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Vercel reported that its Jev decision model, released in limited form on September 15 by TypeSafe AI, reached 35% of paid teams within two weeks — nearly double the reach of the next most popular model, Sonnet 5.5. The data, drawn from Vercel's AI gateway, also shows open source models now handle over 70% of tokens processed but only about 25% of total spend, while frontier-lab spending has grown tenfold.
TypeSafe AI, maker of the Jev model, has closed an $870 million funding round at a $7.5 billion valuation led by Andreessen Horowitz, with Sequoia and existing backer DCVC also participating. Jev launched on Sept. 15 and, per the company, is already used by a third of Fortune 500 firms. Unlike large language models, Jev uses a transformer architecture to output probabilities or 'calibrated decisions' rather than text, which TypeSafe says makes it faster and more token-efficient for automation tasks.
Strands Labs has launched Strands Decider 2B, a small open-source 'decision model' built for fast local experimentation within its strands-labs initiative. Unlike general-purpose LLMs, it is designed to choose among predefined options or assign numerical scores rather than generate free-form text, and it also outputs a reliability score for each decision. The model is intended to integrate with the Strands Harness SDK and the recently launched Strands harness.
Musubi unveiled PolicyLM-1.7B, a lightweight decision model built to moderate content in under 50 milliseconds using plain-English policies, and released it with open weights. The company says it matches the cost and speed of existing moderation classifiers but can adapt to new rules without retraining, unlike conventional systems.
TypeSafe AI released an AI model called Jev about three weeks ago. The model has already prompted other developers to create copycat versions and has stirred discussion in Silicon Valley about alternatives to large language models.
At OpenAI's Dev Day, CEO Sam Altman introduced a new 'Decisions API' that lets developers give the company's Luna model a predefined set of options to choose from, such as image categories or agent behaviors, returning fast, low-cost outputs. The tool appears similar to Jev, a classifier model released earlier this month by TypeSafe AI for software automation tasks. OpenAI's API is currently in limited preview, and TypeSafe did not respond to requests for comment on the similarities.
TypeSafe AI has released Jev, a model designed to convert natural-language input and application state directly into typed decisions, returning choices, scores and probabilities as JSON. The company says Jev uses a new architecture, a parallel sampler, and a training method called Reinforcement Learning for Calibrated Decisions to generate probabilities without token-by-token generation. TypeSafe reports significant speed and cost improvements over general-purpose LLMs for decision-making workflows.
Andrew Boyd, founder of Standard Agents Inc., says his Jev decision model beat Pokémon Red's Elite Four and Champion, reaching the Hall of Fame on September 23, 2026, less than a week after the run began. Jev isn't a chatbot or LLM; it picks from lists of weighted options rather than reading the screen, while Anthropic's Claude Opus 5 monitored the game log and adjusted those options to help it when stuck. A harness changelog logged 474 fixes, including repeated failures like walking into a shut door 53 times and looping a ladder 124 times.
TypeSafe's Jev, a rapidly adopted AI classification model, has become the fastest-growing model in Vercel's AI Gateway history. A commentator argues OpenAI could quickly replicate Jev's core approach and bundle it directly into its own models and agents, since OpenAI's LLMs already function as implicit classifiers that could be retrained for this purpose.
A Show HN post introduces JevBench, a benchmark that measures typed decision models by cost per 1,000 decisions rather than per token, using actual token counts from 534 v1.2 test decisions. The methodology prices systems with public tariffs at their listed per-token rates, while unlisted open-weight models are priced using OpenRouter or DeepInfra hosting rates for the same or comparable weights, explicitly avoiding raw GPU rental costs. Reported figures include Jev 1.13.0 at $0.0399 per 1,000 decisions, SemIf at roughly $0.022, and Winnow-12B Q8 at roughly $0.037.
TypeSafe AI has released Jev, its first 'System One' model designed not for conversation but for evaluating statements and making structured decisions for use in software systems. Built by former OpenAI researcher Diogo Almeida, the model claims to be up to 194x faster and 445x cheaper than frontier LLMs like GPT-6 Astra by processing parallel yes/no style queries with confidence scores instead of generating open-ended text.
A new joke npm package called jev-leftpad reimplements string left-padding by calling an AI model (Jev, via TypeSafe's SDK) to choose how many spaces to add, rather than using JavaScript's built-in padStart() method. It requires an API key, Node.js 20+, and only supports padding up to 10 spaces since the AI is given choices labeled space_0 through space_10. The author explicitly warns against using it in production or for anything important.