curl https://api.openai.com/v1/decisions \
    -H "Authorization: Bearer $(llm keys get openai)" \
    -H "Content-Type: application/json" \
    --data '
  {
    "model": "gpt-6-luna",
    "input": [{
      "role": "user",
      "content": [
        {"type": "input_text", "text": "I am angry about the new product feature"}
      ]
    }],
    "questions": [{
      "type": "predicate",
      "name": "complaint",
      "instructions": "Is this a complaint?"
    }, {
      "type": "predicate",
      "name": "compliment",
      "instructions": "Is this a compliment?"
    }]
  }'

Returned:

  {
    "model": "gpt-6-luna",
    "answers": [
      {
        "type": "predicate",
        "name": "complaint",
        "probability": 0.91
      },
      {
        "type": "predicate",
        "name": "compliment",
        "probability": 0.06
      }
    ],
    "usage": {
      "input_tokens": 310,
      "input_tokens_details": {
        "cached_tokens": 0,
        "cache_write_tokens": 0
      },
      "output_tokens": 0,
      "output_tokens_details": {
        "reasoning_tokens": 0
      },
      "total_tokens": 310
    }
  }

That https://api.openai.com/v1/decisions endpoint is notable because usually when OpenAI define an endpoint like that it ends up as a defecto standard for other providers.

(I turned this all into a new llm plugin: https://github.com/simonw/llm-openai-decisions)

The response to Jev should be the nail in the coffin over whether or not the AI business is a commodity market.

Out of no where Jev appeared as the next round of the price wars. Jev showed the value of System One models. A fast yes/no/confidence score not only is cheaper but also often all people want. Open source versions flood hugging face and now the big players are giving up a potentially big driver of output tokens to keep customers and race to the bottom price wise.

If I were OpenAI or Anthropic I’d be racing to make their products as sticky as possible bc ppl will flock to what’s cheapest otherwise.

Just going to drop this here: https://jeffyclassify.com/

Open source classifier models you can run and train locally on CPU

Ran my decisions evals (still rudimentary, less than 600 calls (UI component selection, chat charting, tag selection, PKM stuff)) on this via OpenRouter against Jev and Mercury Decide. Jev because it has replaced my mt0 efforts by sheer force of affordability (more importantly, the limits running on a MacBook Neo bring even after vocab pruning and quant insanity) and Mercury Decide because I do like dLLM efforts (and I'd like to use fewer model providers if possible).

Preliminary of course, but seems to be slower than Jev and similar to Mercury Decides latency, though not in growing linearly with the amount of input (346ms p50 and 860ms p95, (Mercury Decide also had some extremes up to 1,3s that were around 800ms today, likely preview related, it scaled far more consistently with size)), less "confidence" concerning my ambiguous UI component and response shape specific tasks (have very specific use cases for these models which Luna often fails to meet at 0.6 and lower), lead to a few failed calls which neither competitor had (4 vs 0 for both) and measured more expensive than Jev to boot by a factor of 3,1 times on average (Mercury Decide pricing I think is still unknown so no numbers there).

Basically slower, more expensive and less capable than Jev, roughly on par with Mercury Decide (provided, in my insane set of use cases and requirements that are a PKM focused Firefox fork with multiple infinite canvas using decision models to improve information synthesis from multiple sources).

Seems a bit undercooked overall and I'd rather frontier-labs don't jump on bandwagons until they can offer something competitive in price, performance or both. In fairness, though, I have yet to test image input, maybe that makes all the difference. Also, again, mine is unlikely to reflect everyones use case, so interested in seeing others results.

Didn't comment at the time, but having read up on Devday after the fact, there seems to have been a lot of that going around. Notion and GDocs, Jev, Muse, most seems to have been cloned from existing competitors (and despite infinite, ultrafast, ultra code tokens with unsandboxed Mega Astra not that amazing to boot).

Prefer less announcements, but focused and at a higher quality. Considering ChatGPT Atlas (their Chromium based browser) and its insanely fast death, I'd be skeptical to put much into any of these even if they were in some way an improvement over what is out there. Maybe focus on a fresh pre-train and some sandboxing improvements.

Jev really shook up the industry. This seems obvious in hindsight

Different APIs for different things reminds me of the early auto-complete vs instruction apis.

Will this get folded into models / post training pipelines at some point and make them better at calibrated outputs?

What value is there in knowing if a can has a dent ?

If you rather run your decision model on your CPU, check gutsy [0]

[0] - https://news.ycombinator.com/item?id=49976996

I wonder why the decision routing isn't just integrated into all models in addition to this stand alone.

it already supports image inputs, which was the first big gap I found in Jev.

Since it is fast and understand images, I wonder if it can play video games. I have a harness setup for the LLM play EA FC but even the fastest LLMs are too slow for it. I need to try this with Decisions API

One difference between Decisions and Jev (for now) seems to be that Decisions can take image inputs, which is a pretty common need.

> The tulip became a luxury item and many varieties were introduced. The varieties were classified and the most sought-after, prized tulips were the streaked tulips, especially yellow or white streaks on a red or purple background. These flame-like tulips were highly sought after. Interestingly, the streaks or “flames” of the tulip petals were caused by a virus. The virus is the tulip breaking virus, or tulip mosaic virus.

Source: https://www.canr.msu.edu/news/tulip_mania_the_history_of_the...

What's old is new.

You knew it was going to happen! Benchmarks or it didn't happen.

This rather didn't take long for OAI to create*, I remember people giving opinions and discussions that it won't take too long and that openAI should do it[0], so looks like they were right.

Interesting to see where all this leads us and if other major labs follow suit

Edit: decisions voice looks really interesting as well[1]

[0]: https://news.ycombinator.com/item?id=49802161: OpenAI is well positioned to fast-follow Jev

[1]: https://developers.openai.com/api/docs/guides/decisions-voic...

v3.26.0 of the openai Python SDK covers its use. Those already using the SDK don't need to make explicit HTTP calls.

How is the pricing vs Jev?

Can we use this through subscription?

I genuinely do not understand why anyone would pay OpenAI for this. Running something comparable to Jev is pretty trivial. The whole point of paying for ChatGPT is because OpenAI has a bunch of warehouses that can run a zillion-parameter model.

Running a decision model is way easier and much cheaper. Are they really just trying to capitalize on the hype here? It feels like they really have absolutely zero moat.

Well, Jev doesn't meet any real compliance requirements but OpenAI's models do. So even if Jev is faster, any customers with compliance needs will obviously pick OpenAI because they can't pick Jev out of necessity.

They can follow up in N weeks with a better one. Even if your eval is true and it’s worse, planting a flag makes sense. Some people will just use OAI because it’s OAI. No one will remember their week 2 evals in a few months.

I think releasing something like this makes sense even if it's underbaked, it's still very cheap, and if you have existing enterprise OpenAI relationship it's a lot easier to onboard something like this than set up a new Jev contract.

How these models play out is an open question but existing provider contracts and T&C are important for enterprise.

i would be shocked if luna decides is less generally capable than jev. jev has failed to understand any novel domain I've given it. i have found that i use it only when "some data is better than no data"

  1. Title: "Mortgage calculator: estimate your monthly payment (Bankrate)"
     Tag:   "house hunting"
     Jev:   0.69 (yes), 0.72 (yes)
     Luna:  0.21 (no),  0.21 (no)

  2. Title: "S&P 500 index: live chart and news (Bloomberg)"
     Tag:   "investing"
     Jev:   0.91 (yes), 0.90 (yes)
     Luna:  0.56 (no),  0.56 (no)

Why would I run it myself? It's $0.10 per million tokens. Dirt cheap. (Jev is even cheaper.)

You could ask the same question about why anyone would rent a VPS. I can just run my own hardware, it's just a computer!

Buy vs rent is not just about what's possible, it's about what's economic.

For my use case it will cost like $11 a month and we already have OpenaAI keys and accounts with billing in place. I don't want to run my own model infra and I don't want to get permission to set up an account with typesafe.ai

Depends on the quality of the results. These things are driven by text prompts. If it turns out the OpenAI one returns better quality results than open weight variants they'll be rewarded by the market.

Anyone using a decision model like this is going to have to spin up their own evals - these are far harder to vibe-check than regular text output LLMs.

My opinion is similar, but for a different reason: every use case for decision models that I can think of, I don’t want the model to change in X weeks when the lab decides to “improve it” or “make it safer”.

There isn’t a moat in the sense of self hosting but you need a reason for people who don’t want that to stay on your platform. Customers save time and effort managing payments easier this way. However it’s a race to the bottom price wise.

Going to be all about branding and platform stickiness for OpenAI to make investors and creditors whole.

Existing enterprise contracts? Data retention contracts (some have zero data retention contracts)? Staying with a single provider because it's easier to have everything in one place?

There are probably a lot more reasons.

If you're in an enterprise that already has a procurement agreement with OpenAI, this means you don't have to onboard another vendor. Bucket platform strategy.

If you work for a company that has a 3 to 6 month onboarding period for new vendors and a lifetime commitment to maintain a whole bunch of vendor management horseshit for as long as that relationship exists, it makes a ton of sense.

Add in a bunch of model governance and oversight for anything you train yourself and it’s pretty much a slam dunk deal.