Some thoughts on OpenAI returning to open releases
And welcome to my extra uneditted thoughts blog.
Back when I changed jobs from HuggingFace to AllenAI in the Fall of 2023, I had a few conversations with folks at OpenAI and I had heard of substantial discussions back then of trying to re-up their relationship with open (source) AI developments. Today, a year and a half later, OpenAI is prepping for their first open-weight language model release since GPT-2.
This is great. It seems like they’re doing this earnestly, even if the timing in the change of heart may look political. The most telling reason as to why they’re doing this came from their COO Brad Lightcap:
Primarily because developers, along with many of our business and government customers, have asked for it. We serve millions of developers every day – many rely on a mix of proprietary and open models to build AI products. We’ve provided frontier models through our API since 2020, but we’ve also released models like GPT-2 and Whisper to the open-source community.
Whisper, the speech recognition system, is a super impactful open release. OpenAI largely definitely knows what they’re doing here. The rest of the thread from their COO is very telling:
As models improve, there is more and more demand to run them everywhere. Through conversations with startups and developers, it became clear how important it was to be able to support a spectrum of needs, such as custom fine-tuning for specialized tasks, more tunable latency, running on-prem, or deployments requiring full data control. While we will continue to offer frontier models via our API and in ChatGPT, there are many scenarios where APIs alone won’t fully enable developers to build in the place, or in the way, in which they’d like.
Our goal with this open model is to address exactly that: expanding developer access to powerful AI, while continuing to maintain high standards for safety and responsible deployment. We think this is important for both the US and the world, especially as AI becomes foundational to the world’s computing infrastructure.
OpenAI has a feedback form for how they should manage this, which many are mocking, but it’s clear they’re trying to learn which type of use-cases support a open models approach that API models don’t serve. This is an extremely bullish sign for the open community and not just OpenAI trying to collect random information because they can.
The beginning of 2025 has marked the highpoint of open weight models since ChatGPT, as I discussed at length in my analysis in the main feed on Gemma 3 and OLMo 2:
I recall this section conclusion:
The biggest stories in open-source AI in 2024 often felt like bickering about definitions. I wrote a lot of articles about definitions. Llama 3 was pretty much all we had to get excited about. At the end of the day, even with how much I think it would be better with more information on the whole stack of AI development, open-source is largely going to be defined by community norms. For now, Llama weights have been that norm rather than other definitions.
By comparison, 2025 feels poised to be about actually building open AI. We have had surprising, impactful, and exciting releases and it’s only March. We know Meta is looking to get back into the conversation with Llama 4 in April at LlamaCon. We have our open-source ChatGPT. We’ll have more we can’t predict.
Crucially, on top of the gap being smaller, all of these open models are crossing meaningful boundaries in performance. When model capabilities made the leap to GPT 4 class models, tons more applications were possible. Now, we have GPT 4 class small models that can be deployed in privacy-conscious ways. There’s been a huge demand for this, and the ecosystem is slowly building the tools to do so. Yes, closed AI will continue to march forward, but open solutions need to prove their own independent feasibility.
In the long march of progress, open-source AI feels far closer to an inflection point of proving out the hypothetical benefits we have focused on for a few years. Transparency, privacy, better performance, etc. could actually all be happening this year.
It increasingly feels true.
I’m giving OpenAI and others looking to get into open models the following advice:
Be careful with the open-source vs. open weight nomenclature even if its annoying. The open-source focused users and community members have a point and their efforts with the broader ecosystem are worth supporting until we have answers. Blatantly, you don’t want to make enemies with a release.
Ecosystem integrations being handled professionally, such as VLLM, Llama.cpp, etc. are crucial.
Licenses matter a ton for adoption (here are Sam Altman’s comments on the matter). Non commercial models are going to matter less and less. This factor is increased as the pace of progress is so high because having a good license increases the chance that people adopt your model in the time before the next state of the art open release comes.
All in, it’s a major moment for the open ecosystem that the biggest name in AI is rejoining it. It’s the biggest validation we’ve gotten that building with open models is a needed way to solve certain types of business problems and the solutions will provide real value.
Excited! As with all things in the world, we can’t give them credit until they happen. We can reflect on motivations and give advice, but impact and credit comes later.
What will they release?
Some basic logic for why OpenAI will probably release an ~30B param reasoning model with MIT/apache.
* OpenAI will only release something clearly SOTA in size category
* Will release a reasoning model, else hard to tell story around popular evals
* Want people to use it, cant be too big
* Don't want to compete with API models, can't be too big
* 30B is loved by people finetuning models (can do on one node) + running inference, but still gives some nuance of bigger model feel
* I expect the architecture to be much simpler than their internal models, maybe even based on Qwen / Llama to not reveal secret sauce
* Curious if they go knowledge distillation route like Gemma
* MIT / apache license undercuts Google and Meta with their weird licenses
* Not too sure about no base model, but pretty sure about no data.
competition is gemma 3, qwen 2.5 (or 3 soon), mistral 3.1.
Without the 'secrect sauces', I don't see how OAI's open model will win against Qwen-3 by a meaningful margin that encourages adoption.
It's like Gemma-3 is slightly better, but nobody cares, due to the environment and community support for Qwen/Llama models
But as you said, they not gonna spill the 'secret sauces', like new long context arch, quantized training, new training losses (like MTP), sparse MoE, etc.