What people post on X about Jev

Obie Fernandez

@obie

Pour one out for @CompleteSkeptic. OpenAI, Cloudflare and Perplexity all have decision models now, and ruby_decision_model 0.2 talks to all of them, Jev included, through one Ruby client. Same support ticket, five models, results inside. x.com/i/article/2107…

nikhil mudholkar

@nikhilmudholkar

I put OpenAI’s new Decisions API through the same 1,565-email benchmark I’ve been running on Jev, Perplexity and Clef. 6 models from 4 companies now. Perplexity v1.1 came out best overall 🚀 OpenAI was the fastest⏱️ But the confidence behavior is still the most interesting part to me. Jev was the only model that said “≥99% sure” hundreds of times without getting one of those answers wrong. Its the cocky guy in the class who is also right most of the time🥲 Clef has massive imposter syndrome! it's much better than its confidence suggests. And Perplexity changed a lot in just 4 days. v1 gave just 3 answers at ≥99% confidence. Four days later, v1.1 gave 323. It also got slightly more accurate and cut the price in half. kudos @AravSrinivas and @perplexity_ai for making such massive strides and shipping so quickly! There's no doubt now that @typesafeai has opened a pandora's box and as @CompleteSkeptic said, forked a new branch in the AI timeline with these decision models!

Afiz ⚡️

@itsafiz

Meet Kev - open source version of Jev Everyone is talking about Jev right now But it is a proprietary model and you dont' know what goes behind the scences Kev built on Qwen3.5 based on Jev's architecture. You can use pretrained weights or train your own Open source and 100% local.

Pandaily

@thePandaily

Shanghai AI Lab's Intern-Decision comes in 0.8B, 2B and 4B sizes and returns choices, scores and yes/no answers with probabilities; MetaX shipped Day-0 support. x.com/i/article/2107…

Rhythm Garg

@rhythmrg

You can now train your own decision model in our platform AC2! Jev-like models, custom fit for your use case :) We implement decision models through a language-model backbone with a small learned output layer called a decision head. The backbone processes the input, question, and answer choices, and the head produces scores which are softmaxed to produce a probability distribution over answer choices. Our software interface makes it easy to add a variable-sized decision head to frontier open-weight models. Choose any open source model, and train it as a decision model using Brier loss or cross-entropy loss. Training updates both the decision head and the language backbone. We tried it on 50k sampled training datapoints from the Civil Comments dataset, starting from Perplexity’s pplx-decider-v1-27b, and F1 for detecting toxic comments increased from 0.482 to 0.677 on 20k sampled eval datapoints. The training job took less than an hour on four B300 GPUs. Reach out to get started with your own classification task: https://docs.appliedcompute.com/platform/training/decisions

Natzir

@natzir9

I tested Google's new decision model for the browser, MediaPipe Decision Maker (Chrome's own Decisions API has no engine yet). On its own it gets 75%: below Gemini Nano (88%), OpenAI's Decisions API (83%) and Jev (92%). Psss... it sends usage data back to Google 👀

yessir

@JanahYsr

ran microsoft-decision-1 on my private eval suite (n=3000). Microsoft-Decision-1: 60% F1 VS Jev-1.13: 90% F1 draw your own conclusions but @CompleteSkeptic is right about public benchmarks @satyanadella just put the jev in the bag please

cola_runner · AI experiments

@cola_runner

Microsoft Decision 1 vs Jev 1.13 playing Contra. Same gameplay settings: same prompt and starting state, 30 lives, 6 frames/action, 600 decisions each. Microsoft on the left, Jev on the right. No lookahead or action overrides. Jev got farther. Neither cleared Stage 1.

Stanislav Sorokin

@stas_sorokin_

Jev raised $870M at $7.5B yesterday. On 262 of our real decisions it beat Claude Haiku 5.5, 204 to 177. TypeSafe's decision model got that valuation weeks after launch, in the same week Anthropic shipped Haiku 5.5 at $0.10 per million input tokens. So we checked whether a cheap general model can do the deciding. Haiku 5.5 got the labeled holdout cases from 17 decision systems we run every day, with the same instructions, word for word. → Right picks: Jev 204/262, Haiku 5.5 177/262 → By system: Haiku won 1, tied 5, lost 11 → Cost of the 262 calls: $0.058 on Haiku, $0.022 on Jev (2.7x) → Median answer: 1.16 s vs 375 ms → Biggest gap: "which builder gets this task" 6/12 vs 10/12 The twist: the one system Haiku won is the one that asks for judgment. "Did the agent really finish the task": Haiku 11/11, Jev 9/11. One caveat: those instructions were tuned on Jev for weeks. Haiku read them cold. Would you put a general model or a decision model behind your agent's routing? Repost this now, because "Haiku is cheap enough for everything" is turning into a default nobody measured. Follow @stas_sorokin_ for numbers from AI systems running in real production.