Jev is a lightweight decision model from TypeSafe AI, built with input from a ChatGPT co-inventor and released in limited form on September 15, 2026; it constrains outputs to run directly and is claimed not to hallucinate. Recent coverage includes Vercel data showing 35% of paid teams adopted it within two weeks, a roughly $870 million Andreessen Horowitz-led round at a $7.5 billion valuation, Cloudflare's Clef and Clairic competing models, open releases such as Decision 3.0 and Vega, and a study finding embeddings plus simple classifiers beat Jev when labels are available.
Study: embeddings plus simple classifiers beat Jev modelA Voyage AI employee reported that using an embedding model plus a simple sklearn classifier was cheaper and significantly more accurate than the Jev decision model when labeled data is available, cutting error by 37%. The author noted the gain comes from training labels that Jev does not use, so zero-shot cases may differ.
Developer launches picture quiz game built on Jev vision modelDeveloper Jose Sabater built Jev Cards, a picture quiz game around TypeSafe's new vision model Jev, which reads an image and answers in about a quarter of a second. The game offers solo leaderboards and live multiplayer, and its code is open source.
Cloudflare previews Clairic extraction modelCloudflare released a preview of Clairic, hosted on Workers AI as @cf/cloudflare/clairic, claiming higher accuracy, lower latency and lower cost than traditional LLMs for extracting, estimating and forecasting values from unstructured inputs. It is a System One extraction model, a category inspired by TypeSafe's Jev, and open weights are planned soon.
Developer releases open-source model Herbert, seeks feedbackA developer launched a live demo on Hugging Face Spaces for a model called Herbert, claiming it outperforms Jev, and asked for feedback on bugs and improvements. The post gave no details on model size, benchmarks, or training.
Vercel data: Jev is fastest-adopted AI model everVercel's AI gateway data shows Jev, a lightweight decision model from TypeSafe AI, was used by 35% of paid teams within two weeks of launch, nearly double the reach of second-place Sonnet 5.5 — the fastest two-week adoption Vercel has recorded. Released in limited form on September 15, Jev constrains outputs to run cheaper and faster for classification, prompting rival offerings from OpenAI and Amazon. Open source models now account for over 70% of tokens but under 25% of spend on the gateway.
Decision 3.0 open multimodal decision models releasedThe vLLM Semantic Router team released Decision 3.0 (d3), a family of decision models from 0.6B to 27B under Apache-2.0 that read text, images, and video. It ranks #1 on both Jev Decision Index boards.
Community asks: what are local Jev-like decision models used for?A LocalLLaMA user asks what practical use cases exist for running a Jev-like decision model locally. They currently use it only for deep eval testing, calling it somewhat a "solution in search of a problem," and want to learn what others do with such models.
Open-source Vega: 800M Physics-based Decision Model with 73k Context and Image SupportA developer open-sourced Vega, a physics-based typed decision model with only 800M parameters (a 4B version also exists), 73k context and image support, surpassing many Jev benchmarks in single-shot tests. It uses an engine+adapter test-time training architecture where outcomes are modeled as valleys and a friction-affected ball settles on the best result.
Andreessen Horowitz backs Jev maker at $7.5 billion valueTypeSafe AI, maker of the Jev model, raised about $870 million in a round led by Andreessen Horowitz at a $7.5 billion valuation. Jev went viral weeks after launch, surpassed one million users in days, and is now used by about a third of Fortune 500 companies, the startup said.
Musubi launches open-weight decision model PolicyLM-1.7BMusubi released PolicyLM-1.7B, a 1.7B-parameter open-weight decision model that applies plain-English content policies to messages in under 50 milliseconds without retraining when policies change. It targets real-time platform moderation at classifier-like cost and speed while retaining LLM flexibility.
Jev review: not frontier but still usefulTypeSafe AI's Jev, a reasoning model from a ChatGPT co-inventor, claims it cannot hallucinate. Testing 16,379 benchmark requests for latency and billing found it smaller and humbler, yet genuinely useful for a niche nobody else serves well.
Cloudflare's Clef model removes humans from AI agent loopCloudflare launched Clef and Clef-flash, challenging TypeSafe AI's Jev decision model. Clef-flash delivers classifications in about 39 milliseconds, over ten times faster than Jev. Both models are built on Apache 2.0-licensed Qwen and let AI agents make structured decisions without generating text.
OpenAI Decisions API Confidence UnreliableTests show GPT-6 Luna, behind OpenAI's new Decisions API, is poorly calibrated: when it claimed 99% confidence across 3,600 reasoning problems, it was right only 68% of the time, highlighting AI confidence-calibration problems.
OpenAI's Decisions API: a Jev clone for fast agent decisionsAt Dev Day, Sam Altman introduced OpenAI's new Decisions API, which gives the Luna model a predefined set of choices for tasks like image classification or agent behavior, making it very fast while retaining image understanding and safety. The product resembles Jev, released this month by TypeSafe AI, which developers use to augment LLMs more cheaply and quickly. OpenAI has released it only as a limited preview.
Jeeves: Reasoning Improves Jev-Like Decision ModelsA developer released Jeeves, a Jev-like decision model built on Qwen3.5-9B with LoRA, a pointer head, CISPO training, and a block-4 diffusion drafter. It scores 0.889 on unseen test data versus 0.822 for Kev-9B and 0.857 for Jev, and 0.935 on JevBench's public hard tier; latency on one H100 is about 0.3s without thinking and 3.3s median with it, with code, weights, and data open-sourced.
Raschka Traces Evolution of Text Classification ModelsSebastian Raschka published a visual guide tracing text classification from bag-of-words through RNNs, CNNs, Transformers and Jev-like APIs to calibration, arguing the recently released Jev is essentially a classifier that is faster and cheaper than general LLMs yet more general than task-specific models; the article is not an endorsement.
Home-Trained 0.8B Decision Model Jeff Runs in ~30msThe independent open project Jeff releases 0.8B and 2B zero-shot decision models fine-tuned from Qwen3.5 and Gemma 4, returning calibrated probabilities in a single forward pass at about 22 ms on an RTX PRO 6000 and 28 ms on an M4 Max, trained only on local hardware with synthetic data and released on Hugging Face.
Turning GLM-5.3-Flash into a Jev-like decision modelA study crafts input prompts so the first output token answers the question, enabling standard LLMs such as GLM-5.3-Flash to make decisions in a single forward pass and reproduce Jev-like properties on vLLM. Benchmarks show accuracy and speed on par with Jev and substantially better than Laya, though cost per decision is several times higher than Jev; the setup also supports vision inputs.
Open-source Kev 4B tested side by side with JevA side-by-side test of Kev 4B (an Apache-2.0 Qwen3.5-4B fine-tune) and Jev on 362 fresh items found accuracy within 2 points on every task. Jev led on PAWS paraphrase detection (87.0% vs 74.5%) but counts ~257 extra input tokens per request, making short requests up to 12x costlier.
JevMap your AI worldview by answering a few questions and compare with others.
Developer has Jev play Pokémon Red, pushing AI to a more complex gameA developer open-sourced a project that lets Jev play Pokémon Red in real time, streaming tokens and costs with the goal of earning all badges. The project aims to test AI decision-making in a more complex game than Tetris.
Jev, an AI Model That Can't Chat, Goes ViralJev, an AI model built by a former OpenAI researcher, went viral with about 40 million views on X. Its maker, San Francisco startup TypeSafe AI, has drawn investor offers valuing it above $10 billion.
Stanford and NVIDIA Release CLM-8B Contrastive Language ModelStanford and NVIDIA introduce Contrastive Language Models (CLMs) and release CLM-8B, which matches Jev's performance on computer-use, gaming, and tool-calling tasks while achieving up to 9× lower latency. With fine-tuning, it sets new SOTA on DeepSWE (81.6%) and Terminal Bench 2.1 (87.6%), and the authors establish power-law scaling laws for contrastive loss.
Skepticism Over Jev: Old Tech Marketed as New AI ParadigmA critique argues Jev is not a new class of decision model: its zero-shot classification behavior already exists in NLI classifiers, embedding models and rerankers, and it cannot generate autoregressively or emit invalid classes. Marketing comparisons against LLMs tout speed and cost but lack benchmarking on suites like BTZSC; in one Banking77 test, BGE-small plus logistic regression scored 93.3% versus Jev's 83.2%. The '0% hallucination' claim only guarantees outputs match the allowed schema, not correctness.
LocalLLaMA users urge mods to curb Jev adsA r/LocalLLaMA user asked moderators to address a flood of promotional posts for Jev, calling it a paid product that uses venture funding to shill across the subreddit and others.
Jev in 25 lines of PythonA r/LocalLLaMA user shared a project called Jev implemented in just 25 lines of Python, with a link to the code repository.
Analysis: OpenAI may fast-follow TypeSafe's Jev modelAn Arcturus Labs piece argues TypeSafe's Jev model uses an approach close to conventional LLMs, so OpenAI is positioned to fast-follow and fold the capability into its models and agents. The key question is whether TypeSafe's training data and processes form a moat.
JevBench: a reproducible benchmark for typed decision modelsA developer launched JevBench, a reproducible benchmark for typed decision models that return bounded choices and probabilities, covering 534 English decisions and scoring accuracy, latency and cost with configurable weights. Jev leads at 74.4, followed by SemIf, djev, Winnow-12B Q8 and reflex 4B.
TypeSafe AI Unveils Jev Decision ModelsTypeSafe AI unveiled Jev, a new 'System One' decision model that takes text input and returns probabilistic numeric outputs instead of text. It charges only for input at $0.042 per million tokens, undercutting OpenAI's GPT-5 Nano at $0.05.
Jev calibration benchmark: LLMs beat humans yet know when they're rightJev Benchmarks published calibration results: its training method is called Reinforcement Learning for Calibrated Decisions, yet its calibration gap trails Gemini 3.8 Flash, DeepSeek V4.1 Flash and GLM-5.3. Jev holds 95% accuracy and decides alone on 86% of yes/no items, more than those models, showing worse calibration but better awareness of when it is right.
The A.I. Daily