How to integrate open AI models into an IDE using Qwen3-Coder-Next as an example?

Modern open-source AI models such as Qwen3-Coder-Next have already reached parity with proprietary counterparts in code generation, analysis, and refactoring tasks. Built on a Mixture-of-Experts (MoE) architecture, it activates approximately 3 billion of its 80 billion parameters, delivering high accuracy—comparable to DeepSeek V3.2 on SWE-Bench — while achieving inference speeds of up to 180 tokens per second.

However, the model’s true value emerges not in isolation, but when integrated directly into your IDE — transforming it into an autonomous assistant capable of writing functions, generating tests, explaining changes, and even managing your project’s file structure. To enable this, Qwen3-Coder-Next must be connected via an OpenAI-compatible endpoint to IDE extensions such as Cline or Codex.

If you’d rather avoid assembling and maintaining your own hardware, the optimal solution is renting a cloud server with powerful GPUs. immers.cloud offers ready-to-use GPU server platforms featuring NVIDIA graphics accelerators, Intel Xeon Gold processors (2nd, 3rd, and 5th generations). This allows you to deploy Qwen3-Coder-Next in just minutes without configuring drivers. It is recommended to use vLLM version 0.15.0 or higher (see the detailed guide in the link below) or Ollama.

A detailed guide on deploying the model in the cloud is available here.

In this guide, we’ll show you how to integrate Qwen3-Coder-Next into your IDE to automate everyday coding—from deploying the model endpoint in the GPU cloud to configuring agent-based interaction directly in VS Code.

The model easily integrates with popular VS Code extensions via an OpenAI-compatible API. For example, Cline only requires you to specify the endpoint URL and access key, and it’s ready to work immediately. It supports autonomous mode: once a task plan is approved, it can create files, write functions, add tests, and run them without requiring constant user confirmation.

Of particular interest is the model’s integration into the development environment. The most convenient options are VS Code extensions compatible with OpenAI-compatible APIs.

Supported API providers include both OpenAI-compatible endpoints and implementations in the Anthropic Messages API format — the latter is available in recent versions of Ollama. This enhances the flexibility of integrating the model into various development tools.

A key feature of Cline is its high autonomy in coding-agent mode. Once a task plan is approved, it can independently execute sequences of actions — creating files, writing functions, adding tests, and running them without requesting confirmation at every step.

On simple tasks, such as implementing classic algorithms in Python, the model demonstrates quality comparable to leading proprietary solutions.

In practice, when building even a basic prototype of a real application, the critical challenge is less about code generation and more about accurate planning and adaptation to evolving or clarified requirements. In this regard, Qwen3-Coder-Next performs confidently: its explanations are logical, detailed, and aligned with specific technical requirements.

An added convenience is the ability to view diffs between the current code and proposed changes, as well as request explanations for each modification directly within the editor interface.

To use the Codex extension in VS Code, you first need to set two environment variables:

• OPENAI_API_KEY — for authentication
• OPENAI_BASE_URL — to specify the endpoint address

After that, launch the editor with the code command.

Important note: Codex works only with APIs that follow the OpenAI Responses format, not the classic Completions API. This limits compatibility with some open-source models and infrastructure solutions, as Completions remains the de facto standard for most open-source LLMs.

To bypass this limitation, you can run Qwen3-Coder-Next via Ollama — recent versions support the Responses API. The deployment process remains simple and requires no complex configuration:

ollama pull qwen3-coder-next

Since the Codex extension recognizes only a limited set of model names from the OpenAI ecosystem (e.g., gpt-5.2-codex), you must rename qwen3-coder-next on the server side for compatibility.

To do this, create a file named Modelfile with the following content:

FROM qwen3-coder-next:latest

Then run in the terminal:

ollama create gpt-5.2-codex -f Modelfile

The model will now be available under the name gpt-5.2-codex:latest, which you can verify at:
http://ollama-host:11434/api/tags

This is sufficient for the Codex extension in VS Code to function correctly.

By default, the Codex extension takes a cautious approach to file system interactions — it requests confirmation for every change unless the user explicitly enables full autonomy in the settings.

This makes it well-suited for environments where code change control is critical. Its interface resembles ChatGPT, which may be convenient for users accustomed to this style of AI interaction.

Both tools — Cline and Codex — offer similar functionality but differ in interactivity level and presentation style, giving developers flexibility to choose the approach that best fits their task and team preferences.

Amid the growing number of AI-assisted development solutions, it’s clear that open models have reached a quality level sufficient for professional use.

Qwen3-Coder-Next demonstrates that local or private deployment can deliver performance comparable to leading proprietary alternatives.

The key advantage of this approach is the ability to use AI in corporate environments without sending code or data to external clouds by reducing the risk of intellectual property leaks and meeting the security requirements of many software companies.

Why consider hosting a GPU server?

Open models like Qwen3-Coder-Next enable private AI assistance without sharing source code or internal data with third-party services. This is especially crucial for software companies where intellectual property protection is paramount.

By hosting a GPU-powered cloud server in a data center, you gain:

• Full control over your data;
• Flexibility in choosing your stack (vLLM, Ollama, Docker);
• Pay-as-you-go billing — you only pay for actual usage;
• Rapid deployment — from configuration to launch in just 2–3 minutes.

This approach combines performance, security, and cost efficiency, making professional AI-assisted coding accessible even to small teams.