● Covered by 3 sources · 7 reportsMedium impact4 neutral2 positive
Laya, an open-source non-autoregressive AI model, released as alternative to Jev
Key points
- Laya is an open-source, non-autoregressive AI model.
- It offers schema-based decision-making using bidirectional encoders.
- Laya runs in 32.8 ms on a single GPU, faster than Jev.
- It supports over 100 languages and has Apache 2.0 weights.
- Jev is TypeSafe AI's first "System One" model.
- Jev is designed for programmatic statement evaluation and decision-making.
- Diogo Almeida, ex-OpenAI engineer, co-founded TypeSafe AI and created Jev.
- Jev is 194x faster and 445x cheaper than frontier AI models like GPT-6 Astra.
- Jev uses Reinforcement Learning for Calibrated Decisions (RLCD) for training.
- TypeSafe emerged from stealth after two years.
- TypeSafe secured $40 million in seed funding led by DCVC.
- Jev is a text-only AI model.
- Jev provides structured, probabilistic decisions.
- TypeSafe built a new architecture and sampler for Jev.
- Diogo Almeida co-invented RLHF and InstructGPT at OpenAI.
- A curation site for Jev launched, offering reviewed projects and code examples.
- The Jev curation site includes 33 reviewed picks.
- Jev was adopted faster than any other model in AI Gateway history, according to Vercel.
- JevBench is a new benchmark for typed decision models.
- JevBench evaluates models based on accuracy, latency, and cost.
- JevBench allows configurable weighting of accuracy, latency, and price.
- A full JevBench run asks 534 English decisions.
- JevBench v1.3 score combines chance-corrected Intelligence, Calibration, Speed, and Cost.
- Jev scored 74.4 on JevBench.
- SemIf scored 73.1 on JevBench.
- djeV scored 73.0 on JevBench.
- Winnow-12B Q8 scored 71.2 on JevBench.
- reflex 4B scored 70.3 on JevBench.
- JevBench has a MIT harness, public items, frozen artifacts, scoring code, and public per-task outcomes.
- JevBench has two no-signup demos.
- JevBench is English-only.
- JevBench latency is measured from one German server.
- JevBench applies a disclosed x2 adjustment (+150 ms) for local/demo latency.
Laya's Release and Purpose
Laya, a new open-source AI model, has been released as an alternative to TypeSafe AI's Jev. Laya is designed for lightning-fast probability predictions over structured schemas, distinguishing itself from traditional autoregressive and generative text models. It aims to provide an open and efficient solution for System 1 reflex decisions in AI pipelines.
Technical Specifications and Performance
Laya is built on bidirectional encoders, allowing it to achieve inference times of 32.8 milliseconds on a single GPU, or 7.2 milliseconds per question when batched. This performance is stated to be 6 to 8 times faster than Jev. Laya also supports over 100 languages and is released with 100% open-source Apache 2.0 weights, eliminating API subscription costs.
Background and Comparison to Jev
The developer of Laya previously published research on non-autoregressive, reinforcement learning-guided schema-based decision systems in March and October 2025. TypeSafe AI, founded by Diogo Almeida, later launched Jev in September 2026, proposing a similar non-autoregressive decision concept. Jev uses RLCD (Reinforcement Learning for Calibrated Decisions) for confidence distributions and schema choices, charging $0.042 per million input tokens with typical response times around 150 ms. Unlike Laya, Jev launched without public technical papers, open weights, or open training datasets.
Architectural Approach
Laya addresses architectural limitations of earlier models by focusing on a completely open, horizontal System 1 decision model family. The core idea behind Laya, and similar models, is to move away from using generative LLMs for simple reflex decisions, which are identified as a bottleneck in modern AI pipelines.
Updates
- JevBench is a new benchmark for typed decision models.
- JevBench evaluates models based on accuracy, latency, and cost.
- JevBench allows configurable weighting of accuracy, latency, and price.
- A full JevBench run asks 534 English decisions.
- JevBench v1.3 score combines chance-corrected Intelligence, Calibration, Speed, and Cost.
- Jev scored 74.4 on JevBench.
- SemIf scored 73.1 on JevBench.
- djeV scored 73.0 on JevBench.
- Winnow-12B Q8 scored 71.2 on JevBench.
- reflex 4B scored 70.3 on JevBench.
- JevBench has a MIT harness, public items, frozen artifacts, scoring code, and public per-task outcomes.
- JevBench has two no-signup demos.
- JevBench is English-only.
- JevBench latency is measured from one German server.
- JevBench applies a disclosed x2 adjustment (+150 ms) for local/demo latency.
- A curation site for Jev launched, offering reviewed projects and code examples.
- The Jev curation site includes 33 reviewed picks.
- Jev was adopted faster than any other model in AI Gateway history, according to Vercel.
- TypeSafe emerged from stealth after two years.
- TypeSafe secured $40 million in seed funding led by DCVC.
- Jev is a text-only AI model.
- Jev provides structured, probabilistic decisions.
- TypeSafe built a new architecture and sampler for Jev.
- Diogo Almeida co-invented RLHF and InstructGPT at OpenAI.
- Jev is TypeSafe AI's first "System One" model.
- Jev is designed for programmatic statement evaluation and decision-making.
- Diogo Almeida, ex-OpenAI engineer, co-founded TypeSafe AI and created Jev.
- Jev is 194x faster and 445x cheaper than frontier AI models like GPT-6 Astra.
- Jev uses Reinforcement Learning for Calibrated Decisions (RLCD) for training.
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Primary sources
GitHub NandhaKishorM/laya arXiv 2503.23303 arXiv 2510.01237 PyPI laya GitHub fstandhartinger/jevbench GitHub TheoLeeCJ/openjevHow outlets covered it
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TypeSafe emerged from stealth with $40 million in seed funding to launch Jev, a text-only AI model designed for machines to make decisions within software applications. Jev offers structured, probabilistic decisions, contrasting with sequential LLMs that TypeSafe claims are inefficient for computer use.
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Laya, an open-source, non-autoregressive AI model, has been released as a faster alternative to TypeSafe AI's Jev. Laya offers schema-based decision-making with faster inference times and no API costs, contrasting with Jev's proprietary model.