I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
They are? In all my evals at work, Jev seems to win, despite me wanting the open weight versions to.
I imagine if I fine tuned one of the open weight models for my use case it would be possible to beat it, though, but as a general drop in decision model, I’ve not really seen much that beats it especially for the cost
Same here, and I have a project 100% hosted in Cloudflare ecosystem (AI Gateway, Workers, Queues, R2, D1, DO, etc) I was so happy when I saw clef. Tested, and it's bad. Slower and wronger than Jev.
Still waiting on Decisions API using Luna, as this what I was using before.
I used the model and it was good enough for email classification.
I was wondering if I'm supposed to be doing multiple runs for topic classification with grouping related topics i.e. if its sport,economy or tech related news and then another run for soccer,basketball or something else
or batching them like the News article topic classification example in a single run.
Cool to see another model with support for bounding boxes on images. Really wish to see segmentation in a future release; Gemini 3.5 Flash-Lite is mandatory in my use case as it's the best model for semantic image segmentation in my experience.
Using it as part of an agentic CAD workflow, where the model needs to be able to selectively segment from parts of a user's image as it works on a project. Using OpenSCAD so being a general purpose model is very useful to me.
If openscad is the thing Google models has consistently outpermormed the comparable competition in my experiences. Few other models has better physical spatial reasoning skills.
Going from "I'll need a research team and 5 years" to Jev-based bird classifiers in just 12 years is really nice. I'm happy that some problems get easier, and I'm happy that people make things and share their work so we can all benefit.
Well, they do need to try to compete with SubQ’s longer context and image processing that has been coming out ahead in benchmarks, plus the thousand self-hosted options that now exist.
I have to admit, despite my initial scepticism of Jev, it is actually pretty fucking good at making decisions. I’m trying to break it with weird situations and I just can’t. And it’s so cheap!
Interesting way to release this, I'm not sure how to say this, but this has a real "enterprise"/old school taste to it verses how all the other models/AI companies are releasing things.
I use it as a drop in replacement for LLMs I had previously doing filtering and classification in a pipeline. It's loads cheaper and faster with roughly equivalent performance on my own internal tests. The confidence scores (which are also reasonably well calibrated on my data) are a super nice bonus.
You can turn a lot of process and workflow systems into deep decision trees.
My team hasn't finished writing their blog posts yet, but the customer demos [2] went out the moment TypeSafe took our call and sorted all the paperwork out.
There's a tiny section at the bottom of the demo which explains how it actually works (or the github repo is the code).
We have a single decision API[1] over all similar models, so we run evals across all the "System One" api products & definitely don't trust any benchmark that you haven't run. All standardized benchmarks have been benchmaxxed already.
PS: just in case it wasn't clear, the demo is a game where we play to calibrate the human vs machine decision making (or just do it in auto-pilot mode).
it's actually more usefull than it looks. it is not much useful on its own but it can play a big role in a complex workflow or custom harness.
i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps
I found an example online, I cannot find the github again, but it was interesting: an instant dynamic generation of React component, you type what you want to build using natural language, and it directly add one to the page.
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
They don't. Generally we already have trillions tokens worth of classifier datasets so there's no reason to create synthetic data that will be worse than the data it was trained on.
So they held back a bunch of features because they knew they had basically no moat, and now they're trickling out a bit more functionality to try to convince people they are better than others and move fast? And using Apple advert slogans to show how cool they are?
The way that site charges a fee[1] for benchmarking models, makes any benchmarks on it very sus, IMO. TBF, the benchmarking site is probably more profitable than TypeSafe, just like the joke about how WinRAR is more profitable than OpenAI[2]. :)
- Inference for a bunch of open weights architectures
- Diffusion image generation fir Z ai turbo
- Vision input into LLM for inference
- Vulkan/fast CPU, optimized kernels
- Playground for testing
- Last but not least, system one fast inference mode with same API semantics as Jev
Decision models work by using a backbone model like qwen 3.5 9b, along with a special linear head that provides the readout for the forward pass.
Videos will be next.
I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
I imagine if I fine tuned one of the open weight models for my use case it would be possible to beat it, though, but as a general drop in decision model, I’ve not really seen much that beats it especially for the cost
Still waiting on Decisions API using Luna, as this what I was using before.
Run your own evals, folks.
[0]: https://xkcd.com/1425/
As it's minor change did not expect much, but having a higher context helps for sure. I will own the errors for passing bigger context.
Very refreshing.
My team hasn't finished writing their blog posts yet, but the customer demos [2] went out the moment TypeSafe took our call and sorted all the paperwork out.
There's a tiny section at the bottom of the demo which explains how it actually works (or the github repo is the code).
We have a single decision API[1] over all similar models, so we run evals across all the "System One" api products & definitely don't trust any benchmark that you haven't run. All standardized benchmarks have been benchmaxxed already.
PS: just in case it wasn't clear, the demo is a game where we play to calibrate the human vs machine decision making (or just do it in auto-pilot mode).
[1] - https://github.com/prasanthj/duckdb-dual-cognition
[2] - https://demos.isotopes.ai/demo/apps/chain-command
i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps
If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."
> For the builders AND dreamers
> Enter the danger zone at your own risk.
> Size does matter, apparently
> We’re trying something new out here: we’re going to treat you guys like adults
> Lots of people love to ask what kind of sci-fi we’re releasing next
That said, their docs are great and don't seem to include all the Reddit cringe.
I've yet to see any company match the copy writing skills of Valve though.
I guess their cheeky "Warning: AI Benchmarks, don't trust them" is their way out of that one.
[1]: https://benchmarkheaven.com/submit
[2]: https://news.ycombinator.com/item?id=49797811