JEV - A stripped-down model, a suite, and one that does not speak

DeepSeek, Kimi, and Jev are not ranks on one overall board. One is for stacking throughput, one is a front that is already built, and Jev sits inside a flow, emits a probability, and does not speak.

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Co-author: folkbench.com (Folkbench is a reviewable evaluation and selection platform for AI APIs, model services, and related sites. It draws on published service information, prices, availability, latency, and evidence windows to help users compare and choose a path.)

Ten pieces in this series:

The class group is passing boards around again. Someone throws DeepSeek's score. Someone throws Kimi's score. Underneath, someone drags Jev in and asks which of the three is stronger. I looked once and left. I did not reply. What the group wants is a name, and I do not have that kind of name on hand. These three are not ranks on the same board. Force a ranking, and the noise stays in the group. It has little to do with how I work this week.

I use them by niche now, not by an overall board. The success I recognize is that narrow: a model leads in its own niche, and it does not have to be first at everything. Past the line from Opus 4.5 to 4.6, that habit is fixed. I think 90% of users can no longer pose a task beyond the model. Lift general intelligence another tier, and my days barely move. Compare general intelligence again, and I cannot feel it. Whether something speaks or not, that I choose every day. The interface also affects whether I can keep using it. Price is more direct. It has to be cheap enough that I dare to write it into a loop before I put the model into code.

The ones that speak, I use apart

DeepSeek, for me, is the path pushed to the simple extreme. Engineering taken as far as it will go, rate filled up, price cut in half. I open it, and there is not much on the page to wander through, and no industry workbench waiting. DeepSeek puts its effort into running fast and cutting the price. I dare to run the same thing several more rounds. Switch to a more expensive one, and I start calculating whether to save. Overnight batch work, I also throw to it. People are asleep, the calls are still running, and with an expensive model I feel uneasy.

I use it for work that wants throughput. I push a batch of logs through to scan, and I squeeze interface drafts back and forth, many rounds of the same kind of question. I watch two things: do not stall, and do not get expensive. The cursor spins too long, or one round starts to hurt on money, and the pipeline breaks on its own. It can write sentences too. I usually change them in passing and use them. I rarely stop to taste them. I also do not talk taste with it on this work. Taste has to be ground word by word. It was not priced this way so a person could grind sentences.

I put Kimi on the other side. Not to prove who is smarter. The front-end ability is filled up. A long piece of material goes in, a table of contents appears, the citations are still there, and the source can be spread open to read against. If I assembled that myself, an afternoon would be gone. It also built suites specifically for finance and law. The data sources are connected inside, the skills to use are sitting there, and there is a ready surface when the work is handed over. Regulations and financial reports, I turn through on that surface. I do not open a pile of other pages. When finance or law is spread out as long material, I do not want to build the environment myself first. What I want is to sit down and turn through it, not to spend half the time as my own front end. I open it when I need to read long documents, and when I need a workbench that is already there.

What I accept after using them is one thing. This is a product. It is not a model getting smarter and therefore growing a website on the side. Which house the data connects from, and which clause of a regulation gets turned to, do not grow by themselves because a model is smarter. What a report looks like when it is handed over, someone has to make the flow into an interface first. When speech is fast and cheap, I stack calls, and what I use is DeepSeek. When I actually need to read material and hand work over, I open Kimi's front. Both can speak. I do not substitute one for the other.

The one that does not speak, I set inside the flow

I do not take Jev to chat. State goes in, the closed questions are asked, and what comes back is probability. Which item to pick, or which level it lands on, including a plain yes or no, the type has to be written shut in advance. If the question has not yet been closed, I will not call it. Output is free. Input is $0.042 per million tokens. The speed is the fast kind, 70 to 500 milliseconds. At this price I dare to ask again and again inside one flow, without mental arithmetic on the bill at every question. I treat it as one call inside the flow. I ask, and I go.

Set inside my own flow as a joint. Code cleans the state up first, then throws a closed question over. The probability comes back, and the branch is walked by the program after it. For me it handles classification and routing, it handles levels, and it handles whether this step should call a person over. After classification I go on and ask which level it lands on, and after the level comes out I ask whether to call a person. All closed questions, all the same kind of call. Reasons and explanations written for someone else to read are not its job. Text that really has to be seen by a person, I connect a model that can speak. If this kind of judgment is given to a model that can chat, it often comes back as a whole paragraph, with an attitude and with advice. I then have to write another layer, and dig the paragraph into a branch. One more layer on the joint, and I do not think it is worth it.

I have opened the eval only once. Inside the workflow, its judgments are compared with Astra and Fable 5.1. What is compared is closeness, and cost, not a win or loss at chat. That is another coordinate system. Once I understood that, I closed the page.

A release should say who it is for

The GPT-6 wave made computer use something people recognize again. The model goes and clicks the screen itself, opens software, and finishes a thing on the desktop. People were doing this before. After Astra came out, operating a computer became something people can point at again, no longer only an extra outside the chat box. Set next to these three, that is even more settled. The moment a model is released, the people releasing it have to say clearly who it is for.

People ready to hand over the whole machine can use computer use. Another group sits in front of a chat box and wants the speaking finished. DeepSeek and Kimi both speak, but one is for stacking throughput and one is for a front that is already built, and those are already not the same use. Jev is not on the speaking side. The code is mine. It is only responsible for emitting a probability, so the program after it can walk. If I made a chat page for it, I would use it wrong. The moment there is a chat box, I want it to write the reason out.

I will not write a piece called "who is strongest in 2026". That sentence does not help this year. The two inside the chat box, and this one that does not speak, I separated in use a long time ago. I am not preparing to write that kind of piece either.

This is how I take them. Work that wants throughput, I give to DeepSeek. I do not spend myself on a tug over style, and I do not want to switch up to an expensive one. Long material, if it also comes with a workbench already built, I open Kimi. When code reaches the step that has to make a judgment — classification and routing, a level, and whether to call a person — I set Jev there, and I have it not open its mouth. The overall board can update if it likes. I take them by niche. The rank, I do not look at anymore.

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