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NVIDIA Just Open-Sourced a 100M-Parameter Model That Knows Who's Talking

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Ever read a meeting transcript where every line is correct but you can't tell who said what? NVIDIA just fixed that — and gave the fix away for free. On September 23, NVIDIA released Nemotron 3 Diarization , an open-weight, 100-million-parameter model that answers one of audio AI's most annoying questions: "who spoke when," in live or recorded audio. It handles up to 8 simultaneous speakers with speaker-activity probabilities at 10-millisecond resolution — granular enough to catch rapid-fire crosstalk and interruptions. Streaming latency profiles go as low as about 320 ms, and there's an offline mode for full recordings. The model ranked #1 on VoiceArena's Diarization-Bench leaderboard with a 14.72% diarization error rate. It was trained on roughly 10,000 hours of real conversations plus 82,611 hours of simulated multi-talker mixtures. Under the hood is a neat engineering choice: a single-pass end-to-end design NVIDIA calls the "Arrival-Order Speake...
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Meet Jev: The AI Model That Makes Decisions Instead of Writing Text

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Jev: the AI model that makes decisions






TypeSafe AI logo — the company behind the Jev AI model



A New Kind of AI Model
On September 21, 2026, AI startup TypeSafe AI opened up its new model Jev to everyone — no waitlist, with access starting at $5 in credits. But Jev is not another chatbot. It is a new kind of AI model that does not generate text at all. It makes decisions.


What Is Jev?
Jev is a transformer-based model that outputs probabilities — what TypeSafe AI calls calibrated decisions. Instead of writing paragraphs, it answers the small questions AI agents constantly face: which tool should I call next, should I retry this task, which option is most likely to work. And it answers in under half a second. One of the people behind it is Diogo Almeida, a researcher whose work enabled the instruction-following ability that made ChatGPT possible.


The Price Angle
Here is where it gets interesting for anyone running AI agents. Jev costs $0.042 per million input tokens, and output is completely free. Five dollars in credits buys roughly 120 million tokens. For companies running fleets of AI agents that burn through expensive language-model calls, swapping the decision-making layer to Jev could seriously cut costs. Model gateway platforms like Vercel, Cloudflare, LangChain, and Langfuse have already added it to their stacks.


Why It Matters
Most recent AI progress has been about bigger models writing better text. Jev bets on something different — a decision-making layer sitting underneath the growing ecosystem of AI agents. If agents are the future of software, someone has to power the thousands of tiny judgment calls each agent makes every day. Jev wants to be that layer. It is early, and whether developers actually rebuild their agents around it is still an open question. But the pedigree is real, the pricing is aggressive, and the agent cost wars have officially started.

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