Bytez

How Bytez 0.1 works: Think of an exam. 1,000 students in the room — each one the top mind in their field. Lawyers, doctors, engineers, artists. Teacher asks a question. Everyone writes their answer. Our model gets to cheat. It sees all 1,000 answers. It figures out what kind of question was asked. Legal question? It pulls the top 3 legal minds' answers. Medical? Top 3 medical minds. Then it either combines their answers or picks the best one. Every other model gets 1-shot at the answer. Ours gets N-shots, from N experts. This is what we call a Web-Scale MoE. Each "expert" isn't a subnetwork inside a single model — it's an entirely separate model. As more experts show up on the web, our model gets smarter without retraining. The upside: it scores higher than Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 across benchmarks. The downside: it behaves like a bigger model and costs more to run. We think the tradeoff is worth it. 1,000 minds wired together are smarter than any single mind. Is the path to AGI training one massive mind — or wiring together every mind that comes into existence?

Bytez 0.1 update: more evals ran, matching or beating Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 across benchmarks PS: the 0.1 model also does pro-level 3D generation What happens after models learn to generate 3D worlds?

Bytez 0.1 beats Opus, Gemini Pro, and GPT-5.4 across benchmarks. Achieved without spending millions on GPUs. Karpathy recently said LLM ensembles are "under-explored." He vibe-coded a weekend prototype to test the idea. We've been building the production version for 2 years. Bytez 0.1 is a Web-Scale MoE — instead of training one massive model, we fuse thousands of models into a single intelligence. Instead of training intelligence, we absorb it. More benchmarks dropping soon. What's a faster path to a v1 of AGI? A) One massive model that tries to be an expert on everything B) Thousands of experts fused into one

Bytez is honored to be embedded in @NeurIPSConf 2025 — making 5,858+ AI papers interactive 🔍 Browse, explore and listen to papers 💻 Find paper code and project pages 🚀 Coming soon: free access to NeurIPS models via the Bytez Inference API Congrats to all the researchers! 🎉

Bytez is now live on @LiteLLM, bringing 20,000+ multi-modal chat models to their platform 🎉 ✨ Bytez gives you free access to 100,000 models ✨ Run 150,000 open + closed models with 1 API key ✨ Generate text, images + more with Bytez, the Internet's largest Inference API 🦄

Bytez is honored to be embedded in @icmlconf this year, making 3341 AI papers truly interactive. 🔍 Browse, explore, and listen to papers ✨ Play with ICML text and vision models Congrats to all the researchers presenting their work this year!

Bytez is honored to be embedded in @CVPR 2025, making AI research truly interactive. 🔍 Browse, explore, and listen to papers 💻 Play with CVPR vision models 🚀 Coming soon: free access to CVPR models via the Bytez Inference API Congrats to all the researchers! 🎉

you can create your own AI feed & explore 440k+ interactive papers on @Bytez. Lets us know how we can improve it ⚡️

🚨 Last call for AI founders + builders! We're going live (virtually) from AWS today, 2–4:30PM PT ⚡ Our founder will be sharing how to turn your janky AI prototype into a multi-tenant SaaS empire, powered by our Model API :) Topics include: 👉 "Agentic repos" (yes, it's a thing) 👉 Agent-as-a-Service 👉 Cloud scaling magic 👉 AWS Marketplace wizardry 🎥 Join us or regret forever → lnkd.in/dC4xG3Zm @omnistrate @saastr @DataRobot

⚡ Building agentic AI apps on AWS? Catch Bytez founder @HustleNHack live tomorrow at the AWS SF office w/ leaders from @awscloud, @DataRobot & @omnistrate We’ll demo how to build an AI agent in minutes using the Bytez Model API 📍 May 6, SF 🔗 lnkd.in/gRf_6Zrx