ArchiveStudio/Qwen2.5-32B-Instruct

TEXT GENERATIONPricing:Input $2.72 / Output $4.8Concurrent Unit Cost:2Model Size:32.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 31, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

ArchiveStudio/Qwen2.5-32B-Instruct is a 32.5 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen. This model significantly enhances capabilities in coding, mathematics, and instruction following, while also improving long text generation and structured data understanding. It supports a full context length of 131,072 tokens and excels at generating structured outputs like JSON, making it suitable for complex conversational AI and data processing tasks. The model also offers multilingual support for over 29 languages.

Loading preview...

Qwen2.5-32B-Instruct Overview

Qwen2.5-32B-Instruct is a 32.5 billion parameter instruction-tuned causal language model, part of the latest Qwen2.5 series. Developed by Qwen, this model builds upon its predecessors with significant enhancements across several key areas. It features a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved performance in coding and mathematics due to specialized expert model training.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and generating long texts (over 8K tokens).
  • Structured Data & Output: Excels at understanding structured data (e.g., tables) and generating structured outputs, particularly JSON.
  • Robustness: More resilient to diverse system prompts, improving role-play implementation and chatbot condition-setting.
  • Long Context Support: Supports a full context length of 131,072 tokens for input and can generate up to 8,192 tokens. It utilizes YaRN for handling extensive inputs beyond 32,768 tokens.
  • Multilingual: Provides support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, and Korean.

Good For

  • Applications requiring strong coding and mathematical reasoning.
  • Chatbots and assistants needing robust instruction following and role-play capabilities.
  • Tasks involving long document processing and generation.
  • Scenarios demanding structured data understanding and JSON output generation.
  • Multilingual applications across a broad range of languages.

Finetunes
1 models