security Labs cut Jev’s injection recall from 82.90% to 3.88% by prepending a counterfeit policy. Datadog measured flagging only, not downstream compromise. See news.

None of this changes Jev’s scores.

Ecosystem traction

Within days of launch, open-source and product repos started wiring Jev as a decision / router / judge layer rather than a chat model:

Project Role
Pydantic AI Merged and shipped: TypeSafeModel (#8450, 18 Sep) on a DecisionModel base (#8696), plus SystemOneModel for any /v1/systemone server (#8942, in v2.53.0). Status below
NVIDIA-NeMo/Switchyard type_safe_classifier routing
pipecat-ai/pipecat Judge / router / Flows service hooks
LangChain Official langchain-typesafe: TypeSafeClassifier, ModelRouterMiddleware, AutoModeMiddleware
LiteLLM Decisions API proxy for Jev and other providers, with /v1/decisions and /v1/systemone; also offers Jev context compaction
Community demos Browser automation (decide → act), email batch classification, NES/Mario “one choice per frame”, agent tool gates

LangChain and others published guides framing Jev inside agent loops as structured decision steps rather than another completion endpoint.

Context compaction (LiteLLM)

LiteLLM ships a TypeSafe compaction guardrail (blog, 18 Sep 2026): before the main model call, Jev scores older tool results for relevance to the latest user question. Results below a threshold (default 0.2) are replaced with a short removal notice; tool-call structure, system/user messages, and the latest assistant+tool exchange stay intact.

Classic example: weather lookup then shop hours — user asks when the shop closes → weather blob can drop, hours stay. Compaction is opt-in (guardrails: ["jev-compaction"] on the proxy / team); if Jev is down, default fail_open sends the original request. Jev is also reachable through LiteLLM’s /typesafe/v1/systemone pass-through.

Primary source: Reduce agent context with TypeSafe Jev and LiteLLM.

LiteLLM’s unified Decisions API (8 Oct 2026)

LiteLLM v1.104.2 added two proxy routes for typed decisions: /v1/decisions accepts OpenAI’s input + question-list format; /v1/systemone accepts state + named questions. Both routes share the configured provider list, so the caller can use Jev, Perplexity, Clef and other supported providers through one gateway, with the proxy’s usual keys, budgets, spend tracking and guardrails. Image input is supported only for OpenAI. The release notes list the initial providers; Microsoft Foundry and Databricks support landed later on main.

LiteLLM’s 10 October post also points to a Decisions tab in the Playground. The current documentation describes that UI, but says it is absent from v1.104.2 and v1.105.0. Check the installed build before relying on the tab. The integration adds a shared serving and testing path; it does not change Jev’s model scores.

Cache ≠ compaction. Compaction asks Jev which context to drop. Separately, jevcache (hyperspaceai/jevcache) memoizes the typed decision itself — fingerprint of (model, schema, state) so repeats hit a local ledger (or a shareable bundle) instead of re-calling the model. Not affiliated with TypeSafe; volume/savings claims on the landing page are UnverifiedClaim.

Pydantic AI status (checked 7 Oct 2026)

Official docs: TypeSafe models (typesafe:jev-latest), System One models (system-one: prefix, for Ollama, Laya, CLM and other /v1/systemone servers), decision models, and Pydantic’s article on watching Jev live in Logfire. Latest release: v2.54.0 (3 Oct).

Merged What it adds
#8450 (18 Sep) TypeSafeModel for Jev
#8486 (18 Sep) Docs correction: the page had overstated what Jev does
#8505 (18 Sep) typesafe_boolean_threshold for yes/no answers
#8488 (18 Sep) Docs: Jev in an agent loop (routing, judging tool calls)
#8696 (24 Sep) DecisionModel base; TypeSafeModel becomes one
#8739 (24 Sep) Opt-in decision_route_threshold
#8740 (25 Sep) Jev context_window from genai-prices; compaction docs
#8962 (28 Sep) typesafe extra for pydantic-clai2
#8942 (1 Oct) SystemOneModel for /v1/systemone servers (shipped in v2.53.0)

Still open: #8487 (str fields by picking a candidate), #8454 (Classifier evaluator), #8529 (StructuredJudge), #8759 (capabilities as routes), #9634 / #9646 (two competing OpenAIDecisionsModel PRs), #9669 (clai2 system_one plugin), #9848 (generic question when instructions is empty).

Community ecosystem (apps that call Jev)

Community roundup snapshot (@studio_yebisu, 18 Sep 2026; also r/WebAfterAI roundup, 17 Sep 2026). These are integrations and demos that use hosted Jev — not competing decision models. Star counts drift; treat as awareness, not ranking. We did not run every repo.

Also: classifier.dev — public HTTP/CLI/MCP wrapper over Jev (jev-1.13.0); fast = Jev, smart escalates uncertain cases to a reasoning LM.

Community index: awesomejev.com tracks hundreds of Jev-related repos/apps (snapshot ~561 entries / ~479 repos on 19 Sep 2026). Useful as a living directory — not a ModelSystem.One ranking, and not every entry is a catalog model. Our curated ecosystem table below stays selective.

Second community index: madewithjev.com lists 756 Jev projects in 21 use cases, 318 of them open-source GitHub repos (snapshot 4 Oct 2026), each with the cost and speed its author reported. Those numbers are self-reported, not benchmarks; same caveat as awesomejev.

Also adjacent (not TypeSafe, keep off the quadrant): CUA-S1-FORMS — Cua’s MIT form specialist (~706k params), inspired by jevlike; documented under docs/04 as research specialist, not a catalog peer of Jev.

Open logit-classifier stacks (not TypeSafe models — keep off the quadrant): simple-jev / simple-jev.featherless.ai (Featherless — any open LM → typed decisions via next-token logits; demo API + RFDT), SemIf (formerly OpenJev; browser demo openjev.com), litjev (Qwen wrapper; docs/04), LFM2.5-350M-RLCD (Liquid base + constrained parallel inference — name ≠ TypeSafe RLCD training; docs/04), openjev-sglang.

Browser / PC / mobile

Project Role
browser-use/jev-ultrafast Browser agent: Jev picks ops/elements; small LLM only for typed text
trycua/cua jev-use Hosted Jev → candidate action IDs → Cua Driver (validate + execute + re-observe)
awlevin/typesafe-computer-use macOS computer-use via OCR/accessibility + typed decisions
droidrun/mobile-jev Android phone control via Mobilerun
moritzkremb/jev-voice-browser Voice → intent → Playwright browser actions
lahfir/agent-desktop Rust desktop automation over OS accessibility trees; a recent optional jev-desktop skill lets Jev pick the operation and target each turn. Jev is an add-on: the project (Feb 2026) and its ~1.75k stars predate Jev

AI coding / review

Project Role
tamaratran/fast-jev-compaction Compact Claude Code history by dropping low-value tool noise
thruwire/foreman Supervise Codex agent runs (continue / verify / stop)
devagrawal09/jev-review Structured git/diff review with local dashboard
gargpratyush/jev-router Per-turn model routing for Claude Code / Codex
EliaAlberti/jev-rules Select which Claude Code rules/docs to inject
Asymptote-Labs/agent-beacon Cross-harness telemetry + review-gated agent memory; explicit memory evaluations run uses Jev noul probabilities before human approval. Traces may contain prompts, commands, or files; Jev and network flows are opt-in
allebee/jevgrep (PyPI jevgrep-cli) Semantic grep via Jev Noul per line (tail -f friendly); author benches vs Haiku/Sonnet = UnverifiedClaim
allebee/pytest-jev (PyPI) Pytest semantic assertions judged by Jev; cost comparisons in the README = UnverifiedClaim
Grep.ai AgentRun (announce) Enterprise workflow harness: frontier agent learns a job, then authors a DSL of code + typed Jev nodes (Route/Classify/Sift/Pick). Cost/latency case numbers and “first harness built around Jev” = UnverifiedClaim

MCP / skills / CLI

Project Role
kitze/skillbox Agent skill library + MCP; optional Jev skill recommend
itsmostafa/typesafe-mcp Unofficial MCP exposing Jev choice/score/boolean
jkudish/jev-mcp MCP tools: verify claims, screen injection/relevance, rank candidates
mrnugget/jev-shell-history zsh history suggest ranked by Jev
hyperspaceai/jevcache (jevcache.sh) Decision ledger / memoization CLI — cache typed answers; not context compaction
AkashPriyadarshii/jev-curate (PyPI) Rust/Python sifter for JSONL/Parquet training data: Choice/Score/Noul rubrics per row. The “24.0 rows/sec” is from a local mock bench (no Jev calls), and the README repeats vendor marketing ratios → UnverifiedClaim

SDKs / language bindings

Project Role
kieranklaassen/ruby_llm-typesafe TypeSafe provider for RubyLLM — structured decisions only (rejects chat/stream/tools)
symfony/ai-type-safe-platform (Packagist) Official TypeSafe platform bridge for Symfony AI (composer require; PHP ≥8.2; Packagist dev-main only as of 21 Sep 2026)

Search / databases

Project Role
realZachi/pg-jev PostgreSQL NL filter/classify/rank (rows leave to API)
superagents-lab/jev-search Web search pipeline: query + relevance via Jev
jexp/neo4jev Neo4j hop demo with Choice/Noul

Video / browsing

Project Role
kitze/unclutter Browser extension: hide ads/clutter via Jev
ChetasLua/jevmeter Score spoken lines in video (not fact-check)
trungdq88/youtube-sponsor-detection Detect/skip in-video sponsor segments

Guardrails / community

Project Role
DevMortimer/pi-warden Guardrails for Pi agents (loops, unverified “done”)
brainstormity/Jev-Moderation-Bot Discord spam/scam moderation
qkal/Canny Claude Code / Codex hooks against unverified “done” claims: deterministic checks can block, Jev is optional and advisory only (its answers become notes)

Marketing / GEO

Project Role
usenotra/notra GEO / AI-answer brand mention tracking (Jev for classify — verify in repo)

Smart home / sims / trading

Project Role
AboveColin/HA-Jev Home Assistant sensors/actions via Jev (not safety-critical)
fhshaik/typesafe-mario Mario emulator control from structured state
standardagents/jevpilot Three.js driving sim autopilot
RomanSlack/jev-drone Camera-only drone in a MuJoCo simulation, with Jev in the loop at 2.5 Hz for tactical choices (climb, brake, avoid); not a real drone
jarrodwatts/jev-trader Experimental trading bot (mock/dry-run defaults; PnL unverified)
virajbhartiya/laya-vs-jev Side-by-side T-Rex arena: local Laya (MLX) vs hosted Jev; planner + safety shields mean assisted scores ≠ raw model skill. MLX latency benches = UnverifiedClaim
emrickgarrett/OneVOneJev Browser 1v1 shooter vs Jev: Choice/Noul for move, aim, fire and jump at ~9 decisions/s. Toy demo; no license
irfndi/prism-liquidity-agent Solana (Meteora DLMM) liquidity agent trading real funds; Jev is an optional, fail-open soft consult (toxic flow, mean reversion) that cannot veto the strategy. Backtests are the author’s

Tools / apps

Project Role
monteduro/killmyidea Startup-idea scorer: eight Jev scores → KILL / FIX / SHIP, with raw probabilities shown. Toy app; no license

JevBench live board v1.7.46 and Jev 1.14 (Benchmark Heaven, checked 17 Oct 2026)

On the live API board (1,500-item full set, composite = harmonic mean of Intelligence, Calibration, Speed and Cost), Jev 1.14.0 scores 70.2 (#9): Intelligence 60, Calibration 90, Speed 91, Cost 55, capability 75.0, raw p50 0.22 s, est. $0.0318 per 1,000 decisions. Jev 1.13.0 scores 71.5 (#7; capability 77.1, p50 0.24 s). So on this board 1.14 is 1.3 composite points and 2.1 capability points below 1.13, a little cheaper and faster. On the same board’s Intelligence column, 1.14 is also lower (59.9 vs 63.6). The preview image-input builds are on ImageJevBench v0.3.0, a separate board: Jev 1.13.0 + images is #18 (capability 72.5) and Jev 1.14.0 + images is #20 (capability 60.3), while kettei-fat is #1 (75.95) and Sage 1.4 + images is #5 (75.85).

Artificial Analysis Decision Index v1.0 (checked 17 Oct). The AA page now lists both: Jev 1.13 57.5 at 127 ms per task and Jev 1.14 57.1 at 78 ms, so 1.14 is 0.4 points lower and about 39% faster there. The d1-ultra row (65.2) is the leader.

Maturity 6.0 and capability 8.1 unchanged; adoption re-scored 58 → 73 (below). Accuracy stays 8: independent boards still place Jev 1.13/1.14 in the JevBench top ten and 2nd–3rd on the AA index, and no rubric input moved enough to change a half-up rounding. The formula did not change, so nothing is recomputed. The 1.14 vs 1.13 gap is a model-version note, not a new rating. The three JevBench, AA and Decision Index 0.3 figures are separate boards and are never mixed.

Adoption re-score, 73 (was 58), 17 Oct 2026. The old 58 was recorded only in words (engagement “high”, ecosystem “medium-high”, production “medium”); we reconstruct it as engagement 85 / ecosystem 55 / production 30 (85 × 0.35 + 55 × 0.35 + 30 × 0.30 = 58.0) and re-score each signal on today’s evidence, using the docs/06 weights:

Signal (weight) Was Now Evidence
Mentions / engagement (35%) 85 90 HN 1,890 points, 410K+ YouTube views, TechCrunch and Forbes at launch, a 739K-view engineering post, and now a TechCrunch story (16 Oct) built on Vercel’s data
Ecosystem integrations (35%) 55 75 Vercel AI Gateway and OpenRouter listings, LiteLLM, LangChain, Symfony bridge, Ollama-compatible endpoint, tools such as Foreman and DevPass; Vercel’s own SDK support. Not higher: most are thin adapters and distribution is gateway-led
Reported production use (30%) 30 50 Vercel replaced GPT-6 Luna with Jev for classifying harmful commands (TechCrunch); DevPass Code 2.0 uses Jev by default for tier routing; named apps on the OpenRouter listing (Portkey, Context7, shapes.inc, extra.email); Vercel reports the most requests of any September model and 55.6% week-1 → week-2 return. Held at 50 because the same data shows 1% of tokens and 0.15% of spend, and a gateway undercounts direct-API labs

90 × 0.35 + 75 × 0.35 + 50 × 0.30 = 31.50 + 26.25 + 15.00 = 72.75 → 73. Maturity (6.0) and capability (8.1) are unchanged. The bubble gets bigger because production use is now measured, not because the model changed. What would move it again: spend share above a few percent, a named large customer, or a second independent usage source.

JevBench v1.6.1 (Benchmark Heaven, checked 7 Oct 2026)

On the v1.6 re-measure (open-weights board, not comparable with the v1.5.7 numbers below), Jev 1.13.0 is the unranked reference row: composite 71.5, capability 77.1 (Intelligence 63.6, Calibration 90.6, raw p50 0.24 s, $0.032 per 1,000 decisions). On the separate API board it is #3 on composite, behind Sage (74.0) and Liquid d1 (73.0), and #2 on capability behind Sage (78.6). The open-weights leader is Quyet-1.0-Large (71.4 / capability 81.7). On Hugging Face’s new Decision Index 0.3 (6 Oct), Jev scores 60.11 in the rank-2 tie group, behind pplx-decider v1.1 (62.75). See news. No catalog score change (accuracy stays 8: verifiable public boards).

JevBench v1.5.7 (earlier edition, checked 5 Oct 2026; not comparable with v1.6)

On the live JevBench v1.5.7 board (111 systems), Jev 1.13.0 scores 72.1 official (#3), behind Cygnet 73.7 and Winnow-12B Q8 73.2. On the board’s separate capability headline it leads at 80.0. Numbers read from the page; see news. No score change on this catalog page yet.

Fit / anti-fit

Fit when you need: classification, routing, scoring, semantic gates, guardrails, shadow-mode checks, branching in automations — anything where the product output is a typed decision.

Anti-fit when you need: explanations, drafts, multi-step tool-using chat, open-ended research, or any workflow that is the essay.

Architecture (independent reconstruction)

Speculative black-box reconstruction from Archer Hume — Jev’s Architecture Unmasked (17 Sep 2026). Not a TypeSafe confirmation. Numbers below are only those reported in that essay or by open reconstructions; treat MoE and exact head design as inferred.

Independent probing of the TypeSafe API (latency vs context, question isolation, option-set effects) plus prior art suggests a causal transformer reused for decisions: shared-state encoding, isolated question branches, and a direct probability readout instead of autoregressive text.

  1. Numeric readout, not AR decode — probabilities come from a head on hidden states (slot or pointer-style), not from generated “91%” tokens. Billing output_tokens in the essay is treated as accounting, not evidence of decode steps.
  2. Shared state + isolated questions — encode the state once (KV / prefix); each question is a branch that can read state but not sibling questions. Moving a secret from a sibling question into state changes visibility in probes.
  3. Causal backbone — likely a pretrained decoder LM with decision post-training (vendor: RLCD). Bidirectional encoder is not ruled out from the outside but is a worse fit for shared-prefix serving.
  4. Listwise options before choice — options appear to interact before the distribution is formed (irrelevant extra option shifts odds between existing ones; order sensitivity). Fits final-position slot head or pointer scoring over option states.
  5. Train the distribution; derive confidence — proper scoring on outcomes (log / Brier-style), then arithmetic confidence from the distribution (e.g. distance from uniform) — not a second “confidence LLM”.
  6. Sparse MoE — least certain — inferred for prefill-only compute; not measured. Essay flags this explicitly as speculation.
  7. Branches as a batch, not a chat — questions are independent work items over shared state; no answer→next-question decode chain inside one request.

Open reconstructions / peers: Kev (LoRA on Qwen2.5-0.5B), Laya (ModernBERT-large), NanoJev (Qwen3-0.6B + decision heads), jevlike (research option-attention starter). None are TypeSafe products.

Artificial Analysis Decision Index v1.0 (13 Oct 2026)

Per the AA launch article, Jev 1.13 leads at 58 (GLiDE and Microsoft-Decision-1 54). HLE-MultiChoice 21 (others ≤15); AA-Omniscience accuracy 35 but non-hallucination 35 (2nd); When2Call 81 (4th); WildGuardTest 94. $42 per 1M tasks (on the cost Pareto frontier), 0.13 s median per task. Claude Haiku 5.5 (Low) scores 61 but is 11× slower and 4.4× the cost. Scores unchanged: accuracy was already 8. AA is a separate, independent board (four evaluations: AA-Omniscience-Decision, HLE-MultiChoice, When2Call, WildGuardTest). Its scores and ranks are not comparable with Decision Index 0.3 or JevBench. AA finds every decision model overconfident overall, mainly on the knowledge and reasoning tasks; most are fairly well calibrated on When2Call and WildGuardTest.