Microsoft leans on open weight model from Chinese AI lab to challenge Jev

Microsoft has joined the Jev fan club, an accidental group of companies that share a common desire to be recognized for their own decision models. Jev, announced by TypeSafe AI three weeks ago, is a large language model (LLM) tuned to respond to certain types of questions with a limited range of responses, rated by probability. Due to its speed, relative affordability, and response constraints, it's well-suited for a variety of business applications where open-ended text of uncertain accuracy might be undesirable. One of the selling points of Jev is that decision models don't hallucinate in the way that standard LLMs do. But decision models can make errors and their popularity has already prompted researchers to explore how those errors might be magnified. Nonetheless, they have their uses. The attention lavished on Jev prompted other companies to declare that they too have decision models to offer, even though machine learning researchers have long been able to create classifier models for probability-based decisions. OpenAI said its Decisions API has entered public beta. Cloudflare chimed in with its Clef model. Strands trotted out Strands Decider 2B, "a small, open source, decision model." Liquid AI introduced d1. Perplexity launched its Decisions API. Snowflake talked up its own decision model. Then there's Surogate Rune and H2O.ai's H2O-Lightning-4B, a decision model built on Qwen3.5-4B. All told, more than 100 such models are now vying for attention. Now it's Microsoft's turn. "Decision models are quickly emerging as an important new category in AI," said Achint Srivastava, VP of software engineering in the Office of the CTO at Microsoft, in a blog post on Friday. "Unlike LLMs, which are designed to generate text or reason through complex problems, decision models are purpose-built to deliver structured outputs that software can immediately act on. And once you understand that capability – making decisions and classifying things at very low cost with high performance – all kinds of useful tasks get unlocked." Microsoft's entrant into the race is called Microsoft-Decision-1, which is offered via Microsoft Foundry and, soon, via OpenRouter. Redmond's decision model, like H2O's, is based on a Qwen model, Qwen3.5-9B in this instance. The Qwen model family is developed by Alibaba Cloud, the cloud computing arm of Chinese tech giant Alibaba. Microsoft, for reasons not disclosed, said it will soon rebase the Decision-1 on its models and those from OpenAI. The US software giant claims its model is 2.5x faster than H2O-Lightning-4B and 2.8x faster than Jev in its latency test, leads the pack in accuracy (83.5 percent) on 36 benchmarks, ranks second (behind Quyet-1.0-Large) in confidence score (92.2 percent), and is more than 20x cheaper than OpenAI's GPT-6 Sol in text classification tasks. Input tokens cost $0.042 per million tokens and output tokens are free. "Now that agentic AI is a reality, we’ve seen that cost plays a major role in how people decide to use AI," said Srivastava. "And it’s increasingly important to choose the right model for the right job." ®

source https://www.theregister.com/ai-and-ml/2026/10/10/microsoft-leans-on-open-weight-model-from-chinese-ai-lab-to-challenge-jev/5302473