Jev directory
Jev for Routing
764 curated Jev resources. Send each request, ticket, or task to the right branch, team, model, or tool.
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All 764 Jev AI model resources for Routing
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Fortune reports Jev went viral among developers, with nearly 13% of Vercel's paid teams trying it within a day of its gateway arrival. Three weeks after Jev's launch, OpenAI rolled out a competing Decisions API built on GPT-6 Luna.
Jev Media & TalksArticle#community#press#competition
Community
Same-day reaction to Jev from four engineers: Mario Zechner called it basically a general purpose classifier, Thorsten Ball praised the onboarding, Malte Ubl said it reached AI Gateway, and Armin Ronacher expects fast followers. Wes Bos measured $0.000232 a call and there were no evals yet.
Jev Media & TalksArticle#community#press#adoption
Community
A BIG CHANGE analysis of the Diogo Almeida Latent Space interview: Jev's most consequential proposition is the unit of automation, a bounded judgment inside an existing process, and the launch claim that Jev can't hallucinate needs a narrow reading because schema matching does not establish truth.
Jev Guides & ArticlesArticle#community#press#explainer
Community
Simon Willison released llm-typesafe 0.1a0, an LLM plugin for TypeSafe's Jev: install with llm install llm-typesafe, set a typesafe API key, then ask noul yes/no questions, Choice questions and Score questions from the command line. A demo refund question returned noul 0.99.
Jev Media & TalksArticle#community#press#explainer
Community
VentureBeat's Sam Witteveen argues a large share of enterprise AI only needs a label, not generated text. Per TypeSafe, Jev returns choices, scores and yes/no answers in 70 to 500 ms at $0.042 per million input tokens, with output tokens free.
Jev Guides & ArticlesArticle#community#press#explainer
Community
TNW reports Jev reached nearly 13% of Vercel's paid teams within 24 hours, the AI Gateway's fastest adoption: twice the GPT-5.6 family's share and more than 6x Fable 5.1's, at $0.042 per million input tokens with output unmetered.
Jev Media & TalksArticle#community#press#adoption
Community
Vega's proof of concept put Jev in front of its alert triage agent on two tenants: it gated off 15% of alerts on the busy tenant and a third on the quiet one, about 98% correct by three LLM judges verified by its own analysts.
Jev Guides & ArticlesArticle#community#press#adoption
Community
DigitalToday's Korea report relays The Information's September 24 story: TypesafeAI is seeking $1 billion, with some unnamed investors proposing valuations above $10 billion, while the talks remain early and terms could change.
Jev Media & TalksArticle#community#press#funding
Community
INXEN's syndication of Financial Times reporting says TypeSafe, last valued at $200 million, is fielding offers that would value it at $10 billion or more days after launching Jev, with Vercel citing record paid-developer interest.
Jev Media & TalksArticle#community#press#funding
Community
Langfuse's notebook integration traces TypeSafe Jev System One calls through the OpenInference instrumentor, which patches the Python client in place and emits OpenTelemetry spans.
Jev Guides & ArticlesArticle#community#press#explainer
Community
LangChain's September 25 post argues Jev's typed decisions fit LangGraph's deterministic orchestration, with a litigation review example and a Stagehand browser case where act() latency fell to 0.46 seconds.
Jev Media & TalksArticle#community#press#explainer
Community
A community roundup of every public Jev evaluation: TypeSafe's own workflow evals plus at least twenty independent builder benchmarks. The site reports speed medians of 127-422 ms per call leading every LLM compared, while accuracy splits by task.
Jev Media & TalksArticle#community#press#calibration
Community
AICrier's news-feed summary of Celeris-1 Decision: a multimodal diffusion model for bounded structured decisions that Celeris says reaches 80% on JevBench, 7 points above Jev 1.13.0, with 67 ms median latency.
Jev Media & TalksArticle#community#press#competition
Community
An episode of The Merge (by CodeRabbit) recorded at the first Jev hackathon 11 days after launch; TypeSafe DevRel Allie Laabs explains how Jev replaces prompt-parse cycles with typed choice, score and yes/no outputs.
Jev Guides & ArticlesArticle#community#press#podcast
Community
John Lindquist joins Claire Vo to walk through eight Jev use cases, from a real-time voice to-do app to data deduplication with confidence scores, on the episode billed as Jev week at How I AI.
Jev Guides & ArticlesArticle#community#press#podcast
Community
A pointer-network decision model from Keyush Nisar that reads one agent state document plus typed questions and returns calibrated tool-routing probabilities in a single forward pass, reporting 89.00% zero-shot tool routing.
Jev Models & Reimplementations#community#open-models#local
Community
A DistilBERT prototype from akumar0205 that sorts historical email into legitimate and spam, reporting 0.987 accuracy and 0.983 spam F1 on a held-out SpamAssassin split; it is not a phishing or malware detector.
Jev Models & Reimplementations#community#open-models#local
Community
A small distilled sibling of Perplexity's pplx-decider-v1.2: a Qwen3.5-4B decider that reads a state plus typed choice, noul and score questions in one pass, keeping most of the 27B model's Decision Index.
Jev Models & Reimplementations#community#open-models#multimodal
Community
mradermacher's static GGUF quants of Omartificial-Intelligence-Space/ara-jev-Faisal, an Arabic intent, policy and routing classifier; Q4_K_S and Q4_K_M are marked fast and recommended, under a non-commercial CC-BY-NC-4.0 licence.
Jev Models & Reimplementations#community#open-models#local
Community
A CPU-first decision model from Decision Models that answers the seven questions an LLM auto-router needs (category, difficulty, stakes, four flags) in one ONNX pass; MIT weights plus a Jev-compatible hosted API.
Jev Models & Reimplementations#community#open-models#local
Community
Perplexity's 27B decision model rebuilt on Qwen3.8-27B with a joint request format that answers a whole request's questions in one sequence, reporting 68.09 on Decision Index 0.3 and 77.43 on vision.
Jev Models & Reimplementations#community#open-models#multimodal
Community
TurinTech's independent GGUF conversion of the 27B Kev decision model for llama.cpp: the default IQ4_XS file is 13.91 GB and keeps 97.8% of the BF16 original's answers over 2,000 test decisions, with zero changed confident answers.
Jev Models & Reimplementations#community#open-models#local
Community
TurinTech's independent GGUF conversion of Jared Palmer's Kev-4B decision model; the default Q4_K_M file is 2.54 GB and returns the same answer as the BF16 original on 93.0% of 2,000 test decisions with no confident-answer changes.
Jev Models & Reimplementations#community#open-models#quantized
Community
TurinTech's independent GGUF conversion of Jared Palmer's Kev-9B decision model; the default IQ3_M file is 4.78 GB and matches the BF16 original on 92.2% of 2,000 test decisions with a minus 1.2-point accuracy change.
Jev Models & Reimplementations#community#open-models#quantized
Community
Torchcast AI's 4B text decision model on Qwen3.5-4B returns yes/no probabilities, choice distributions and ordinal score distributions through a TypeSafe-compatible API, reading option letters in one forward pass under CC BY-NC 4.0.
Jev Models & Reimplementations#community#open-models#typed-decisions
Community
StartLux's 9B Instant decision model as GGUF for llama.cpp: Q8_0 (9.53 GB) measures DecisionPivot 53.2 against 50.6 for Q4_K_M and 53.2 for BF16 on vLLM, returning one-forward-pass typed-question probabilities.
Jev Models & Reimplementations#community#open-models#quantized
Community
The first v1 checkpoint of the Strands decider line from the Strands Agents team: a LoRA adapter and readout head on Qwen3.5-2B-Base that answers typed decisions with calibrated confidence, reporting 0.7792 JevBench public accuracy (180/231).
Jev Models & Reimplementations#community#open-models#local
Community
A Strands decider v1 checkpoint: a LoRA adapter and readout head on google/gemma-4-E4B-it that answers typed decisions with calibrated confidence; its model-index reports 0.8095 JevBench public accuracy (187/231) and mixed code-bug evaluation scores.
Jev Models & Reimplementations#community#open-models#local
Community
A Strands decider v1 checkpoint: a LoRA adapter and readout head on google/gemma-4-E2B-it that answers typed decisions with calibrated confidence; its model-index reports 0.7792 JevBench public accuracy (180/231) and code-bug evals from 0.281 to 0.978.
Jev Models & Reimplementations#community#open-models#local
Community
A 2B decision model from Strands Agents for agentic workflows: a LoRA adapter on Qwen/Qwen3.5-2B-Base with a readout head that answers noul, choice and score questions with calibrated confidence, reporting 0.7619 on JevBench at 4096 context.
Jev Models & Reimplementations#community#open-models#local
Community
A 27B decision model from torchcast-ai that answers choice, noul and score questions together in one forward pass over the TypeSafe-compatible /v1/systemone format; the card reports Decision Index 0.2.1 of 65.00, +7.09 over Jev 1.13, at 33 ms per yes/no question.
Jev Models & Reimplementations#community#open-models#local
Community
Torchcast AI's 12B decision checkpoint: a LoRA fine-tune of google/gemma-4-12B-it that answers noul, choice and score questions in one forward pass, with a card-reported, author-run 203/231 (87.88%) on the JevBench v1.4 public items.
Jev Models & Reimplementations#community#open-models#local
Community
The Ollama build of iapp's OpenThai-SystemOne: a 0.8B Thai and English decision model whose v0.3 weights are fine-tuned for Ollama's /v1/systemone letter readout, reporting a 74.3 macro across 13 public subsets for Q8_0.
Jev Models & Reimplementations#community#open-models#local
Community
The 9B entry in vLLM Semantic Router's Decision 2.0 family: a 7.94B decision model with a 16,384-token context that answers choice, yes/no and score questions in one pass, reporting 68.1 JevArena and 18.4 ms median single-question latency.
Jev Models & Reimplementations#community#open-models#local
Community
The 4B model in vLLM Semantic Router's Decision 2.0 family: a 4.21B fine-tune of Qwen3.5-4B-Base that answers choice, yes/no and score questions in one forward pass, with a self-reported JevArena 63.6 and a 12.9 ms median per request.
Jev Models & Reimplementations#community#open-models#local
Community
A 27B decision model from StartLux Labs that takes text, JSON or images up to 262,144 tokens and returns a probability per option through the TypeSafe /v1/systemone format, reporting Decision Index 0.2.1 63.88 and 208 of 231 on JevBench public.
Jev Models & Reimplementations#community#open-models#local
Community
The Q8_0 8-bit GGUF build of StartLux-Decision-v1-27B, 28.60 GB for llama.cpp, served through a decision wrapper that the card measures at 100% same decisions as the original and 209 of 231 on JevBench public.
Jev Models & Reimplementations#community#open-models#quantized
Community
The Q8_0 8-bit GGUF build of StartLux-Decision-v1-9B, 9.53 GB for llama.cpp, with the card reporting 100.0% same decisions as the original weights and 200 of 231 correct (86.6%) on JevBench public.
Jev Models & Reimplementations#community#open-models#quantized
Community
The 0.6B model in vLLM Semantic Router's Decision 2.0 family: a 0.60B fine-tune of Qwen3-0.6B-Base with an 8,192-token context that returns probabilities for choice, yes/no and score questions from one forward pass.
Jev Models & Reimplementations#community#open-models#local
Community
The 2B model in vLLM Semantic Router's Decision 2.0 family: a 1.88B fine-tune of the earlier Decision-1.0-Sol-2B checkpoint with a 16,384-token context, reporting JevArena 52.1 and a 7.2 ms median per request.
Jev Models & Reimplementations#community#open-models#local
Community
The 0.8B model in vLLM Semantic Router's Decision 2.0 family: a 0.75B fine-tune of Decision-1.0-Eos-0.8B with a 16,384-token context, reporting JevArena 53.9 and a 6.0 ms median per single-question request.
Jev Models & Reimplementations#community#open-models#local
Community
Fastino's 287M multilingual classification model in the GLiNER2.5 family: pass any label set at call time and get labels back in a single forward pass, with no prompt template and no generated tokens. It averages 56.7% exact-match on the Fast Decisions suite.
Jev Models & Reimplementations#community#open-models#local
Community
Jared Palmer's 27B Kev decision model: a full-weight fine-tune of Qwen3.8-27B averaged with v1, plus a pointer head that returns calibrated probabilities for typed choice, score and noul questions in one forward pass. Card-reported transfer-v4 accuracy is 88.87%.
Jev Models & Reimplementations#community#open-models#local
Community
Fluid Inference's fixed-shape Core ML conversion of the classification path of Fastino's GLiNER2.5-Decide for Apple silicon: the DeBERTa-v3-large encoder and shared label classifier score up to four decision heads per call, in fp16, 8-bit or 6-bit packages.
Jev Models & Reimplementations#community#open-models#local
Community
Fastino's 1B English member of the GLiNER2.5 classification family: pass any label set at call time for intent, routing, sentiment or multi-label tags and get labels back in a single forward pass with no generated tokens. It averages 59.6% exact-match on Fast Decisions.
Jev Models & Reimplementations#community#open-models#local
Community
Fastino's 340M English classification model and the headline member of the GLiNER2.5 family: pass any label set at call time and get intent, routing, sentiment or multi-label tags back in one forward pass, at 60.2% exact-match average on the Fast Decisions suite.
Jev Models & Reimplementations#community#open-models#local
Community
Mapika's 4B decider: a Qwen3.5-4B-Base fine-tune that returns probabilities over supplied options for typed questions in one forward pass, with no decoding. Version 2.1 adds a rank-64 LoRA stage and reports a JevBench public hard tier of 0.649.
Jev Models & Reimplementations#community#open-models#local
Community
Nox-4B, the 4B model in vLLM Semantic Router's Decision 1.0 family: give it a state, questions and possible answers, and it returns typed decisions and probabilities with labels defined at runtime. The card reports 73.09% weighted accuracy across 3,766 decisions.
Jev Models & Reimplementations#community#open-models#local
Community
Start here with the Jev AI model
Featured entries for routing.
The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features: composable images on both models, the box question type on 1.14 only, and opt-in 262k context.
Jev Guides & ArticlesArticle#official#release#vision
Official
The Hugging Face hub for Laya's open System 1 checkpoints: the 421M English model at the repo root, a 322M multilingual build in a subfolder, and the typed-decisions fine-tune, all Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
Autonomous agent platform with 187,504 stars for building and running agents from plain English or a visual builder. Its platform features first-class TypeSafe Jev decision blocks for typed routing, filtering, scoring and confidence-gated next-action dispatching.
Jev Integrations & Adapters#community#autonomous-agents#platform
Community
Significant-Gravitas/AutoGPTPython
Significant-Gravitas/AutoGPTPythonJev AI model articles
Original guides that go deeper on Jev routing.
Voice agents need decisions in milliseconds. How turn detection, intent, and escalation work in real time, and where a fast decision layer fits.
Jev Practices & Patterns#voice#realtime#agents
Read article
Synthetic labels can train a decision model, but they can also bake in a teacher's errors. How to generate and audit them.
Jev Practices & Patterns#synthetic-data#training#distillation
Read article
Model deprecations and price changes are operational risks. How to pin versions, read contracts, and plan a migration before you need one.
Jev Guides & Articles#operations#vendor-risk#sla
Read article
From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.
YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.
YouTube: Retriever AI tests Jev on real browser tasks: where it worked, where it struggled, and what comes next.
Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.
Show HN: Verifying robot picks with a decision modelRead the thread on Hacker NewsHacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.
ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker NewsHacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.
vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker News