NOTE:
This model is only supported for fine-tuning. Base model inference is not currently available.

Direct from Azure models

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Key capabilities

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

• Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
• Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
• Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
• Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
• Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

Qwen3-32B is a large language model from the Qwen3 series. It features 32.8 billion parameters, a 131K token context window, support for 100+ languages, and hybrid thinking modes allowing switching between deep reasoning and fast responses. It demonstrates strong performance in reasoning, instruction-following, and agent capabilities.

Pricing

Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.

Technical specs

Qwen3-32B has the following technical specs:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training
• Number of Parameters: 32.8B
• Number of Paramaters (Non-Embedding): 31.2B
• Number of Layers: 64
• Number of Attention Heads (GQA): 64 for Q and 8 for KV
• Context Length: 32,768 natively and 131,072 tokens with YaRN .

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