khazarai/Qwen3.5-4B-Agentic-Coding

VISIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 15, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

khazarai/Qwen3.5-4B-Agentic-Coding is a 4.5 billion parameter language model built on the Qwen3.5 architecture, fine-tuned for agentic coding tasks, structured technical reasoning, and automated tool interaction. It excels at multi-step problem solving, refactoring codebases while preserving logic, and executing tool calls for Bash, Python, and file operations. This model is specifically optimized for maintaining execution correctness and internal chain of thought during complex code transformations. Its primary use case is powering autonomous software development agents and advanced code refactoring.

Loading preview...

Model Overview

khazarai/Qwen3.5-4B-Agentic-Coding is a 4.5 billion parameter model, fine-tuned from the Qwen3.5 architecture, specifically designed for advanced agentic coding tasks. It focuses on structured technical reasoning and seamless interaction with external tools.

Key Capabilities

  • Agentic Coding: Optimized for autonomous software development agents that utilize external tool execution.
  • Code Refactoring: Excels at transforming legacy code paradigms (e.g., callback-based) into modern, asynchronous, or modular structures while strictly preserving logical correctness.
  • Structured Reasoning: Implements a robust internal chain of thought, often embedding step-by-step reasoning blocks (<think>) for transparency before generating code.
  • Tool Integration: Capable of generating structured commands for integrated tools including editor (file operations), bash (terminal commands), python (in-line validation), and browser (documentation lookup).

Intended Use Cases

  • Agentic Coding Assistants: Building intelligent agents for software development.
  • Code Refactoring & Optimization: Automating complex code transformations with high logical integrity.
  • Structured Technical Reasoning: Generating detailed explanations and architectural decisions.
  • Interactive Tool Execution: Facilitating automated interactions with development environments.

Limitations

  • Language Restriction: Primarily optimized for English code annotations and prompt instructions.
  • Synthetic Data: Fine-tuning relies on synthetic distillation, which may carry biases from teacher models.
  • Core Biases: Inherits standard biases from the foundational Qwen3.5 architecture.

Finetunes
2 models