ErtasAI/Qwen3-4B

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 19, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

ErtasAI/Qwen3-4B is a 4 billion parameter causal language model from the Qwen3 series, developed by Qwen. This model uniquely supports seamless switching between a 'thinking mode' for complex logical reasoning, math, and coding, and a 'non-thinking mode' for efficient general-purpose dialogue. It features enhanced reasoning capabilities, superior human preference alignment, and strong agent capabilities, supporting over 100 languages with a native context length of 32,768 tokens, extendable to 131,072 tokens with YaRN.

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Qwen3-4B Model Overview

ErtasAI/Qwen3-4B is a 4 billion parameter causal language model from the Qwen3 series, developed by Qwen. It is designed to offer a comprehensive suite of capabilities, distinguishing itself through a unique dual-mode operation and significant advancements in various AI tasks. The model has a native context length of 32,768 tokens, which can be extended up to 131,072 tokens using the YaRN method for processing longer texts.

Key Capabilities

  • Dual-Mode Operation: Seamlessly switches between a 'thinking mode' for complex logical reasoning, mathematics, and code generation, and a 'non-thinking mode' for efficient, general-purpose dialogue. This allows for optimal performance across diverse scenarios.
  • Enhanced Reasoning: Demonstrates significant improvements in reasoning capabilities, outperforming previous Qwen models in mathematics, code generation, and commonsense logical reasoning.
  • Human Preference Alignment: Excels in creative writing, role-playing, multi-turn dialogues, and instruction following, providing a more natural and engaging conversational experience.
  • Agent Capabilities: Features strong integration with external tools in both thinking and non-thinking modes, achieving leading performance among open-source models in complex agent-based tasks.
  • Multilingual Support: Supports over 100 languages and dialects, offering robust capabilities for multilingual instruction following and translation.

Good for

  • Applications requiring dynamic reasoning: Ideal for use cases where the model needs to switch between deep logical processing and quick, general responses.
  • Complex problem-solving: Suited for tasks involving advanced mathematics, code generation, and intricate logical reasoning.
  • Interactive conversational agents: Excellent for building chatbots and virtual assistants that require superior human preference alignment, creative writing, and multi-turn dialogue management.
  • Multilingual deployments: Effective for applications needing strong performance across a wide array of languages and translation tasks.
  • Agentic workflows: Recommended for scenarios leveraging tool-calling and integration with external systems, especially with frameworks like Qwen-Agent.

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