Decisions API is in public beta

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...

• BoorishBears · 7 hours ago

  Decisions voice isn't a product for anyone else who was confused: it's a canned guide for hooking up a voice model to the decision model browser use thing

How is the pricing vs Jev?

• jerrygenser · 12 hours ago

  $0.10/mm input vs. $0.042/mm input. Both free output.

• Topfi · 11 hours ago

  In the same bench a full Jev run cost USD 0.0192,- vs Luna at USD 0.06,-, both via OpenRouter today. So about 3x in favour of Jev.

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

Can we use this through subscription?

• OutOfHere · 10 hours ago

  No. The OpenAI subscription has never covered any API calls. The closest you can probably use via subscription is to get structured outputs via Codex.

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

• boltzmann64 · 4 hours ago

  have you tried Moondream? https://docs.moondream.ai/

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

• binlog · 12 hours ago

  One of the examples on the docs page is it playing a video game. Doubt it’ll be able to run anything complex though. You’re simply trading accuracy for speed.

• babelfish · 7 hours ago

  Get an LLM to build it and report back! Cool idea

• swingboy · 18 minutes ago

  I think the hardest part would be coming up with the instructions/inputs to send for every non-trivial action?

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.

• mediaman · 11 hours ago

  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.

  • dvt · 11 hours ago

    Yeah and OAI is twice as expensive as Jev, which is kind of my point. And more expensive than open models, which you don't necessarily have to host yourself. Pure bandwaggoning.

  • lelandfe · 9 hours ago

    For one, to remove network RTT

• TSiege · 11 hours ago

  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.

• simonw · 11 hours ago

  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.

• tmhall · 11 hours ago

  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

• super256 · 11 hours ago

  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.

• jcims · 11 hours ago

  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.

• csharpminor · 11 hours ago

  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.

• drdexebtjl · 10 hours ago

  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”.

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.

• gobdovan · 11 hours ago

  > 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. Hopefully people will flock to whatever product is making its mission to be commodity and the easiest to replace. Really don't want another free ingress, 100$/TB egress Cloud situation.

• tripleee · 11 hours ago

  It takes me all of 2 keypresses to switch models. I don't know of a less sticky product

  • st3fan · 11 hours ago

    If you are a business dealing with with anything remotely sensitive then this is not so easy and you are basically forced to do business with a big player.

  • schleck8 · 10 hours ago

    You've not seen how long it takes to switch an enterprise claude subscription to github copilot or vice versa with all the compliance and shareholders

    • htrp · 10 hours ago

      isn't that a problem for large enterprises?

      • mikestorrent · 9 hours ago

        It's a problem for the security, legal, and IT teams, but not that much for developers, unless they get really particular about their harness. On the other hand, these are the high dollar value accounts that providers want to keep; but if there's no reason to avoid switching, this market really will feel like a utility market (i.e. it'll be like switching ISPs or cell phone providers - annoying but fungible). The AI companies want to differentiate and become something more than a commodity, even if it's as critical as a utility is.

      • judge2020 · 9 hours ago

        Enterprises tend to be your largest customers. Especially when current stock values for tech companies are majority predicated on AI becoming a staple in everyday life.

    • skissane · 8 hours ago

      A lot of places already subscribe to both. Indeed, in large enterprises it isn’t uncommon to simultaneously subscribe to Copilot, Claude, OpenAI, Cursor, AWS Bedrock, Gemini, etc — you might subscribe to different ones for different teams/projects/employees/etc, but often the approval by legal/IT/etc is generic not scoped to whoever is using it right now If you are charged based on usage, you can “soft switch” between them really quickly.

      • dozerly · 7 hours ago

        A lot of places just pay the token cost of the usage, so they sign up to all of them and lets the winner win.

    • afavour · 8 hours ago

      Comes to something then OpenAI’s best hope is to essentially become the next Oracle.

    • killingtime74 · 7 hours ago

      Not sure where you work but at ours we have api access to all of them and can freely switch.

      • saghm · 7 hours ago

        At my job people discuss almost weekly what the best models are for various price/quality points, with our CTO in particular wanting us to make sure we're being cost-effective in terms of what we're using for a given task (his words are basically "I don't want you to use these tools less, I just want you to use them effectively")

      • yieldcrv · 5 hours ago

        I’ve seen enterprises have access to all the harnesses but not all the models within them and they ask dumb followup questions after 7 business days when you want different access

      • seventhtiger · 4 hours ago

        No DLP concerns?

        • killingtime74 · 4 hours ago

          I think they have zero data retention contracts with all of them.

          • akie · 3 hours ago

            Yes, they'll throw it out after they trained on it ¯\_(ツ)_/¯

            • whateveracct · 2 hours ago

              they don't retain the data! just the features

    • mathisfun123 · 5 hours ago

      Lol at my $job we have one token quota which can be used with all the frontier models.

    • toomuchtodo · 1 hour ago

      We built our harness around swapping endpoints on demand. We can send to one model, many models, cloud or on prem infra. Certainly, not everyone has, but this is the future imho.

  • johnfn · 10 hours ago

    This implies you didn’t run any sort of evaluations? It is not realistic for any sort of production use case to do this.

    • shermantanktop · 9 hours ago

      Agree. Switching models with a keypress is for developer coding. Jev and this decisions api are mostly useful for inference at scale in a workload where cost and latency matter… and that’s where evals become crucial. Could coding tools use it? Sure, but that’s probably a special case.

    • majormajor · 6 hours ago

      My gut is that the market for "ai as a tool" aka Claude Code/Codex/computer use/etc is a significantly bigger one than "models behind the scenes of some service". I've seen people use evals a lot in the latter case and very little in the former case (outside of people's whose job is basically to review the new releases). I haven't personally met anyone with something like "here are a bunch of tickets + a snapshot repo checkout, please try to solve them all" eval approach. Though honestly I'm also surprised by the hype around Jev from a POV of "wait, are so many people just building on these by using them for classification tasks vs something more multi-step or generative?"

    • ajmurmann · 5 hours ago

      For every production use case I build a eval suite which I use for prompt tuning and model evaluation and config. How else do you establish your model and prompt combination works? How do you upgrade to a newer model or decide on a fallback? This same suite can simply be run very handoff to switch to a new prod model.

  • TSiege · 6 hours ago

    Not only can you switch models easily, but their competition can easily duplicate their product. I'm not sure stickiness has been found yet, but my guess is being more of G-Suite for "intelligence" than being a token hawker.

  • TZubiri · 2 hours ago

    well, it's a function with the string(string) type signature, so it's bound to be weakly coupled, it's literally stringly typed. If there's efforts to build vendor lock in, it's going to be in the surrounding api, whether it's streaming responses, or this weird prediction thing, or temperature settings, seeds, stuff like that. And even then, competitors can copy the interface because interfaces are not copyrightable.

    • wonnage · 1 hour ago

      The only way they can get lock in is with the data. Right now it’s like a return to the early Web 2.0 days where everyone had an API. You hook the harness up to Slack, Gmail, GitHub, etc. so it can do work. And like Web 2.0 I’m sure the day will come where the walled gardens return. All the old SaaS companies are starting to charge for agentic access. Proportionally more of the AI budget will go to them instead of the model providers, who are still stuck in this price war

• aprilthird2021 · 6 hours ago

  But OpenAI and Ant can be in more than just the commodity part of the business. They can both make frontier models and sticky products on top of those that have network or other effects that make competition tough.

  • TSiege · 6 hours ago

    The question isn't whether the market is big enough. It's whether at a race to the bottom on prices is sustainable and worth what investors expected it to be. The original story of OpenAI and Anthropic was that it was a race to super intelligence and whoever gets there first basically captures all the money in the economy. That kind of investment pitch means missing out not just on big returns but perhaps infinite return on investment. It seemed like it'd be true when the market was young and the cost to compete was prohibitive, but now anyone can compete with open weight models that are cheaper and maybe not the best, but are good enough. You raise $200B to be a high margin low capex business, not an industrial commodity producer with high capex and margins being set by competitors who can duplicate your product and undercut you on price.

    • fastball · 5 hours ago

      They are still gunning for ASI.

• totetsu · 3 hours ago

  Is making their product sticky perhaps what all the talk of supply chain security is really motivated by?

• jiggawatts · 2 hours ago

  > 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. My approach would be per-user (or per-project) "memory". I don't mean a tack-on like a RAG / document store with "notes" added to it in the background, but an actual medium- and long-term memory. Something like an extension of the KV cache stored in High Bandwidth Flash (HBF), or a subset of the weights trained "online", similar to LoRA. This would not be transferable to any other base model, so would be excellent "lock in". The downside is that the memories likely wouldn't be transferable to new models either, but I can imagine solutions to that too. I.e.: Train an MLP to "translate" from the old memory weight space to the new one.

• TZubiri · 2 hours ago

  >nail in the coffin > is .. all people want. Try making more measured statements, you exaggerate your point and end up being wrong. Of course this new barely used product feature is not all people want. Of course this minor product feature is not the nail in the coffin of whatever argument, they are just responding fast to a smaller competitor by providing that feature themselves, something that happens thousands of times in business, did Instagram prove it was a commodity when it implemented reels by copying tik tok? Did Uber prove it was a commodity when it started offering food delivery? Doesn't make much sense.

• wonnage · 2 hours ago

  It’s interesting that both companies have fully embraced agentic development with unlimited token budgets and yet still can’t expand beyond the original core products All of those companies that naive HNers used to say they could code in a weekend literally can be coded in a weekend now, so why haven’t they? If enterprise is the goal they’re really at the whims of the old school software companies because they own no data of their own. Imagine Google decides to push Gemini one day and now ChatGPT can’t write docs anymore.

• tomaskafka · 10 minutes ago

  If somebody from OpenAI or Anthropic is reading this, one way to make the products stickier would certainly be to not make the desktop apps a complete shitshow. Maybe they could for example point their amazing models to their GitHub issues, and use their power to actually fix the bugs and myriad of papercuts before solving cancer and world war/peace/whathever the stockholders believe they want.

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 )

• chupchap · 8 hours ago

  How is this different from the categorisation models from ML era?

  • sethaurus · 8 hours ago

    The pitch is that it's a fully-general model, so you can skip training/tuning/selecting a particular categorisation model for each task.

    • chupchap · 8 hours ago

      That's great! So someone finally built the zero-shot model from the sales decks of 2015 =D

      • ehe78qhe · 5 hours ago

        Specifically, it happened a few weeks ago when Typesafe released Jev; this is OpenAI's competitor to Typesafe.

        • weird-eye-issue · 4 hours ago

          This isn't really anything new it just seems like a new API but you could do the exact same thing with just a little bit of prompt engineering all the way back when GPT-3 was first released. Am I missing something?

          • sweetjuly · 4 hours ago

            No amount of prompt engineering will give you the true probabilities for the model producing a certain response; this is something you can only get by inspecting the internal state at inference time.

            • weird-eye-issue · 3 hours ago

              For most use cases is that actually needed though? Just having it choose between predefined responses seems like enough but I'm curious about specific use cases because I do feel like I'm missing something

              • ehe78qhe · 3 hours ago

                This is useful for classification problems; any time you need to write software that looks at some fuzzy data and needs to make a probabilistic decision. It's far more cost-efficient and performant to use this type of model instead of an LLM. Before now you had to train a model on your specific classification problem, now these new models don't require any specific training at all to do pretty well on novel problems.

          • Closi · 4 hours ago

            It's much faster and cheaper (an order of magnitude). And theoretically will give you better answers statistically as it's calibrated.

        • WASDx · 2 hours ago

          Specifically, it happened over a year ago. Jev just hyped it up. https://laya.convaiinnovations.com/

          • mi_lk · 2 hours ago

            I don't know if they invented it since I've seen other papers floating around. All about marketing that's why it's hard to root for Jev's success

  • mogili · 6 hours ago

    These are essentially zero-shot classifiers; they don't need to be trained for a specific classification task. You could include some natural language context on the rules for classification and it should get good enough accuracy.

    • olalonde · 54 minutes ago

      How is it different than just asking an LLM with structured outputs enabled? Is the primary value-add that it gives a confidence level?

      • pegasus · 50 minutes ago

        Mostly, they are much faster and cheaper.

      • mkotlikov · 49 minutes ago

        1) you can parallelize the request 2) since a structured output is still just text then every previous property of the structured output effects subsequent ones

• Bassilisk · 5 hours ago

  >defecto standard for other providers. I know it's just a little typo but it made my morning :)

• zackchen · 23 minutes ago

  Why don’t the probabilities sum to 1? Am I missing something? Even with rounding errors the probability would reach 0.98 max.

• zackchen · 23 minutes ago

  Why don’t the probabilities sum to 1? Am I missing something? Even with rounding errors the probability would reach 0.98 max.

  • zild3d · 12 minutes ago

    they are separate questions about the input. One question can be "is this a complaint?" and another "Should we flag this email to security" and a third "Is the sender a Bush era republican?"

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

• OutOfHere · 10 hours ago

  All this is for extraordinarily simple decisions. Real world problems often are a lot more complex requiring highly structured outputs covering many output attributes and substructures, for which a conventional structured output via a documented schema is better. If instead you make twenty independent calls to a decisions API, you lose coherence among your twenty decisions. I think any hype surrounding decisions will be forgotten soon enough.

  • devin · 9 hours ago

    I don’t think so. People want more determinism and this is just another step in that direction.

    • lofties · 7 hours ago

      Can't wait to read about BERT on the front-page!

    • OutOfHere · 5 hours ago

      Deterministic predictions are sought by those looking to offload their decision responsibility to AI, whether to lower perceived legal risk or otherwise. This is fake risk reduction, i.e. "risk theater". Unfortunately, a deterministic prediction utterly fails to yield an uncertainty measurement which is critical to have in actual risk reduction. If you want the variance in measurement, it is vital to obtain multiple measurements. This also gives a confidence interval.

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.

• oh_no · 11 hours ago

  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.

  • Topfi · 11 hours ago

    Good point, commercially, being an existing partner is always easier for adoption. Heck, why I'd like to get Mercury Decide to replace Jev myself, rather than one than two to work with. Still surprised they even leveraged Luna for this. Given their resources in data, compute and manpower, would training a decision model from scratch take that much longer to not make sense given the cost, compute and performance advantages that would likely provide? 3 times more expensive at twice the latency with lower performance is a tough sell, though yeah, prior relationships will likely smooth some of those deficiencies over.

    • oh_no · 10 hours ago

      yeah and this isn't a long term solution, stand this up, see what value you get out of it, and in a few months you cans witch to whatever the best decision model is

• nostrebored · 11 hours ago

  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"

  • Topfi · 10 hours ago

    Very task-dependent of course and mine are unique to say the least, so could see Luna being better in certain domains, even if my measurements have not shown that yet, happy for anyone to show otherwise. For what it's worth, ran every task twice on each model, most were for some UI component synthesis and charting insanity that is a bit hard to explain, but some were simple tag selection, basic noul at threshold 60%. Essentially, whether to use the provided tag given the title of a browser tile: 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) Of course, tags can be a bit subjective, but in these cases, I'd argue the values provided by Jev were far more representative of my subjective assessment over Lunas. If SnP stuff on Bloomberg isn't investing, nothing is. Goal for tagging is mainly a near instant, over writable, sane default provided to users in the background. Resolve the whole "I love using Notion/Obsidian/PKM software of your choice but spend 80% of my time just thinking about the ideal tag before starting to read" issue. Lunas output is not really helpful here.

    • brianyu8 · 5 hours ago

      I tried reproducing your examples as predicate questions on the Decisions API ( https://gist.github.com/by-openai/7b376d7866e36a4f38581f73ce... ) and got: 1. Title: "Mortgage calculator: estimate your monthly payment (Bankrate)" Tag: "house hunting" Decisions API (2 runs): 0.99, 0.99 2. Title: "S&P 500 index: live chart and news (Bloomberg)" Tag: "investing" Decisions API (2 runs): 1.0, 1.0 If you have other examples of requests with unexpected outputs, feel free to email me at by@openai.com and we can try to get to the bottom of it. Thanks for trying out the API!

• Shank · 11 hours ago

  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.

  • jasonjmcghee · 9 hours ago

    Or even just- they've already procured OpenAI. That's a big motivator.

• scosman · 11 hours ago

  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.

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

• chr15m · 10 hours ago

  Clef can also do this.

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

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.

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?

> 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.

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

• mikeryan · 9 hours ago

  Manufacturing quality control. There are automated tools to pull things like dented cans off a line.

  • MiroslavPokorny · 3 hours ago

    So AI is a trillion dollar industry and one of the biggest thing it can do is spot dents on a can ?

    • progx · 1 hour ago

      Imagine a worker in a car factory who inspect the car's body. Pictures from all angles and AI inspection is faster, cheaper more thorough and works 24 hours a day. And that is only 1 scenario.

Just going to drop this here: https://jeffyclassify.com/ Open source classifier models you can run and train locally on CPU

If we compare this with using the older solution of writing a prompt to find out the answer of the classification - Cost : It is the same for both scenarios $0.10 per 1M tokens - Speed : decisions is 10x faster than responses API - Quality : I guess if we compare with luna which is a pretty good model it itself, both will be at par So essentially it has to do more with speed vs any other factor.

• rockinghigh · 8 hours ago

  With decisions models, you don't pay for output tokens. Also this OpenAI API is multimodal.

  • ashu1461 · 7 hours ago

    Output tokens anyway will be minimal in a decision scenario, so even if you use responses API the cost will be less.

    • dorkitude · 5 hours ago

      Not exactly. System 2 style reasoning to follow instructions can be expensive and counts as output tokens.

Have there been any signals from Anthropic about matching this? We use AWS bedrock and just switched to Anthropic from OpenAI because of the ZDR guarantee. Would be great to not have to entertain switching back.

• mattnewton · 6 hours ago

  Why not try Jev?

• chaos_emergent · 6 hours ago

  Feels antithetical to their big model bitter lesson strategy

• binlog · 6 hours ago

  > just switched to Anthropic from OpenAI because of the ZDR guarantee Did you get that reversed? OpenAI has a ZDR guarantee while Anthropic doesn't.

How well calibrated is it? Is 90% calibrated to be correct 9 times out of 10? I think Jev had put significant effort here and its not clear if luna will be well calibrated in this way.

It’s interesting they skipped caching. I could see wanting to ask follow up questions so having your first x tokens in cache would be interesting. Also if you have a long “system prompt” then caching would have saved a considerable amount on bulk data processing. There may well be a technical reason I don’t understand.

• BoorishBears · 7 hours ago

  I'd imagine it's chasing the lowest possible latency

  • sprobertson · 5 hours ago

    Wouldn't caching be in favor of lowest possible latency?

    • Kubuxu · 3 hours ago

      It might be faster to burn the compute instead of having to fetch the KV-cache over the network from an SSD.

Note that, being that this is gpt-6-luna under the hood, this offers you 1m token input window, and multi-modal (image) input. In my testing so far, I'm seeing 160-175ms end to end. Worst 5% 285ms, worst so far was 743ms.

• copperx · 5 hours ago

  Image input? Wait a second; so this can be used for image classification without any kind of training at those speeds?

  • brianyu8 · 5 hours ago

    Yes! Check out the 20 second mark of this video for a demo of how fast image tasks can be: https://x.com/OpenAIDevs/status/2107573382229188645

But... Can it draw a pelican on bicycles?

Every time something comes during the AI bubble we get a wave of me-toos. I think the Jev wave is noticeable for how muted it is. The fact that this keeps happening demonstrates there is no moat. The fact that each wave gets a little less attention demonstrates there is no killer product here.

Is there a mention of context length? I can't find it. I could not integrate Jev due to limited window (64k?).

• lifeisloving · 3 hours ago

  Someone said 1mm

Boy am I glad we're already dropping the "noul" term for a binary decision

• bargainbin · 3 hours ago

  Typesafe’s biggest blunder by far, they could have at least set the standard and left their mark that way.

• WASDx · 2 hours ago

  And "system one".

• a_c · 2 hours ago

  Naming is hard. I still don't know what noul means. I don't mind calling a probabilistic boolean as boolean at all.

  • reddalo · 1 hour ago

    Exactly. We already have "boolean", why come up with a new random name that doesn't mean anything?

Overpriced crap, local models are better than this, it's also dumber than Luna for some reason, and Jev is definitely ~2-3x cheaper than this. I am sure people will be able to use it, but if LLM progress is anything like before we will have Jev 2 in about a month. Training a local model like Jev with some learnings that can be extremely cheap to host shouldn't take that long either. So I honestly don't see the point of this, other than to put something out. Although that does seem like OpenAI's strength turn around slop products and iteratively improve and try to out compete others in everything. Only 2 things they have clearly given up on are Video models(no moat, copyright nightmare) and Music. And it makes sense why. I feel like they will compete with even the no-name Dog, if the Dog launched a successful marketing video of an AI product. I have seen this often in SF startups, heck I work for them, but man this is extreme. But honestly all I see are long term price wars, I don't understand how this is a sustainable business strategy. Or maybe that's the point... Who knows.

To quote Bruce Willis "Welcome to the party pal" It seems this one caught openai on the back foot, and this is a scramble to maintain parity. The company is clearly still innovating towards AGI rather than asking "what do people actually need?" Despite once being the darling of AI it's - lost it's models' performance edge, and got too many similar offerings - continues to launch products without a market or isn't done better using other tools (e.g. dots) - despite having AI can't lock down it's own products showing lack of skill - focuses on solving maths problems humans can do for tests, when real world problems - disease, materials, energy research etc is all outstanding - abandoned it's open model and open source programmes, despite Google, and multiple successful Chinese, and now European companies make their frontier models open weight. - pissed off a portion of its non corporate fan base by killing GPT 4o instead of recognising the brand and product attachment as an opportunity - launched laughing stock projects like being able to actually call chatgpt on a telephone number (wtf!) - fails to capitalise on market segments like an AI that can provide corporate network sentry duties It's increasingly looking like the company has jumped the shark and if I was an investor would be asking questions as to why it actually took so long to bring a jev like product to market, and why they are labelling something that is a simplification of existing models as "beta". The whole point of AI as I see it is to make our life easier and answer the questions we can't. It isn't to make an AGI so powerful that it can replace us. Along the way, that goal was forgotten, but it's not been forgotten by the new startups.

If you happen to have an nvidia RTX 4090, you can try my fork [1] to have a JEV compatible decisions endpoint with qwen-3.8-27b while simultaneously serving a fast chat endpoint (chat completions, responses, anthropic compatible), both sharing the same base weights and both with dynamic LoRA loading. This means you can essentially serve many fine tuned variants at the time on one consumer GPU. I still need to upload my decisions LoRA to huggingface so you don't have to train it yourself. I should probably also add support for this new openai decisions API format as well. [1] https://github.com/tensorninja/ninfer-4090

• nunodonato · 1 hour ago

  isn't 27b too big (slow) for a decision model? I think they should be as fast as possible

  • netsroht · 46 minutes ago

    I'm getting about 3k tok/s on prefill so it heavily depends on the input size. I hooked it up into a coding agent for shell permission checks, estimated shell execution time (buckets), goal pre-screening, subagent routing etc. and I have RTTs between 100-200ms. It can take noticeably longer for huge contexts because of the prefill speed but it can be cached so subsequent calls will be much faster. I tried smaller decision models like laya (also with custom finetunes) but the accuracy was not really good (for the things I tested). Also I don't have any VRAM left on this GPU so i had to decide whether to host laya or qwen-3.8-27b but not both at the same time. Running decision models on a CPU will also be noticeably slower so I went down this route to have both combined with shared base weights.

> gpt-6-luna is the only model currently available. Clearly this was a rush job to respond to the competition. I am more curious about how the dedicated model will perform after they've had time to do it the right way. The probabilities I am seeing so far do not correspond with figures the business would find very agreeable. The hidden danger with this could be demonstrating how thin the veil actually is. We may wind up reducing confidence in decisions simply by making their probabilities visible. Some kinds of information are quite hazardous.

At this point I just want chatgpt plus and claude pro to have API access

Feel I need to play around with these new breed of classifiers so came up with this personal use case yesterday whilst at gym: "If update from select group of individuals on WhatsApp is classified as urgent then interrupt my music." Doable?

• search_facility · 2 hours ago

  Yes, why not. "If then ..." Is for "harness", but the answer to prepared question is for jev-like cheap and fast service

This is more a great signal marketwise than anything else. But still this is confirming the buzz.

Let's see if Google also releases something similar I'm mostly waiting for EU endpoints