Jev model routers and tool routing

Desert Ant Labs

@desertantlabs

Jev + on-device models = results in seconds with no LLM in the loop. Quick demo app to show the possibilities. Drop in an audio file: Ear detects the language, Voz transcribes it and Redact removes PII. Then @typesafeai's Jev makes about 20 decisions in one call in milliseconds, and picks which of our on-device models to run. Voice memo to to-do list. Meeting to redacted transcript. Podcast to clips.

OpenRouter

@OpenRouter

1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging. Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.

Sarah Drasner

@sarah_edo

🎇 I made a small Chrome extension: a side panel that drives any site's WebMCP tools with Jev! When you type, on every keystroke it picks the relevant page's tool, fills in the arguments, and tells you how sure it is. Here it is grocery shopping:

idan levin

@0xidanlevin

We just ran Jev on our WebMCP benchmark. The result: basically broke the benchmark. Jev + Mercury 2.5 (a fast, low-cost LLM) using WebMCP solved 100% of the tasks at roughly 112× lower model cost than GPT-6 Astra using computer use with code execution. Compared to Astra using screenshot-based computer use, the model cost was 245× lower (!). We also compared Jev operating the browser with and without WebMCP. We used Browser Use’s open-source Ultrafast, with some improvements to the harness to make it more reliable across the benchmark. Jev’s browser-control accuracy on its own was not amazing - adding WebMCP nearly doubled the number of solved tasks, from 25/49 to 49/49, while reducing model cost by 18% (more on why below). The benchmark and methodology are fully open and reproducible. Full results: https://webmcp.com/benchmark A few words on how the Jev + WebMCP harness works and why this is exciting: Jev receives text as input and a set of discrete options it can choose from. With WebMCP, those options are the tools exposed by the website. At each step, Jev sees the task, the available tools and previous results, then picks what to do next. The limitation is that Jev can’t generate arbitrary text, which you need for tool arguments. For example, it can choose the search_products tool, but it can’t generate the search query itself. So we split the work: Jev picks the tool and Mercury 2.5 generates the arguments if needed. This works well because turns out most of the cognitive load in these tasks is around choosing the right action. The argument generation itself is relatively simple, so we can delegate to a small and very fast model. We used Mercury, which outputs 1,000+ tokens/sec and is very cheap. The result is a pretty simple combination: Jev for tool selection + Mercury for arguments + WebMCP for the interface. It ends up being very reliable, very fast, and very cheap. A few words about Ultrafast and why do we think it underperforms: Without WebMCP, Jev chooses from the page’s controls: which button to click, which field to fill, or which option to select. But choosing a valid button is different from choosing the right next step. The agent still has to navigate menus, understand forms, recover from errors and recognize when the task is actually complete. Our hypothesis is that WebMCP makes the decision space much simpler. Instead of figuring out a sequence of clicks through a website, Jev chooses explicit actions that directly advance the task. @typesafeai itself documents weaker accuracy on questions requiring multiple reasoning steps. WebMCP moves much of that complexity into the website’s tools, leaving Jev with clearer decisions and fewer opportunities to go wrong (in a sense WebMCP "compresses" a sequence of clicks into one tool call). Our modified Ultrafast setup solved 25/49 tasks - that is a result for our particular implementation and benchmark, not a universal limit on Jev or Browser Use. We are open to more harness optimization to get this result to perform better, feel free to directly contribute to the benchmark here: https://github.com/nekuda-ai/WindTunnel Browser-use ultrafast: https://github.com/browser-use/jev-ultrafast

Hassan

@nutlope

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on @togethercompute - $0.003 (1/3 of a cent) from Jev on @typesafeai. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.

Milind S

@milindlabs

got @typesafeai's new model Jev as a chief of staff for bots Jev reads the task, wakes the right teammates off the bench and gives each one the right model It is possible on OpenMausBot as it supports all the LLMs from your existing subscriptions Jev as a decision engine is great

Riley Brown

@rileybrown

Building an agent with model router powered by Jev.

CJ (Coding Garden)

@CodingGarden

I built a chat bot with jev, no LLM at all! Responses are instant, no hallucinations. I hooked it up to web search, wikipedia, weather, todoist and home assistant. Jev decides what tool to call and what args to use based on the prompt. Instant answers cite sources as well!

John Yeo

@johnyeo_

Jev made our Slack agent 2x faster ⚡️ Our agent can be quite slow because it needs to read skills and figure out which tools to call. We used @typesafeai's new model to speed this up by first passing it the prompt and classifying the best skill, tool and params to use before handing it to the agent

Duncan

@ephraimduncan

Built a model router with Jev by @typesafeai. Jev decides what model fits your request best and the request is sent to that model.

Sawyer Hood

@sawyerhood

thanks to @typesafeai jev I no longer have fill out all of those fields on prompt boxes. It picks the agent / model / computer / folder for me. - For a major rewrite it uses Fable + Claude Code. - Changes to an ios app run on one of my macs

t0t0

@t0t0_build

Un JEV me está monitoreando más de 25 grupos de whatsapp con clientes en tiempo real y evaluando si tengo que saber algo por urgencia problemas, pedidos irresueltos etc. Si tengo algo que saber, le avisa a una LLM que me escriba. 🔥

Jiaqi Gu

@droidqw

github.com/droid-Q/jev-sk… ，基于 jev 的 skills 路由

codila

@0xCodila

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open @typesafeai , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

VertrAI

@vertr_ai

154ms vs 860ms is a useful routing result, scoped to these 200 synthetic classification cases—not a blanket ChatGPT comparison. We credited your latency and cost charts in our Jev video, alongside the game/browser demos: x.com/vertr_ai/statu…

VertrAI

@vertr_ai

ChatGPT thinks through the plan. Jev helps pick the next move. ⚡ Minecraft, browser automation, speed and cost—Jev explained in under 2 minutes. 👇 Featuring demos by @rronak_ and @gregpr07, benchmarks from @OpenRouter, and Jev by @typesafeai. #Jev #AI

Vadim Choi

@vadimchoi

$10k/month just to decide which agent should answer. not to answer. to decide. group chat + @mentions: fast, cheap, terrible UX. an LLM orchestrator: nice UX, 4-7s and $0.00046 a message. @typesafeai jev: 0.29s median, $0.00002. same decision, $20/month.

Ayush Jain

@urayushjain

Most AI models are built to generate. But Jev is trying something different: Don’t generate. Decide. Give it some context and a specific question, and it returns a structured decision with a probability. I found this interesting for things like model routing, agent workflows, tool safety and RAG. The idea is simple --Instead of calling a big LLM for every small decision, let your code handle the workflow and use a smaller model where human-like judgment is actually needed. Honestly, this feels like an interesting direction for AI systems.

Shengkun Ye

@shengkunye

Introducing Jev + OpenRouter + tools. You can now use Jev with 2,000 tools on Monid. Your agent picks the tools, Jev speeds the work up 30x. > Score 2,000 leads > Scan the TikTok viral video hooks > Research fundraising of 200 companies > Audit a site's SEO and rebuild the internal links on every page > Sort every Reddit thread about your category

mercante

@merccante

GROK 4.7 IS $2/$6 IN CURSOR. Jev now decides if the model even wakes up same sticker as 4.6. live in Cursor and Grok Build. CursorBench 4.0: 46.3. DeepSWE v1.1: 71.0. Terminal-Bench 4.0: 37.6. the loop: repo state → Jev Noul/Choice/Score → allow / ask / skip → only then grok-4.7 1 → dump the tool call as state, not a prompt essay 2 → Jev Noul: is this user-requested 3 → Jev Score: blast radius 0-3 4 → Jev Choice: allow, ask, or skip 5 → hard rules still veto. model never gets last word 6 → shadow 200 tool calls. log pick vs what 4.7 would have billed 7 → flip live. you still approve the risky ones no auto-merge. no wallet. no "just send every bash to Fable" result: Jev on OpenRouter is $0.042/MTok in, output free, p50 ~270ms a ~1k-token gate is about $0.00004. 4.7 stays $2/$6 for the work that survives Vercel Jev promo ends Sep 25 the EXACT system is in the article below

Loutchone

@LiorNsnd

bon un peu de serieux, jev peut etre vraiment utile... pour préparer un workflow de qualification de leads après un audit client, je veux un premier tri de ce que l’agent propose avant de mettre les étapes dans Obsidian. j’ai donné à Jev le contexte de qualification avec les étapes candidates. À lui de choisir pour chacune : retenir, clarifier ou écarter. sur cet appel API, il retient la qualification par téléphone et le rappel des prospects injoignables. Par contre le rappel à J+2 ressort « à clarifier » : le contexte fourni ne fixe aucun délai. L’enrichissement LinkedIn est écarté, il était hors périmètre. la première image, c’est sa réponse réelle avec les probabilités. La deuxième, le workflow qu’on en a tiré, avec le délai toujours à confirmer. dans mon process, je garde la main sur cette présélection : je vérifie et je valide avant de l’intégrer à l’audit. Voilà où je vois l’intérêt de Jev dans mon taf.

Adithya Thatipalli

@adithatipalli

After seeing so much Hype around Jev, I started using it inside my Hermes as a decision and routing mechanism for a AI video pipeline I was working on But now, I am Changing that thought. Laya is a 421M local decision model by @Nandakishorm1 . Same job as Jev -> choice, score, yes/no with a probability, But it runs on my machine. No API hop. No per-token meter. ~33ms. Weights stay here. For an AI film pipeline, that is the actual bottleneck. >Not “write a better prompt.” >The expensive part is sending the wrong shot to the wrong generator. Before Seedance / Kling / Minimax / Veo / Flux / GPT Image burns a credit, Laya now answers four things in one pass: → which model gets this shot → text-only, character-ref, or first-frame → is the brief ready, or does the enhancer skill run first → how hard is identity lock Sol still writes. Kimi still helps. Video models still generate. Laya only decides. Jev was a good hosted nervous system. For shot routing I don’t need a hosted brain. I need a local one that sits in front of the generate button and says go / wait / switch model. 421M. Local first. That is the router now.

Cesar Favero

@cesaremuszka

botei o jev no codexrouter, sinto o trabalho muito mais otimizado, vou criar um harness personalizado para o codexrouter, sinto que da pra fazer muitas melhorias nesse fluxo o jev por enquanto no codexrouter está com poucas funções

Neha Sharma

@hellonehha

As Jev (@typesafeai) is the talk of the town, and people are having fun finding new use cases for it. I built a real-time demo of smart AI agent routing for anyone working on AI tools, agents, or products. this is what I have done for one of my project. https://youtu.be/c8qV0f4XnRY

Oliver Prompts

@oliviscusAI

grok 4.7 thinks. jev reacts. together, one ai agent now thinks twice before every move, and pays a fraction of the price for it. grok-jev-reflex is a free, open source router that pairs the two: grok 4.7 plans and acts, jev makes the cheap yes/no calls in between, at $0.042 per million tokens, answered in about 239ms. > 25 tools sent on every call, cut down to 8 > first prompt: 17.8k tokens → 9.5k tokens > bill: up to 40% less, same speed > numbers measured independently on xAI's grok build setup is 5 messages, no code, about 7 minutes: make a key, install, add one rule, run a shadow day watching only, then go live. jev can only make the agent do less or ask more, it can never approve anything irreversible on its own. 100% free. open source.

Geek Lite

@QingQ77

Codex 一旦开跑，推理档位就钉死不动，读个文件也用最高强度烧 token,这个项目让 Jev 在每次生成前重新挑档位。 github.com/miuuyy/Astra-A…

Harsh Singh

@haaarshsingh

i created a 404 page that fixes your typos for you, and redirects you to the correct page built with @typesafeai jev try it out: kobra.systems/cmr-table

امیرحسین ثقه الاسلامی

@AHSeslami

اولین تجربه‌ی خودم با JEV 👀 این تجربه برای ۳ روز پیشه؛ وقتی JEV هنوز توی حالت Waiting List بود. من کلاً هر ابزاری که سخت‌افزارم بتونه بکشه رو تست می‌کنم 😄 برای همین توکن JEV رو از OpenRouter گرفتم و به Grok Bot دادم. ماجرا چی بود؟ از GITEX کلی ویدیو گرفته بودم و نمی‌دونستم کدوم‌ها رو اول منتشر کنم. از اون طرف هم برای هرکدوم کلی کاور طراحی کرده بودم و واقعاً حوصله انتخاب و مرتب‌کردنشون رو نداشتم 😂 به JEV گفتم: «خودت انتخاب کن، دسته‌بندی کن و فولدربندی‌شون کن.» در کمتر از چند دقیقه همه‌چیز رو مرتب کرد و حتی پیشنهاد داد کدوم ویدیوها رو اول منتشر کنم. Grok Bot هم بقیه‌ی توضیحات و تحلیل‌ها رو انجام داد. برای اولین تجربه، واقعاً از JEV راضی بودم 🔥 هنوز Laya-Max چینی رو هم تست نکردم؛ نصبش هنوز تموم نشده. اونم که آماده شد، تجربه‌ام رو می‌گم 👀