LightweightAlibaba

Qwen 2.5 Coder 7B

Qwen 2.5 Coder 7B is Alibaba's lightweight coding-specialized model with a 32K token context window, designed for efficient code generation tasks.

Context 33K
Tier Lightweight
Input from
$0.030 / 1M tokens
across 1 provider

API Pricing

ProviderInput / 1MOutput / 1MSpeedTTFTUpdated
$0.030$0.09072.8 t/s1.2s4/14/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
Alibaba
Family
Qwen
Tier
Lightweight
Context Window
33K
Modalities
Text

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

  • Specialized training for coding tasks and programming languages
  • 32,768 token context window allows processing of substantial code files
  • 7B parameter size enables efficient deployment and faster inference
  • Part of Alibaba's established Qwen model family
  • Lightweight tier appropriate for resource-constrained environments
  • Text-focused design optimized for code generation workflows
  • No tool calling capabilities for integrated development workflows
  • Text-only modality lacks support for code visualization or diagrams
  • Smaller parameter count compared to flagship coding models
  • Proprietary model with weights not publicly available
  • Limited to coding tasks rather than general-purpose applications

Key Features

32,768 token context window
Text input and output processing
Code generation and programming assistance
7 billion parameter architecture
Lightweight model deployment
Programming language understanding
Code completion capabilities

About Qwen 2.5 Coder 7B

Qwen 2.5 Coder 7B is a lightweight coding-specialized language model developed by Alibaba as part of the Qwen family. This 7 billion parameter model represents Alibaba's focused approach to code generation, sitting in the lightweight tier for applications requiring efficient computational resources while maintaining coding capabilities. The model operates with a 32,768 token context window and supports text-only inputs, focusing specifically on programming and software development tasks. As a coding-specialized variant of the Qwen 2.5 series, it has been trained and optimized for understanding programming languages, generating code snippets, debugging, and related software development workflows. Qwen 2.5 Coder 7B targets developers and organizations needing reliable code generation capabilities without the computational overhead of larger models. Its lightweight architecture makes it suitable for deployment scenarios where resource efficiency matters while still requiring competent coding assistance.

Common Use Cases

Qwen 2.5 Coder 7B is designed for development teams and individual programmers who need efficient code generation capabilities without the computational requirements of larger models. Its 32K context window makes it suitable for processing and generating substantial code files, code reviews, and refactoring tasks. The lightweight architecture is particularly valuable for organizations running coding assistance tools at scale, embedded development environments, or applications where inference speed and resource efficiency are priorities. It works well for code completion, debugging assistance, programming tutorials, and automated code generation workflows where specialized coding knowledge is more important than general reasoning capabilities.

Frequently Asked Questions

How much does Qwen 2.5 Coder 7B cost per million tokens?

Qwen 2.5 Coder 7B pricing varies by provider and pricing type. Check the pricing table above for current rates across all providers offering this model.

What is Qwen 2.5 Coder 7B best used for?

Qwen 2.5 Coder 7B excels at code generation, programming assistance, and software development tasks. Its 32K context window and coding specialization make it ideal for processing large code files, code completion, debugging help, and automated programming workflows where efficiency matters more than general reasoning capabilities.

How does Qwen 2.5 Coder 7B compare to general-purpose coding models?

Qwen 2.5 Coder 7B offers specialized coding training in a lightweight 7B parameter package, making it more efficient than larger general-purpose models for pure coding tasks. However, it lacks tool calling capabilities and multimodal support that some general-purpose models provide, making it focused specifically on text-based programming assistance.