• Nov 27, 2025: 🎉 We release the inference code and model weights of Step-Audio-R1 (HuggingFace; ModelScope)
• Nov 27, 2025: 🎮 We released the HF Space Playground
• Nov 19, 2025: 🎉 We release the Demo Page
• Nov 19, 2025: 👋 We release the technical report of Step-Audio-R1.

• Inference Code (vLLM)
• Online demo (Gradio)
• Model Checkpoints

Step-Audio-R1 is the first audio language model to successfully unlock test-time compute scaling. It decisively solves the "inverted scaling" anomaly plaguing existing models, where performance paradoxically degrades with longer reasoning chains.

We identify the root cause of this failure as Textual Surrogate Reasoning: conventional models, due to text-based initialization, rely on linguistic abstractions (analyzing transcripts) rather than genuine acoustic properties. To resolve this modality mismatch, we introduce Modality-Grounded Reasoning Distillation (MGRD), an iterative training framework that shifts the model's reasoning focus from textual surrogates to acoustic analysis.

This new approach allows us to create Step-Audio-R1, which:

• Is the first audio reasoning model that successfully benefits from test-time compute scaling.
• Surpasses Gemini 2.5 Pro and is comparable to Gemini 3 across comprehensive audio benchmarks.
• Transforms extended deliberation from a liability into a powerful asset for audio intelligence.

Step-Audio-R1 builds on the architecture of our previous StepAudio 2 and consists of three main components:

1. Audio Encoder: We use the pre-trained Qwen2 audio encoder. It operates at a 25 Hz frame rate and is frozen during training.
2. Audio Adaptor: A simple adaptor (identical to Step-Audio 2) connects the encoder to the LLM and downsamples the feature frame rate to 12.5 Hz.
3. LLM Decoder: We use Qwen2.5 32B as the core reasoning component. It directly takes the latent audio features from the adaptor to generate a purely textual output (first the reasoning, then the final reply).

The key innovation is our training method, Modality-Grounded Reasoning Distillation (MGRD). This process iteratively refines the model's thoughts, progressively strengthening their connection to the underlying audio features until they evolve into "native audio think."

This ensures the model's reasoning is not merely about the transcribed text but is deeply grounded in the acoustic nuances of the audio itself.

• GPU: NVIDIA GPUs with CUDA support (tested on 4×L40S/H100/H800/H20).
• Operating System: Linux.
• Python: >= 3.10.0.

First, you need to download the Step-Audio-R1 model weights.

Method A · Git LFS

git lfs install
git clone https://huggingface.co/stepfun-ai/Step-Audio-R1

Method B · Hugging Face CLI

hf download stepfun-ai/Step-Audio-R1 --local-dir ./Step-Audio-R1

We provide two ways to serve the model: Docker (recommended) or compiling the customized vLLM backend.

A customized vLLM image is required.

1. Pull the image:

docker pull stepfun2025/vllm:step-audio-2-v20250909

2. Start the service: Assuming the model is downloaded in the Step-Audio-R1 folder in the current directory.

   docker run --rm -ti --gpus all \
       -v $(pwd)/Step-Audio-R1:/Step-Audio-R1 \
       -p 9999:9999 \
       stepfun2025/vllm:step-audio-2-v20250909 \
       -- vllm serve /Step-Audio-R1 \
       --served-model-name Step-Audio-R1 \
       --port 9999 \
       --max-model-len 16384 \
       --max-num-seqs 32 \
       --tensor-parallel-size 4 \
       --chat-template '{%- macro render_content(content) -%}{%- if content is string -%}{{- content.replace("<audio_patch>\n", "<audio_patch>") -}}{%- elif content is mapping -%}{{- content['"'"'value'"'"'] if '"'"'value'"'"' in content else content['"'"'text'"'"'] -}}{%- elif content is iterable -%}{%- for item in content -%}{%- if item.type == '"'"'text'"'"' -%}{{- item['"'"'value'"'"'] if '"'"'value'"'"' in item else item['"'"'text'"'"'] -}}{%- elif item.type == '"'"'audio'"'"' -%}<audio_patch>{%- endif -%}{%- endfor -%}{%- endif -%}{%- endmacro -%}{%- if tools -%}{{- '"'"'<|BOT|>system\n'"'"' -}}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{{- '"'"'<|BOT|>tool_json_schemas\n'"'"' + tools|tojson + '"'"'<|EOT|>'"'"' -}}{%- else -%}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- '"'"'<|BOT|>system\n'"'"' + render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message["role"] == "user" -%}{{- '"'"'<|BOT|>human\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- elif message["role"] == "assistant" -%}{{- '"'"'<|BOT|>assistant\n'"'"' + (render_content(message["content"]) if message["content"] else '"'"''"'"') -}}{%- set is_last_assistant = true -%}{%- for m in messages[loop.index:] -%}{%- if m["role"] == "assistant" -%}{%- set is_last_assistant = false -%}{%- endif -%}{%- endfor -%}{%- if not is_last_assistant -%}{{- '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- elif message["role"] == "function_output" -%}{%- else -%}{%- if not (loop.first and message["role"] == "system") -%}{{- '"'"'<|BOT|>'"'"' + message["role"] + '"'"'\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '"'"'<|BOT|>assistant\n<think>\n'"'"' -}}{%- endif -%}' \
       --enable-log-requests \
       --interleave-mm-strings \
       --trust-remote-code

After the service starts, it will listen on localhost:9999.

Step-Audio-R1 requires a customized vLLM backend.

1. Download Source Code:

   git clone https://github.com/stepfun-ai/vllm.git
   cd vllm

2. Prepare Environment:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install and Compile: vLLM contains both C++ and Python code. We mainly modified the Python code, so the C++ part can use the pre-compiled version to speed up the process.

   # Use pre-compiled C++ extensions (Recommended)
   VLLM_USE_PRECOMPILED=1 pip install -e .

4. Switch Branch: After compilation, switch to the branch that supports Step-Audio.

   git checkout step-audio-2-mini

5. Start the Service:

   # Ensure you are in the vllm directory and the virtual environment is activated
   source .venv/bin/activate
   
   python3 -m vllm.entrypoints.openai.api_server \
       --model ../Step-Audio-R1 \
       --served-model-name Step-Audio-R1 \
       --port 9999 \
       --host 0.0.0.0 \
       --max-model-len 65536 \
       --max-num-seqs 128 \
       --tensor-parallel-size 4 \
       --gpu-memory-utilization 0.85 \
       --trust-remote-code \
       --enable-log-requests \
       --interleave-mm-strings \
       --chat-template '{%- macro render_content(content) -%}{%- if content is string -%}{{- content.replace("<audio_patch>\n", "<audio_patch>") -}}{%- elif content is mapping -%}{{- content['"'"'value'"'"'] if '"'"'value'"'"' in content else content['"'"'text'"'"'] -}}{%- elif content is iterable -%}{%- for item in content -%}{%- if item.type == '"'"'text'"'"' -%}{{- item['"'"'value'"'"'] if '"'"'value'"'"' in item else item['"'"'text'"'"'] -}}{%- elif item.type == '"'"'audio'"'"' -%}<audio_patch>{%- endif -%}{%- endfor -%}{%- endif -%}{%- endmacro -%}{%- if tools -%}{{- '"'"'<|BOT|>system\n'"'"' -}}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{{- '"'"'<|BOT|>tool_json_schemas\n'"'"' + tools|tojson + '"'"'<|EOT|>'"'"' -}}{%- else -%}{%- if messages[0]['"'"'role'"'"'] == '"'"'system'"'"' -%}{{- '"'"'<|BOT|>system\n'"'"' + render_content(messages[0]['"'"'content'"'"']) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message["role"] == "user" -%}{{- '"'"'<|BOT|>human\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- elif message["role"] == "assistant" -%}{{- '"'"'<|BOT|>assistant\n'"'"' + (render_content(message["content"]) if message["content"] else '"'"''"'"') -}}{%- set is_last_assistant = true -%}{%- for m in messages[loop.index:] -%}{%- if m["role"] == "assistant" -%}{%- set is_last_assistant = false -%}{%- endif -%}{%- endfor -%}{%- if not is_last_assistant -%}{{- '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- elif message["role"] == "function_output" -%}{%- else -%}{%- if not (loop.first and message["role"] == "system") -%}{{- '"'"'<|BOT|>'"'"' + message["role"] + '"'"'\n'"'"' + render_content(message["content"]) + '"'"'<|EOT|>'"'"' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- '"'"'<|BOT|>assistant\n<think>\n'"'"' -}}{%- endif -%}'

After the service starts, it will listen on localhost:9999.

Get the example code and run it:

# Clone the repository containing example scripts
git clone https://github.com/stepfun-ai/Step-Audio-R1.git r1-scripts
cd r1-scripts

# Run the example
python examples-vllm_r1.py

If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝 :)

@article{tian2025step,
  title={Step-Audio-R1 Technical Report},
  author={Tian, Fei and Zhang, Xiangyu Tony and Zhang, Yuxin and Zhang, Haoyang and Li, Yuxin and Liu, Daijiao and Deng, Yayue and Wu, Donghang and Chen, Jun and Zhao, Liang and others},
  journal={arXiv preprint arXiv:2511.15848},
  year={2025}
}