🎤 Introducing Step-Audio-R1.1: The New Frontier of Audio Reasoning!
🏆 We just hit No.1 on the Artificial Analysis Speech Reasoning leaderboard!
Our results:
✅96.4% accuracy on BigBench Audio, setting a new record and surpassing Grok, Gemini, OpenAI, and Google models (Fig. 1).
✅1.51s TTFA (Time-to-First-Audio), fast enough to feel like a real conversation
🚀Step-Audio-R1.1 demonstrates that deep reasoning and real-time performance are no longer a trade-off. As shown in Fig. 2, it maintains the highest reasoning depth in class while staying under the critical ~1.5s latency threshold.
What’s under the hood：
✔️Test-time compute scaling: Step-Audio-R1, the first audio LLM to unlock dynamic compute allocation during inference.
✔️End-to-end audio reasoning: coherent, on-the-fly reasoning with zero added latency.
✔️Scalable CoT optimized specifically for audio understanding tasks.
R1.1 is the upgraded, smarter, faster evolution.
Weights are OPEN. Go build something crazy.
🤗 HuggingFace: https://t.co/VZAKvk7v1h
🎤 Try it here: https://t.co/g3v0fGjX5y
🔮 ModelScope:
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
test-time compute — Spending more computation when the model answers — thinking longer, trying multiple attempts — to buy accuracy without training a bigger model.
chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.
Why it matters
Voice agents usually trade reasoning depth against response time. This model reports leading BigBench Audio accuracy while staying near 1.5s to first audio, and the weights are open so you can test that claim.
