Robert Ta introduces OX Alpha, a new model with a 1-million-token context window available free with no training on user data. In a real bug-fix test, OX Alpha produced three times fewer output tokens than Fable 5 for the same task, suggesting major cost savings for enterprises running large engineering teams.
OpenRouter on X. New stealth model drops. OX Alpha. OX Alpha is a frontier model built for efficient coding, sustained agentic work, and real-world production use. 1 million token context window, text, image, and video input. Try it now and share feedback to improve the model. Notes for this stealth model. It is free. This time the provider does not train on your prompts or completions. To me, that's a signal from market. Which is people are hearing, we want to keep our IP, our IP. I'm a business. I'm buying your intelligence. I want to keep my IP, my IP. And you saw that botch with the Fable 5 release where they kind of went against their zero data retention deals by collecting data under the guise of safety. It might be safety. I don't know. They have more data points that they're maybe not showing the public, but that's, that's what I see. Brian says this model is 3 Gemini Flashes wearing a trench coat. Says this is probably Thinking Machines Lab's next version of Tinker. It's not a Chinese model as it speaks freely about Tiananmen Massacre and Taiwan. Tinker is one of the few large open models which is highly multimodal, including video input. This one is as well. When asked directly, it often says it's GLM or another open Chinese model and shows traits of Chinese models. A previous version of Tinker was distilled from Kimmy, so it would make sense if they distilled post-trained GLM for the next iteration. For those that don't know, this dropped this week as of the podcast recording, and it allows for multimodal. Let me just show some of the benchmarks. So OX or OxAlpha benchmarks against the competition. This is how it looks at a high level. The ones it's bad at, SCC bounded memory spilling, Vulture persistent analysis catch. I would say nobody's really solved memory. But against Fable 5, 5 tasks, OxAlpha had the edge. 3, both solved cleanly. 1, Fable 5 had the edge. 1, neither solved. Against GLM 5, 5 tasks, OxAlpha had the edge. 3 both solved cleanly, 1 GLM-5 had the edge. Against 5.6 SOL, 7 tasks OxAlpha had the edge, and 1 both solved cleanly, 2 GPT-5 had the edge. And versus Groq-4, 4 tasks OxAlpha had the edge, 4 both solved cleanly, and 2 Groq-4 had the edge. Very interesting to see that they don't even bother benchmarking against Opus 5 in their, like, flagship article here. They probably think it's so crap, like, we don't even need to benchmark against that crap anymore. Let's just benchmark against the other ones. That's Also just a little bit of a dig. This is really interesting. For anybody out there that's using OxAlpha, use it while it's free. This is a post from Klein. We benchmarked OxAlpha versus Fable on a real bug from the Klein repo. Both fixed it correctly, but we found that Ox used much fewer thinking tokens. Most reasoning models loop and rederive the same conclusion over and over before acting. Fable 5 said, I found the root cause 7 times before editing. Aux stated it once, then wrote the fix. Roughly 3x lower output tokens for the same work. If you're a CTO, if you're an engineer, a staff engineer at an enterprise company looking to token optimize, not token max, from earlier this year, how everybody's budgets are affected now because of the casino gambling that feels like using tokens with AI across your engineering workforce, especially if you have hundreds, thousands of engineers, really What the enterprise meta is finding is the harness matters a lot. And now it looks like some of these open models or open weight models, open source models are catching up to frontier level intelligence with real world use cases. In the All In podcast, a couple of the guys there talk about how they've been using, I think most of their compute and inference is routed through open source or open weight models with really good domain specific harnesses. And they're seeing better results as time goes on. So I think that's a really interesting part of the meta that I'm finding. Jonathan, what do you think of that? Like token optimization and then this Ox Alpha model and the implications of that for large engineering teams?
Yeah, it's definitely something to keep an eye on. I'm really curious about the methodology they use to build this and post the benchmarks and people use it in the field. You know, that one example was good, but just like how it performs in general tasks and how usable it is. I'll be really looking for people's experiences to share, you know, about how well it serves them. And I'm just really curious too about like what company this came out of, you know, if it's not a Chinese model. I think the tinker thesis might be a good one, but yeah, it could be somebody that we've never heard of before also. And that's kind of an interesting thing.
