01 Get access
Anyone can use Jev. OpenRouter is the easiest way in: one account and one key for many AI models, Jev among them.
- Sign up at openrouter.ai.
- Top up a small amount. Jev is paid, but only just: sorting all 100 emails in part 04 cost $0.0032.
- Make an API key at openrouter.ai/keys and copy it. The key is what lets Claude call Jev on your behalf, so guard it like a password.
- Hold on to it for part 02. The prompt asks you for it and stores it safely on your machine. There is no manual setup.
Model ID
typesafe/jev-1.13What Claude calls Jev on OpenRouter.
Limits
64K tokens per request, up to 32K of it your data. Text only, and best in English.
Price check
| Model | Input, per 1M tokens | Output, per 1M tokens | Input price vs Jev |
|---|---|---|---|
| Jev | $0.042 | free | 1x |
| Claude Haiku 4.5 | $1 | $5 | 24x |
| Claude Fable 5.1 | $10 | $50 | 238x |
USD list prices as of 21 September 2026. They change, so confirm on the provider’s page before you count on them.
Already on another platform? Jev is also on Vercel AI Gateway (typesafe-ai/jev), Cloudflare Workers AI (typesafe/jev) and TypeSafe itself (keys at console.typesafe.ai). Everything below assumes OpenRouter.
Anatomy of one call
You send a state
One email
“Hi, we run an online skincare store and want an AI agent to answer our order emails. Budget is signed off and we want to start next month. Can we book a call this week?”
You ask
3 typed questions
- choice: which team should reply?
- score: how good a lead is it?
- noul: does it need a personal reply today?
Jev answers
3 decisions
- sales, confidence 1
- 2.99 on a 0 to 3 scale (hot lead), confidence 0.99
- 0.78 chance it needs a reply today
A live call through OpenRouter on 21 September 2026: 0.26 s (the median of 3 runs) for $0.00002. The prompt in part 02 runs the same kind of test for you.
02 Wire it into your agent
Open your coding agent, in a fresh folder or your usual workspace, and paste the prompt below. It is written with Claude Code in mind, but any agent that can run commands on your machine will do. The agent asks for your key, makes one live test call so you can watch Jev answer, and saves the setup as a reusable skill. Jev is newer than every model’s training data, so the prompt sends your agent to the public docs first.
i want to add a new kind of AI model called Jev to this workspace. Jev doesn't write anything. you hand it some text plus a few questions, and it answers them almost instantly, for almost nothing. it's made for sorting and deciding. - Jev is brand new, so you won't know it yet. before anything else, read how it works: https://docs.typesafe.ai/llms.txt - i'll use it through OpenRouter, where it's called typesafe/jev-1.13. how to call it there: https://openrouter.ai/docs/api/api-reference/alphadecisions/submit-a-decisions-questions-and-answers-request. it's a brand new route, so follow those docs if anything has moved - ask me for my OpenRouter key (i get one at https://openrouter.ai/keys). keep it somewhere safe on my computer, never inside a file i might share, and never show it back to me - Jev can only answer in 3 shapes: pick one option from a list, give a score on a scale, or say how likely something is true. most answers also come back with how sure it is - do one real test so i can watch it work. make up a short sales email and ask Jev 3 things: how strong a lead is this, what kind of email is it, and does it need a personal reply. show me its answers, how long it took and what it cost. if the test fails, show me the exact error - then save what you learned as a reusable skill (or whatever your setup calls a saved instruction), so next time i can say "use Jev to sort these" about any pile of text the rule from here on: Jev decides, you write. when Jev isn't sure about something, you make the call yourself. anything you send to Jev leaves my computer, so ask me before sending anything private.
Once it’s done, point it at any pile of text: “use Jev to sort these”.
03 Level 1: route every message
Most of what you ask Claude doesn’t need its biggest model. Jev reads each message and names the smallest model that can handle it.
Step 1
You send a message
Anything normal. Quick replies and slash commands bypass the router.
Step 2
Jev sizes it
A hook calls Jev first, in about 0.3 s, and gets back a tier: haiku, sonnet, opus or fable.
Step 3
Claude gets a note
The hook passes the verdict along, something like “Jev sized this as SONNET, confidence 0.97”.
Step 4
A pinned helper works
Claude hands self-contained work to a helper agent locked to that model.
The catch. Claude Code can’t switch models per message, and a hook can’t change the model either. All a hook can do is add a note. So the router is that note plus four helper agents, each locked to one model by the model: line in its file. The prompt tells your agent to say this plainly instead of inventing a setting that doesn’t exist.
You end up with three switches. Your agent chooses the exact words and tells you them at the end. They look something like this:
/jev onRouter on. Jev sees every message first.
/jev offRouter off. This is the default, and the right setting for private work.
/jev statusON or OFF, how many messages went to each tier, and what Jev has cost so far.
It never holds a message up: if Jev is slow or the key is missing, it quietly steps aside. While it is on, your messages travel through OpenRouter to TypeSafe. It works in Claude Code in the terminal, in the VS Code extension and in the Code tab of the Claude desktop app.
build me a model router that uses Jev. the idea: Jev sizes up every message i send, and small jobs go to a smaller, cheaper AI model instead of the biggest one. Jev is already set up in this workspace. - for each message, ask Jev one question: what's the smallest model that can do this job well? give it 4 sizes to pick from: tiny (a lookup, a rename, a one-line answer), everyday (a normal email, post or short document), large (a multi-step build, research, a full report) and hardest (strategy, or anything where a wrong call is expensive) - match each size to a model i actually have, smallest to biggest, and make one helper agent per size that runs on that model. if i have fewer models than sizes, use fewer sizes. each helper ends its reply with one line saying which model did the work - when Jev is sure, hand the job to the matching helper and pass the result back to me. when Jev is less than 60% sure, or my message is a short reply that only makes sense inside our conversation, handle it yourself - it must never slow me down or block a message. if Jev is slow, or anything at all goes wrong, carry on as if the router wasn't there - give me a simple on and off switch, plus a status check that shows how many messages went to each size and what Jev has cost so far. keep it OFF until i turn it on - be honest with me: if your setup can't switch models per message, say so, and build the closest thing that works - test it for real with 8 sample messages, from tiny to hardest, plus 2 short replies like "yes do that but make it shorter". show me a table of what Jev picked and how sure it was leave it OFF when you're done and tell me the exact words to turn it on. remind me that while it's on, my messages pass through OpenRouter to the company that makes Jev, so it stays off for private work.
The same trick for skills. Give Jev the name and first line of every skill, and it returns the one to load with a confidence. Below 0.6, Claude decides. In one recorded run it picked the right skill 12 times out of 14, from a list of 145. TypeSafe’s cookbook reports wrong-skill loads dropping from 17% to 7.3% (182 skills, agent on Haiku 4.5), but that is their own test, not an independent one.
04 Level 2: sort anything
Before
Someone reads every email, ticket or invoice by hand. Or a big chat model reads the whole pile, at big-model prices.
After
Jev sorts the pile in seconds. Claude writes only the replies worth writing.
Same 100 emails, three models
| Model | Time | Cost | Lead score exactly right | Hot leads found |
|---|---|---|---|---|
| Jev | 15 / 15 | |||
| Claude Haiku 4.5 (thinking off) | 15 / 15 | |||
| Claude Fable 5.1 (effort low) | 15 / 15 |
Fable scored best, Jev beat Haiku, and every model caught all 15 hot leads.
100 invented emails for a made-up agency. Same questions, 10 emails per batch, live calls through OpenRouter on 21 September 2026. Cost is what OpenRouter billed. Separately, 27 questions about a single support ticket came back in one pass in about 0.25 s, for $0.00008.
use Jev to sort my inbox by lead quality. the emails are in [my export file or folder]. ask Jev 3 things about every email: - how strong a lead is this? 4 levels: not a lead at all (spam, vendors, newsletters, job seekers, support, existing clients), cold (vague interest, tiny budget, poor fit), warm (a real need that fits, but no budget or timeline yet), hot (a clear need that fits, plus at least two of: a stated budget, a stated timeline, a decision maker writing) - what kind of email is it? new lead, existing client, vendor pitch, spam, job seeker, newsletter or support - does it need a personal reply from my team? yes or no - tell Jev in one line what my business does, send the emails in small batches so it stays fast, and give me one spreadsheet with the hot leads at the top anything Jev is less than 60% sure about goes in a "check these" pile for you to judge. then draft replies for the hot leads only. writing is your job, not Jev's.
use Jev to sort my support tickets by urgency and by team. the tickets are in [my export file, or i'll paste them]. ask Jev 3 things about every ticket: - how soon does it need an answer? today, this week, or no rush - which team should take it? technical (bugs, outages, errors), billing (charges, refunds, invoices), sales (upgrades, pricing) or success (onboarding, training) - how likely is this customer to leave? 4 levels: no sign of leaving, mild frustration, openly looking at other options, has set a deadline to leave - if i have the customer's plan and how long they've been with us, send that along with the ticket. send tickets in small batches so it stays fast, and give me one table sorted by urgency, then by how likely they are to leave anything Jev is less than 60% sure about gets flagged for a person to read. then write a first reply for the "today" tickets only. writing is your job, not Jev's.
use Jev to check my supplier invoices for signs of fraud before anyone pays them. the invoices are in [my folder]. ask Jev 3 things about every invoice: - how risky does it look? 4 levels: looks normal, one small oddity, several warning signs, strong signs of fraud - does it combine changed bank details with pressure to pay fast? yes or no - what should we do with it? pay as normal, hold it and phone the supplier on a number we already know, or reject it - Jev reads text only, so turn each invoice into plain text first. send it together with what we know about that supplier: their usual bank details, their usual amounts and the date of their last invoice - Jev is weak at maths and at comparing dates, so you do the sums and the date checks yourself. only ask Jev the judgement calls give me a table with the riskiest invoices first and one line on why each was flagged. nothing gets paid or rejected on Jev's word alone. it only decides what a person looks at first.
05 Level 3: what this unlocks
When a decision costs next to nothing and lands in a third of a second, you can put one where a chat model was always too slow. There is already a real example.
Unclutter, in 5 lines
- Kitze’s free, open-source browser extension that tidies up web pages: github.com/kitze/unclutter
- One Jev pass judges up to 60 page elements: keep it, or is it an ad, a promo, a newsletter box, a social widget or a cookie banner?
- It hides only what Jev is at least 0.9 sure of. Anything doubtful stays on the page.
- It remembers the rule per page type, so the next article on that site gets cleaned without a new call.
- It needs a Vercel AI Gateway or TypeSafe key. An OpenRouter key won’t work.
Where Jev is the wrong tool
- Writing
- Chat
- Reasoning
- Counting
- Maths
- Dates
TypeSafe’s own list of weak spots, plus long inputs padded with irrelevant text. Hand these to Claude. The rule from part 02 holds everywhere: Jev decides, Claude writes.
06 Where this goes next
Wiring Jev in is the easy part. One prompt does it. The real skill is spotting which decisions in a business can go to a fast, cheap model, then building the system around them.
Start small. Pick one pile you sort by hand every week, write three questions about it, and let Jev take the first pass.
More guides like thisfollow along at@devdrop.ai
Every link in this guide
- openrouter.ai · account and credit
- openrouter.ai/keys · your API key
- github.com/kitze/unclutter · Unclutter
- console.typesafe.ai · TypeSafe direct keys
- instagram.com/devdrop.ai · more guides
- Model ID:
typesafe/jev-1.13