Show HN: JevBench, a reproducible benchmark for typed decision models

Posted by florianstandhar 13 hours ago

Counter69Comment17OpenOriginal

Hi HN! I built JevBench because Jev kicks ass, and the world deserves to know how the serious open source and fake lookalike projects really perform in comparison.

Jev-class models return bounded choices and probabilities instead of text, and are disruptively faster and cheaper than LLMs, while being similarly intelligent on the text input they operate on.

JevBench allows looking at accuracy, latency and price all at once, in a weighted way - you can even configure the weighting.

A full run asks 534 English decisions. The v1.3 score combines chance-corrected Intelligence, Calibration, Speed and Cost.

Leaderboard right now:

  #1 - Jev            74.4
  #2 - SemIf          73.1
  #3 - djev           73.0
  #4 - Winnow-12B Q8  71.2
  #5 reflex 4B        70.3.
MIT harness, public items, frozen artifacts, scoring code and public per-task outcomes:

https://github.com/fstandhartinger/jevbench

Two no-signup demos:

https://who-is-right.app.mintapis.com

https://is-it-ai-slop.app.mintapis.com

Limitations: English-only; latency from one German server; local/demo latency gets a disclosed ×2 adjustment (+150 ms on my servers) which is an informed assumption; held-out prompts still reach evaluated services; ~1-point gaps can be noise.

Wdyt?

Comments

Comment by hbrn 4 hours ago

$40m in funding, 2 years in stealth.

Performs on-par with SemIf which was built in a couple days and apparently uses raw Qwen, with no fine-tuning. SemIf runs in your freaking browser. Oh and Jev is twice as expensive?

Is it surprising that Jev consistently thinks it's Qwen?

I'm almost convinced that Jev is a scam. Take Qwen, fine tune it a little, tell investors it cost $10m, spend $1m on advertising, profit.

Comment by Retro_Dev 2 hours ago

Isn't the entire deal with jev that it is fast? I'd be interested to know how the energy cost of the Qwen-based model compares with Jev. Of course, Jev is currently locked up so we don't know... "Trust me bro Jev is revolutionary and amazing, pay more money for our inferior product which costs more to run, and of which you need to access by sending us the data"

Comment by ks2048 1 hour ago

I was trying to figure out what exactly the tests here are. I guess I found some of the questions (here: https://github.com/fstandhartinger/jevbench/blob/main/datase...)

e.g.,

  "instructions": "Which intent does the user's message express?",
  "labels":["set_alarm", "play_music", "weather", "send_message", "turn_off_lights"],
  "state": "Play some Taylor Swift.",
  "expected": "play_music"

Comment by dmix 1 hour ago

You can spot vibecoded websites by how they include the prompt or commit-style comments into the literal interface, instead of communicating it via visual context (or simply excluding it)

> Sort by any column; values the run could not produce always sort last. Hover a cost for how it was priced, a latency for the endpoint. Names link to each project.

A designer would never write this, but an LLM just inserts it by making it small grey text next to the interface, just like it does with inane code comments.

Comment by dogscatstrees 1 hour ago

It's helpful metadata, what's wrong with it? Dashboards at work do this.

Comment by janalsncm 29 minutes ago

One person’s helpful metadata is another person’s noise. It’s much better for a door to visually indicate that it should be pushed open than to put up a sign there.

However, doing the former requires a level of empathy with humans that LLMs rarely have.

Human brains have caloric demands. It is possible for humans to process enormous amounts of unrelated facts to make a decision, but it’s tiring. It’s much better to not do that, especially just to get some basic information.

To anthropomorphize a bit, an LLM might find it charming and interesting to read someone’s life story as a preamble before their taco recipe. Humans by and large find that annoying, not because we can’t understand the biography but because processing that information is not free.

So it’s probably possible to design using an LLM. You would probably have to be intentional about it.

Comment by dmix 1 hour ago

It's fine in isolation to have help text for complex interfaces needing explanation. Better yet contextual hints.

But there's about 10 other examples explaining intention of the coding rather than immediately useful information to the user, it's all over this one site in small grey text. And I guarantee you nobody is reading them carefully. Just like how nobody likes reading a 15 line LLM code comment over a simple function.

Most of it could be better solved with more thoughtful design or deleted. The link explanation is particularly egregious.

Comment by CBLT 54 minutes ago

If you don't have any kind of agent instructions saying "don't copy code into prose", you'll inevitably get text that reflects some previous state of the system instead of its current state.

Comment by janalsncm 25 minutes ago

It is strange that you put the BGE reranker in the list but not BART which is an actual zero shot classifier.

Comment by sean_pedersen 4 hours ago

Good project but this one also exists https://huggingface.co/spaces/multimodalart/jev-decision-ind... and the results do not seem to add up and also model sets are different... still needs time to mature likely

Comment by swyx 5 hours ago

jev ceo on why he eschewed benchmarking: https://www.latent.space/i/216783460/privacy-benchmarking-an...

Comment by arbot360 4 hours ago

Many SaaS vendors forbid benchmarking, I find it crazy that such anti-competitive terms are standard across the industry but they are. Generally the goal of such terms is to "control the narrative" around the product, regardless of the truth of performance being better or worse than competitors.

Comment by jldugger 4 hours ago

Interesting; was curious how this didn't fall into trouble with ToS. Apparently the "no benchmarks" clause was intended for "limited preview" audiences and didn't get removed at launch on accident.

Comment by tomrod 4 hours ago

I mean, that's a great reason to ignore JEV entirely.

"Trust, but verify" isn't just a catchy cliche. It's the only way to operate where models and code are fast to market.

Comment by 2 hours ago

Comment by nzoschke 4 hours ago

https://is-it-ai-slop.app.mintapis.com/ is a fun tool. Is the source or methodology for that in the github repo? I couldn't find it immediately.

We've been experimenting with Jev for classifying email, some thoughts here: https://housecat.com/blog/classifying-email

Flagging AI written email is a much requested feature too.

Comment by 542458 2 hours ago

Keysmashing my keyboard resulted in 86% confidence that the text was AI written. I don't think this is a particularly good classifier, I've never seen an LLM output "kad jfkhasljkdhf laksjhdf".

Edit: If that's not realistic enough for you, the text "Hello world! My name is GravitasIsOverrated and I like coding and cooking. This text is 100% genuine, and not AI generated at all." results in 85% confidence that it's AI generated.

More broadly, I don't know why this would work. Qwen/Jev/whatever doesn't magically have the ability to discern AI-authored text from non-AI-authored text, and will increasingly get worse at it as the hallmarks of AI-written text change.

Comment by ajs1998 2 hours ago

I asked free chatgpt to give me some essays that will fool a slop detector and they all fooled this slop detector. Its best guess was "6% slop probability 87% confidence answered in 0.5 s for $0.000027" and yet it was 100% slop.

I am very skeptical slop detectors will ever work.

Comment by kevinbaiv 3 hours ago

[flagged]

Comment by 13 hours ago