Overview

Qwen3 Coder 30B A3b Instruct is a chat model by Qwen. It has 30.5B parameters. It supports a 160K token context window. Supports function calling.

Model Card

Qwen3-Coder-30B-A3B-Instruct

Highlights

Qwen3-Coder is available in multiple sizes. Today, we're excited to introduce Qwen3-Coder-30B-A3B-Instruct. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements:

Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks.
Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding.
Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format.

Model Overview

Qwen3-Coder-30B-A3B-Instruct 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 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.
Quickstart

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-Coder-30B-A3B-Instruct"
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 = "Write a quick sort algorithm."
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=65536
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)

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 Coding
Qwen3-Coder excels in tool calling capabilities.
You can simply define or use any tools as following example.
# Your tool implementation
def square_the_number(num: float) -> dict:
    return num ** 2
Define Tools
tools=[
    {
        "type":"function",
        "function":{
            "name": "square_the_number",
            "description": "output the square of the number.",
            "parameters": {
                "type": "object",
                "required": ["input_num"],
                "properties": {
                    'input_num': {
                        'type': 'number', 
                        'description': 'input_num is a number that will be squared'
                        }
                },
            }
        }
    }
]
import OpenAI
Define LLM

client = OpenAI(
    # Use a custom endpoint compatible with OpenAI API
    base_url='http://localhost:8000/v1',  # api_base
    api_key="EMPTY"
)
 
messages = [{'role': 'user', 'content': 'square the number 1024'}]
completion = client.chat.completions.create(
    messages=messages,
    model="Qwen3-Coder-30B-A3B-Instruct",
    max_tokens=65536,
    tools=tools,
)
print(completion.choice[0])

Best Practices
To achieve optimal performance, we recommend the following settings:
Sampling Parameters:

We suggest using temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.05`.

Adequate Output Length: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models.

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}, 
}

Features & Capabilities

Mode	chat
Context Window	160,000 tokens
Max Output	32,768 tokens
Function Calling	Supported
Vision	-
Reasoning	-
Web Search	-
Url Context	-

Technical Details

Architecture	Qwen3MoeForCausalLM
Model Type	qwen3_moe
Library	transformers

API Usage

from openai import OpenAI

client = OpenAI(
    base_url="https://api.haimaker.ai/v1",
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="qwen/qwen3-coder-30b-a3b-instruct",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
)

print(response.choices[0].message.content)

Frequently Asked Questions

What is the context window of Qwen3 Coder 30B A3B Instruct?

Qwen3 Coder 30B A3B Instruct (qwen/qwen3-coder-30b-a3b-instruct) has a 160,000-token context window and supports up to 32,768 output tokens per request.

How much does Qwen3 Coder 30B A3B Instruct cost?

Qwen3 Coder 30B A3B Instruct is priced at $0.07 per 1M input tokens and $0.27 per 1M output tokens when accessed via the haimaker.ai OpenAI-compatible API.

What features does Qwen3 Coder 30B A3B Instruct support?

Qwen3 Coder 30B A3B Instruct supports function calling.

How do I use Qwen3 Coder 30B A3B Instruct via API?

Send requests to https://api.haimaker.ai/v1/chat/completions with model "qwen/qwen3-coder-30b-a3b-instruct" using any OpenAI-compatible SDK. Authentication uses a Bearer API key from https://app.haimaker.ai.