First time in my life seeing a benchmark scramble overnight to fit a model. It used to be benchmaxxing; now it’s model-maxxing 🤣 Build a great model, and the leaderboard will chase after you 😉

🚀 Introducing Audex, a unified audio-text LLM for text, speech, sound, and music 🚀 🏆 Audex delivers best-in-class performance among open models across: 🎧 Audio understanding 🗣️ Speech recognition and translation 🔊 Text-to-speech 🎵 General audio generation 🔄 Speech-to-speech generation 🥇 Audex also achieves best-in-class results in math and code reasoning, alignment, and instruction following, even outperforming the text-only Qwen3.5-35B-A3B. 🧩 Minimalist architecture: a single 30B-A3B MoE model. Audio inputs are projected into the text embedding space, while text tokens and quantized audio output tokens are treated uniformly during generation. 🧠 Strong text intelligence, preserved • Built on the Nemotron-Cascade-2-30B-A3B text backbone • Trained with multi-stage SFT on blended audio-text data, Cascade RL, and multi-domain on-policy distillation • The result: broad and SOTA audio capabilities with no regression in text intelligence. 🤗 Model: 👉 📄 Technical report: 👉