This issue is about decision models, a new kind of AI model that does not write text. You give it an input and a fixed list of answers, and it returns the best answer with a probability, in milliseconds. TypeSafe AI released the first one, Jev, on September 15. On Wednesday Google released its own, after OpenAI and Microsoft did in October, and all three charge $0.10 or less per million input tokens and nothing for output. Shanghai AI Lab says its 4B model beat Jev on its tests, and Microsoft built its model on Alibaba's Qwen. Chatbots still score a little higher on the hardest decisions, at 15 times the cost. In the middle: on Vercel's gateway, decision models took one in four AI requests in October.
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- Jev was the fastest-adopted model Vercel has ever measured. Within two weeks of launch, 35% of paid teams on Vercel's AI Gateway were using it, nearly double the reach of the second most popular model, Sonnet 5.5, TechCrunch reported. Vercel itself swapped Jev in for GPT-6 Luna to classify harmful commands.
- TypeSafe's launch had developers talking about trusting a number. Armin Ronacher, CTO of Earendil, told TechCrunch that a 50% answer is "maybe a coin toss" he would ignore, while a 95% answer is one he can act on. He also sees it picking which model should handle a job.
- A developer released a Jev-style model you can run on your own machine. Jev-Style 2B v4 was trained in part on answers collected from Jev's public API, its maintainer Chaoliang Yan says. He has no tie to TypeSafe.
- InfoQ walked engineers through the new format. You send a state and a set of typed questions, and Jev answers them all in one pass with a choice, a score or a yes/no probability. TypeSafe calls these System One models.
- Free, open decision models now beat Jev on a public index. Nace AI released four Drex decision models with all the weights and inference code, and they are also served through OpenRouter. Drex Max scores 67.52 on Decision Index 0.4.1, against 60.70 for Jev 1.14.0.
Quick Hits
The Lab Gladiator Era
- Google released its own decision model on Wednesday. Gemini Decision 1 and a new Decisions API went live on November 11 for yes/no, choice and rubric decisions across text, images, audio, video and PDFs. Its answers are calibrated decisions with zero output tokens, so there is no text to parse.
- OpenAI opened its version to every customer, built on a sped-up GPT-6 Luna. OpenAI made its Decisions API widely available on October 22, TechCrunch reported. Product lead Nikunj Handa said answers now take 100 to 180 milliseconds instead of one to three seconds. Speak, which makes AI language tutors, cut the time between actions from 1.4 seconds to 0.13 seconds.
- The startup that started the category raised $870 million weeks after launch. TypeSafe AI, maker of Jev, is now valued at $7.5 billion after a round led by Andreessen Horowitz, with Sequoia and DCVC. CEO Diogo Almeida previously worked as a researcher at OpenAI.
DeepSeek's Quiet Takeover
- Shanghai AI Lab's 4B model beat Jev on the paper's tests. Intern-Decision 4B averaged 90.02% across seven test suites, against 88.74% for Jev v1.13.0. It answers a three-question request in 44 milliseconds on one RTX 4090, and the smaller versions take about 33 milliseconds.
- Microsoft's decision model is built on Alibaba's Qwen. Microsoft post-trained Qwen3.5-9B to make Microsoft-Decision-1 and says it will soon rebase the model on others, including its own MAI models and OpenAI's. Input costs $0.042 per million tokens and output is free.
Auto Mode Everything
- Liquid AI's open decision model answers in 8 milliseconds on a gaming GPU. d1-3B scores 48.57 on the Decision Index, ahead of every model under 10B parameters, Liquid said. It takes 8 ms on an RTX 4090 and 50 ms on a Jetson Orin Nano, and the weights are free to download.
- Chatbots still score higher on decisions, at 15 times the cost. On Artificial Analysis' Decision Index v2, Claude Haiku 5.5 scores 77 and GPT-6 Luna 76, about 3 points ahead of the best decision model, Drex Max v1.1, at 73. GPT-6 Luna costs $1,530 per million tasks against $102 for Drex Max, and takes 11.34 seconds per task against 275 milliseconds. 45% of the index now uses private data, up from 25%, to guard against benchmark gaming.
Production AI just split into two jobs
Vercel routes more than 100 trillion tokens a month through its AI Gateway, and its November report says production AI is splitting into two jobs: generation and judgment.
Decision models arrived on the gateway in mid-September. By October there were 15 of them from eight vendors, and they took 26% of all requests while accounting for 0.53% of spend. Per request, they cost about a seventieth as much as a language model.
Nearly a third of paid teams used one in October. The bigger the team, the more likely: just over one in five teams running fewer than 10 million tokens a month used a decision model, and seven in ten teams running more than 10 billion did.
Almost all of that volume went to Jev, which alone got nearly four times as many requests as all Anthropic models. The money still went the other way: for every $1 teams spent on Jev, they spent $64 on Anthropic.
That is the split the labs are now fighting over. Language models still earn most of the money, and the cheap, fast calls inside software now go to a different kind of model. Google charges $0.10 per million input tokens for Gemini Decision 1, with no charge for output. OpenAI charges the same $0.10 for its Decisions API, with no output, cache-read or cache-write charges. Microsoft charges $0.042 per million input tokens and nothing for output.
Key Takeaways
- If your code asks a chatbot to pick from a list, try a decision model. Google, OpenAI and Microsoft all sell one now, for $0.10 or less per million input tokens and nothing for output.
- Chinese work is already inside this market. Shanghai AI Lab's Intern-Decision 4B beat Jev on its paper's tests, and Microsoft built its model on Alibaba's Qwen.
- Keep a language model for the hardest calls. Claude Haiku 5.5 and GPT-6 Luna still lead Artificial Analysis' decision index by about 3 points, but cost 15 times as much and take far longer.
Worth Reading
- MMDM, a benchmark of 28 decision models: the best model gets only 36.81% on hard questions with several right answers, and Perplexity's Decider drops from 78.75% on world knowledge to 47.95% on constraint planning.
- Jeva, a decision model designed from scratch: the authors argue a decision model should give the same answer when you reorder the options and should be able to say "none of these."
- The Decoder's first look at Jev: how TypeSafe pitched a model that scores options instead of answering customers, and where its limits are.
Wait, What?
- Jev is named after the economist who said cheaper things get used more. William Stanley Jevons described how a falling price can make people use a commodity more and more, TechCrunch reported, and TypeSafe expects cheap intelligence to end up everywhere.
- About 11% of Vercel's paid teams used a decision model and no text model at all. In October those teams did not send a single request to a model that writes text, Vercel said.
This week's poll
Are you using a decision model yet?
Last week, 402 of you voted:
**Which result this week will tell you the most about AI?**
**Are you using a decision model yet?**
Until next time,
Alexis