Qwen/QwQ-32B is a breakthrough 32-billion parameter reasoning model delivering performance comparable to state-of-the-art (SOTA) models 20x larger like DeepSeek-R1 (671B parameters) on complex reasoning and coding tasks. Deployed on Groq's hardware, it provides the world's fastest and cost-efficient reasoning, producing chains and results in seconds. Along with native tool use support, the 128K context window enables processing extensive information while maintaining comprehensive context.

Try now on Groq Card

Key Technical Specifications

Technical Details

FEATURE	VALUE
Context Window (Tokens)	128K
Max Output Tokens	-
Max File Size	-
Token Generation Speed	~400 TPS
Input Token Price	$0.29 per million tokens
Output Token Price	$0.39 per million tokens
Tool Use
JSON Mode
Image Support

Best Practices

• Use `temperature=0.6` and `top_p=0.95` to avoid endless repetitions and hallucinations.
• Utilize the full context window - with 128K tokens available, provide comprehensive problem descriptions and relevant background information.
• Set `reasoning_format` to `parsed` with to handle the missing first `<think>` token in QwQ-32B output.
• For multi-turn conversations, include only the final output from previous turns in history, not the thinking content.
• Prompt the model to be concise when needed - the model tends to produce extensive reasoning chains.
• Increase `max_completion_tokens` to give the model sufficient space to complete its reasoning without truncation.
• If reasoning chains are critical, prompt the model to avoid using Chinese characters in its output (this is normal behavior).
• Take advantage of QwQ-32B's strong tool use and function calling capabilities for agentic applications.
• If the model provides thinking without reaching a final answer, try prompting for conciseness or rerun your query.

Get Started with Qwen/QwQ-32B

Experience the world's fastest breakthrough reasoning capabilities with qwen-qwq-32b on Groq:

pip install groq

1from groq import Groq
2client = Groq()
3completion = client.chat.completions.create(
4    model="qwen-qwq-32b",
5    messages=[
6        {
7            "role": "user",
8            "content": "Explain why fast inference is critical for reasoning models"
9        }
10    ]
11)
12print(completion.choices[0].message.content)