Qwen3‐Coder‐Plus - chunhualiao/public-docs GitHub Wiki

Qwen3-Coder-Plus is a powerful, commercial AI model developed by Alibaba Cloud, optimized for advanced coding tasks with a focus on agentic programming. It’s part of the Qwen3-Coder series, designed to handle complex software development workflows, including code generation, debugging, refactoring, and repository-scale tasks. Below is a detailed overview based on available information:

Key Features

  • Architecture: Qwen3-Coder-Plus is built on a Mixture-of-Experts (MoE) architecture with 480 billion total parameters, activating 35 billion per query for high efficiency. This allows it to deliver strong performance while managing computational costs.
  • Context Window: It supports a native context length of 256,000 tokens, extendable to 1 million tokens using YaRN extrapolation, making it ideal for processing entire codebases or large documentation sets.
  • Agentic Capabilities: The model excels in autonomous, multi-step workflows, including planning, tool usage, feedback processing, and decision-making. It can handle tasks like generating SaaS prototypes, automating testing, and producing documentation with minimal human intervention.
  • Language Support: It supports over 350 programming languages, including Python, JavaScript, TypeScript, Java, C++, Go, Rust, and SQL, with strong performance in multi-language codebases.
  • Training: Trained on 7.5 trillion tokens (70% code), it uses advanced techniques like large-scale reinforcement learning (RL) and synthetic data filtering with Qwen2.5-Coder. The "Hard to Solve, Easy to Verify" approach enhances its ability to tackle real-world coding challenges.
  • Tool Integration: Seamlessly integrates with developer tools like Qwen Code CLI, Claude Code, and Cline via OpenAI-compatible APIs. It supports function calling, file manipulation, and browser-like interactions for agentic workflows.
  • Performance: Qwen3-Coder-Plus achieves state-of-the-art results among open models on benchmarks like SWE-Bench Verified (69.6% in 500-turn interactive settings) and outperforms models like GPT-4.1 (54.6%) and Mistral-small-2507 (53.6%), though it trails slightly behind Claude Sonnet 4 (70.4%). It excels in medium-level tasks but may struggle with uncommon patterns like advanced TypeScript narrowing.

Pricing

  • Commercial Use: Available through Alibaba Cloud’s Model Studio with tiered pricing based on input token count:
    • Singapore Region:
      • 0–32K tokens: $1 (input), $5 (output), $0.1 (cached input, 75% off).
      • 32K–128K: $1.8 (input), $9 (output), $0.18 (cached).
      • 128K–256K: $3 (input), $15 (output), $0.3 (cached).
      • 256K–1M: $6 (input), $60 (output), $0.6 (cached).
    • China (Beijing) Region:
      • 0–32K tokens: $0.574 (input), $2.294 (output), $0.23 (cached).
      • Pricing scales similarly up to 256K–1M: $2.868 (input), $28.671 (output), $1.147 (cached).
    • A limited-time discount started July 24, 2025, reducing cached input token prices to 25% of the original (10% of standard input price).
  • Free Quota: 1 million tokens, valid for 180 days after activating Model Studio.
  • Note: The snapshot version (qwen3-coder-plus-2025-07-22) does not support context caching, using the same pricing as above without discounts.

Access and Usage

  • API Access: Available via Alibaba Cloud’s DashScope platform. Developers need an API key and can use OpenAI-compatible SDKs or HTTP methods. Example setup: 
    import os
from openai import OpenAI
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
)
completion = client.chat.completions.create(
    model="qwen3-coder-plus",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a Python function to find prime numbers."}
    ]
)
print(completion.choices[0].message.content)
  • Qwen Code CLI:
    1. Install Node.js 20+.
    2. Install CLI: npm i -g @qwen-code/qwen-code.
    3. Configure environment variables: 
      export OPENAI_API_KEY="your_api_key"
export OPENAI_BASE_URL="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
export OPENAI_MODEL="qwen3-coder-plus"
    4. Run qwen to start coding interactively.
  • Other Tools: Compatible with Claude Code and Cline via DashScope’s OpenAI-compatible endpoints.
  • Hosted Platforms: Available on platforms like CometAPI, OpenRouter, DeepInfra, and Together AI for cloud-based inference.

Performance Highlights

  • Strengths:
    • Excels in medium-level coding tasks like markdown cleaning, scoring 9.25/10, matching premium models like Claude Sonnet 4.
    • Strong in repository-scale tasks, handling large codebases and dynamic data like pull requests.
    • Outperforms open-source models like DeepSeek V3 and Mistral-small-2507 on SWE-Bench and matches GPT-4.1 in functional correctness on MBPP and HumanEval.
  • Weaknesses:
    • Struggles with uncommon tasks like advanced TypeScript narrowing (scored 1/10).
    • Formatting issues in complex visualizations (e.g., benchmark visualization task, scored 7/10).
    • Instruction-following can be verbose for tasks requiring concise outputs.

Real-World Applications

  • Prototyping: Generates functional SaaS prototypes or full-stack web applications with minimal input.
  • Automation: Automates repetitive tasks like code optimization, refactoring, and test generation.
  • Debugging and Refactoring: Identifies bugs, improves code readability, and adds error handling or type hints to complex codebases.
  • Documentation: Produces comprehensive documentation for projects, enhancing maintainability.
  • Data Storytelling: Can build apps to process CSV files, generate visualizations, and answer natural language questions about data.

Comparison to Other Models

  • Vs. GPT-4.1: Qwen3-Coder-Plus outperforms GPT-4.1 on SWE-Bench (69.6% vs. 54.6%) and matches it in functional correctness, offering a cost-effective, open-source alternative.
  • Vs. Claude Sonnet 4: Slightly trails in overall performance (69.6% vs. 70.4% on SWE-Bench) but matches it in medium-level tasks and is open-source, unlike Claude.
  • Vs. Kimi K2: Outperforms Kimi K2 (65.4%) on SWE-Bench but lags in formatting for visualization tasks.
  • Vs. DeepSeek V3: Consistently outperforms in coding tasks, making it a stronger open-source option.

Best Practices

  • Sampling Settings: Use temperature 0.6–0.8 for balanced creativity, lower (0.2–0.4) for deterministic tasks, top-p 0.7–0.9, top-k 20–50, and repetition penalty 1.05–1.1 to avoid boilerplate.
  • Hardware: Requires NVIDIA GPUs with ≥48 GB VRAM (A100 80 GB recommended) and 128–256 GB system RAM for local deployment.
  • Prompting: Use clear, structured prompts with system instructions (e.g., “You are a senior Python developer”) for complex tasks. For file-based tasks, specify file paths or use Code Context for smarter searches.

Availability

  • Cloud: Accessible via Alibaba Cloud Model Studio, CometAPI, or other platforms like OpenRouter and Together AI.
  • Local Deployment: Not directly available for local use as it’s a commercial model, unlike the open-source Qwen3-Coder-480B-A35B-Instruct. Use cloud-hosted endpoints or check Alibaba’s documentation for deployment options.

Conclusion

Qwen3-Coder-Plus is a cutting-edge, commercial coding model that rivals top proprietary models like Claude Sonnet 4 and GPT-4.1 while offering cost-effective pricing and robust agentic capabilities. Its large context window, extensive language support, and seamless tool integration make it ideal for developers working on complex, repository-scale projects. However, it may require careful prompt tuning for uncommon tasks and optimal formatting. For detailed pricing or API access, visit Alibaba Cloud’s Model Studio: https://x.ai/grok.[](https://www.alibabacloud.com/help/en/model-studio/qwen-coder)

If you’d like a hands-on example, specific code generation, or further details on setup, let me know!