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Qwen3-30B-A3B-Instruct-2507

Highlights

We introduce the updated version of the Qwen3-30B-A3B non-thinking mode, named Qwen3-30B-A3B-Instruct-2507, featuring the following key enhancements:

Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
Substantial gains in long-tail knowledge coverage across multiple languages.
Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
Enhanced capabilities in 256K long-context understanding.

!image/jpeg

Model Overview

Qwen3-30B-A3B-Instruct-2507 has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 30.5B in total and 3.3B activated
Number of Paramaters (Non-Embedding): 29.9B
Number of Layers: 48
Number of Attention Heads (GQA): 32 for Q and 4 for KV
Number of Experts: 128
Number of Activated Experts: 8
Context Length: 262,144 natively.

NOTE: This model supports only non-thinking mode and does not generate ``` blocks in its output. Meanwhile, specifying enable_thinking=False` is no longer required.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Performance

|--- | --- | --- | --- | --- | --- | --- |

| MMLU-Pro | 81.2 | 79.8 | 81.1 | 75.2 | 69.1 | 78.4 |

| MMLU-Redux | 90.4 | 91.3 | 90.6 | 89.2 | 84.1 | 89.3 |

| GPQA | 68.4 | 66.9 | 78.3 | 62.9 | 54.8 | 70.4 |

| SuperGPQA | 57.3 | 51.0 | 54.6 | 48.2 | 42.2 | 53.4 |

| AIME25 | 46.6 | 26.7 | 61.6 | 24.7 | 21.6 | 61.3 |

| HMMT25 | 27.5 | 7.9 | 45.8 | 10.0 | 12.0 | 43.0 |

| ZebraLogic | 83.4 | 52.6 | 57.9 | 37.7 | 33.2 | 90.0 |

| LiveBench 20241125 | 66.9 | 63.7 | 69.1 | 62.5 | 59.4 | 69.0 |

| LiveCodeBench v6 (25.02-25.05) | 45.2 | 35.8 | 40.1 | 32.9 | 29.0 | 43.2 |

| MultiPL-E | 82.2 | 82.7 | 77.7 | 79.3 | 74.6 | 83.8 |

| Aider-Polyglot | 55.1 | 45.3 | 44.0 | 59.6 | 24.4 | 35.6 |

| IFEval | 82.3 | 83.9 | 84.3 | 83.2 | 83.7 | 84.7 |

| Arena-Hard v2* | 45.6 | 61.9 | 58.3 | 52.0 | 24.8 | 69.0 |

| Creative Writing v3 | 81.6 | 84.9 | 84.6 | 80.4 | 68.1 | 86.0 |

| WritingBench | 74.5 | 75.5 | 80.5 | 77.0 | 72.2 | 85.5 |

| BFCL-v3 | 64.7 | 66.5 | 66.1 | 68.0 | 58.6 | 65.1 |

| TAU1-Retail | 49.6 | 60.3# | 65.2 | 65.2 | 38.3 | 59.1 |

| TAU1-Airline | 32.0 | 42.8# | 48.0 | 32.0 | 18.0 | 40.0 |

| TAU2-Retail | 71.1 | 66.7# | 64.3 | 64.9 | 31.6 | 57.0 |

| TAU2-Airline | 36.0 | 42.0# | 42.5 | 36.0 | 18.0 | 38.0 |

| TAU2-Telecom | 34.0 | 29.8# | 16.9 | 24.6 | 18.4 | 12.3 |

| MultiIF | 66.5 | 70.4 | 69.4 | 70.2 | 70.8 | 67.9 |

| MMLU-ProX | 75.8 | 76.2 | 78.3 | 73.2 | 65.1 | 72.0 |

| INCLUDE | 80.1 | 82.1 | 83.8 | 75.6 | 67.8 | 71.9 |

| PolyMATH | 32.2 | 25.5 | 41.9 | 27.0 | 23.3 | 43.1 |

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

\#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.

Quickstart

The code of Qwen3-MoE has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3_moe'

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-30B-A3B-Instruct-2507"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 or to create an OpenAI-compatible API endpoint:

SGLang:

    python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Instruct-2507 --context-length 262144

vLLM:

    vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144

Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768.

For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.

Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-30B-A3B-Instruct-2507',

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'http://localhost:8000/v1',  # api_base
    'api_key': 'EMPTY',
}

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            'time': {
                'command': 'uvx',
                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
            },
            "fetch": {
                "command": "uvx",
                "args": ["mcp-server-fetch"]
            }
        }
    },
  'code_interpreter',  # Built-in tools
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters:

We suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.

3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}