The crazy thing is that nobody knows exactly who is behind it — just a cryptic name of Ox Alpha.
But it’s already scoring amazingly in several benchmarks — even beating Claude Fable and GPT-5.6 Sol in critical areas.
An initial evaluation using 10 DeepSWE coding tasks found that Ox Alpha completed eight, producing an 80% result on that subset. In the same comparison, Claude Fable 5 scored 65%, GLM-5.3 62%, Grok 4.6 62%, and GPT-5.6 Sol 52%.
Some people think it’s Gemini 3.5 Pro or 4, others are saying it has to be GLM-6.
But they’re literally offering the first 100 trillion tokens of usage for free — so clearly they have to be a huge company with deep pockets.
Backed by a provider claiming an unprecedented serving capacity of 100 trillion tokens per day, Ox Alpha offers near-unlimited free inference at an industrial scale that allows developers to run massive agentic workflows and long-horizon tasks without compute bottlenecks
And of course it has a 1 million token context window — something that’s quickly becoming standard across models.
Maybe they temporarily removed the “safeguards” to avoid being detected like this? 😅
One of Ox Alpha’s biggest features is its 1,048,576-token context window, alongside an output limit of approximately 131,072 tokens.
So it will have more than enough room for all your massive codebases.
That makes it particularly interesting for AI agents. Developers can feed the model substantial software repositories, documentation, issue histories, and specifications in a single context.
It also has full multimodal reasoning.
Ox Alpha isn’t limited to text. It natively supports text, images, and video, alongside tool calling and structured outputs.
So for example you can provide a UI wireframe and ask the model to implement it, or feed it a video recording of a software bug instead of explaining the problem manually.
It also uses the sophisticated Mixture-of-Experts (MoE) architecture — which let’s it achieve the intelligence of a massive model at a fraction of the computational cost and latency.
By deploying a Mixture-of-Experts architecture that routes tokens through approximately 40 billion active parameters out of a massive 744 billion total parameter pool, Ox Alpha delivers the world-knowledge capacity of a ultra-large model with the high inference speed needed to process up to 100 trillion tokens daily
I’ve really been noticing this growing trend of releasing stealth AI models — and I can definitely see why they do this.
When you don’t know who exactly is behind the model, you’re less likely to suffer from a preconceived bias of models from a certain company being better or worse.
Releasing a model without revealing its creator gives AI companies an enormous blind test. Developers benchmark it, discover weaknesses, stress-test its infrastructure, and compare it with competitors without being influenced by the company behind it.
And of course it’s also great for marketing — especially in a case like this that the model is doing particularly well — like a mysterious genius that every is going crazy about.
It also creates powerful marketing. Instead of announcing Ox Alpha with a conventional launch event, its creators have given the internet a mystery to solve.
It’ll be really interesting to see who the creators end up being and what more they have in store for us and the AI race.