Run Qwen3-Coder-Next Locally (2026 Guide)

Qwen3-Coder-Next is one of the most exciting coding models released in early 2026. It is designed specifically for local coding agents, giving you powerful AI-assisted programming without sending your code to a cloud provider.

With a clever Mixture-of-Experts (MoE) design, it activates only about 3B parameters out of a total Qwen3 Next 80B, while still matching the performance of much larger dense models for many coding tasks.

This guide explains, step by step, how to run Qwen3-Coder-Next locally, even if English is not your first language and you are not an AI infrastructure expert. It also includes benchmarks, a comparison table with competitors, pricing insight, testing strategies, and best practices so that your setup is not just “working” but actually optimized.

1. What Is Qwen3-Coder-Next?

Qwen3-Coder-Next is an open-weight coding-focused language model from Alibaba’s Qwen team, announced in February 2026. It is built to power:

• Local coding agents (multi-step tools that edit, run and debug code)
• IDE integrations (VS Code, JetBrains, etc.)
• CLI-based coding workflows

Key characteristics

According to the official model card and community documentation, Qwen3-Coder-Next offers:

• Type: Causal language model (decoder-only)
• Total parameters: ~80B
• Active parameters per token: ~3B (sparse MoE)
• Architecture: Hybrid MoE with Gated DeltaNet + Gated Attention
• Mixture of Experts: 512 experts, 10 activated per token + 1 shared expert
• Context length: 256K tokens (native support)
• Mode: Non-thinking (no <think></think> chain-of-thought blocks in output)
• Primary focus: Coding agents, long-horizon tasks, tool usage, and recovery from execution failures

In simple terms: it is highly optimized to read and write large projects, remember long conversations and file trees, and act as a reliable coding partner for agents and IDEs.

Why it matters for local use

Most “top” coding models (like GPT-4-class or Claude Sonnet-class models) are cloud-only. Qwen3-Coder-Next is different:

• Open weights: You can download and run it yourself, on your own machine.
• MoE efficiency: It behaves like a big model in terms of intelligence, but computes like a much smaller one thanks to only 3B active parameters.
• Agent-friendly: It is specifically tuned for tool-calling and long-horizon, multi-step coding tasks.

If you care about privacy, latency, and control, this model is a strong candidate to become your main local coding assistant.

2. Hardware Requirements for Running Qwen3-Coder-Next Locally

Qwen3-Coder-Next is powerful, but it is not a tiny model. You need to plan your hardware carefully, especially if you want a smooth, responsive experience.

Official guidance and memory usage

Unsloth’s guide to running Qwen3-Coder-Next locally (via llama.cpp) reports the following approximate requirements for the 4‑bit quantized model:​

• Around 46GB RAM/VRAM/unified memory for 4‑bit GGUF
• Around 85GB for 8‑bit weights

They also note a rule of thumb:

disk space + RAM + VRAM ≥ size of quantized model​

So if your chosen quantized GGUF is 40–45GB, you need that much combined across disk cache + RAM + GPU memory.

On Apple Silicon (like M2/M3 with unified memory), the unified RAM acts as both CPU and GPU memory, so a 64GB MacBook is a good “sweet spot” for 4‑bit.

Community “minimums”

Community reports (for example, guides that mention Qwen3-Next and related models) show that, with aggressive quantization and clever offloading, some users attempt to run these MoE models with around 30–32GB of RAM, but with slower performance and tighter limits on context length. Treat that as an experimental minimum, not a comfortable baseline.

Recommended setups

Here is a practical, opinionated guide:

If you only have CPU and no discrete GPU, Qwen3-Coder-Next will still run with enough RAM, but token generation will be much slower. For interactive coding, a modern GPU (NVIDIA, Apple, or AMD with ROCm support where available) is strongly recommended.

3. How Qwen3-Coder-Next Is Different (USP vs Other Coding Models)

To understand why Qwen3-Coder-Next is special, it helps to compare it with its “big brother” Qwen3 Coder 480B and other coding models like DeepSeek-Coder-V2.

3.1 Core USPs of Qwen3-Coder-Next

From the official model card, evaluation sites, and community guides, these are the main unique selling points:

1. Massive efficiency with MoE
   • Only 3B parameters are active per token, yet total capacity is 80B.
   • Delivers performance comparable to models with 10–20× more active parameters for coding tasks.
   • This means lower compute cost for near “frontier” coding quality.
2. Huge context window (256K)
   • Natively supports 262,144 tokens, which is extremely large for a local coding model.
   • Ideal for large codebases, monorepos, and long-running agent sessions.
3. Agent-first training
   • Trained to excel at long-horizon reasoning, complex tool use, and recovery from execution failures.
   • Works especially well with CLI/IDE tools such as Qwen Code, Claude Code, Cline, Kilo, etc.​
4. Non-thinking, fast responses
   • Designed as a non-reasoning model that does not output <think></think> blocks.
   • This makes responses cleaner and faster, especially for tools expecting only final code, not chain-of-thought.
5. Local-first orientation
   • Documentation and community tooling explicitly target llama.cpp, llama-server, and OpenAI-compatible local APIs.
   • Many examples show it integrated into local coding agents and IDEs, not just cloud APIs.

3.2 Benchmark intelligence and behavior

ArtificialAnalysis reports that Qwen3-Coder-Next:​

• Scores 28 on their Intelligence Index, well above the average of 13 for comparable models.
• Is relatively verbose (26M tokens used in evaluation vs 2.8M average).
• Is described as slower than average in their cloud-based tests, but keep in mind this is API-centric; local performance depends heavily on your hardware.

This tells you two things:

• It is smart and capable, especially within the open-weight/non-reasoning segment.
• You should control verbosity via system prompts and sampling parameters for best UX.

4. Downloading Qwen3-Coder-Next

The primary source for the model weights is the official Hugging Face repository:​

Repo: Qwen/Qwen3-Coder-Next​

4.1 Using huggingface_hub

On a machine with Python and pip:

bashpip install huggingface_hub

# Example: download main safetensors weights (adjust file as needed)
huggingface-cli download Qwen/Qwen3-Coder-Next \
--include "*.safetensors" "*.json" \
--local-dir ./Qwen3-Coder-Next

You will usually not run the full FP16 model directly for local use, because it is very large. Instead you will:

1. Use a GGUF quantized version for llama.cpp.
2. Or let tools like Unsloth generate quantizations and optimized variants.​

Unsloth’s documentation mentions quantized formats like UD-Q4_K_XL and similar 4‑bit configurations that work with llama.cpp.​

Tip: Always check the license in the repository before using it commercially. Qwen models are open-weight, but some commercial usage terms may still apply.

5. Running Qwen3-Coder-Next with llama.cpp (Recommended)

For most developers, llama.cpp is the easiest, most robust way to run Qwen3-Coder-Next locally. It supports:

• CPU and GPU acceleration
• GGUF quantized models
• An optional OpenAI-compatible API server (via llama-server)

5.1 Install llama.cpp

On macOS with Homebrew:

bashbrew install llama.cpp

Or build from source (Linux/macOS/WSL):

bashgit clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

Unsloth’s guide recommends using a recent version of llama.cpp to ensure compatibility with MoE and Dynamic GGUFs.

5.2 Get a GGUF quantization

You can either:

• Use an official/community-converted GGUF (often linked from Unsloth or the Hugging Face model card), or
• Convert the original safetensors to GGUF yourself with llama.cpp’s conversion tools.

Look for a 4‑bit quant (for example, variants like Q4_K or similar) that mentions Qwen3-Coder-Next and MoE support.​

Place the GGUF file into a folder, for example:

bashmodels/qwen3-coder-next-4bit.gguf

5.3 Basic command-line run

A simple llama.cpp command to start a chat session might look like:

bash./main \
-m models/qwen3-coder-next-4bit.gguf \
-c 32768 \
-n 4096 \
-t 8 \
--temp 1.0 \
--top-p 0.95 \
--top-k 40 \
--min-p 0.01

Here:

• -c 32768 sets context to 32K (you can go higher if you have enough memory, up to 256K).​
• -t 8 sets number of CPU threads (adjust to your CPU).
• --temp, --top-p, --top-k, --min-p are sampling parameters. Unsloth recommends temperature = 1.0, top_p = 0.95, top_k = 40, min_p = 0.01 for Qwen3-Coder-Next.​

For GPU offloading, add flags like -ngl 35 or similar to offload layers to GPU. Exact values depend on your VRAM; start with a moderate number and increase until you get close to your VRAM limit.

5.4 Running as an OpenAI-compatible local server

To integrate with IDEs, agents, and tools, run llama-server:

bash./llama-server \
-m models/qwen3-coder-next-4bit.gguf \
-c 32768 \
-t 8 \
--temp 1.0 \
--top-p 0.95 \
--top-k 40 \
--min-p 0.01 \
--api-key "not-needed"

This exposes an HTTP API (by default on http://localhost:8080/v1) similar to OpenAI’s. You can then use any OpenAI SDK by pointing it to this URL.

For example, in Python:

pythonfrom openai import OpenAI

client = OpenAI(
base_url="http://localhost:8080/v1",
api_key="not-needed"
)
resp = client.chat.completions.create(
model="qwen3-coder-next",
messages=[
{"role": "system", "content": "You are a helpful coding assistant."},
{"role": "user", "content": "Write a Python function for binary search with unit tests."}
]
)
print(resp.choices[0].message.content)

Unsloth’s guide shows similar patterns and how to connect this to tool calling workflows using the same API format.​

6. Integrating Qwen3-Coder-Next with Coding Tools and Agents

Qwen3-Coder-Next is particularly strong when used in agentic setups: tools that can edit files, run tests, and iterate.

6.1 IDE and editor integrations

Because llama-server exposes an OpenAI-compatible API, many tools work almost out-of-the-box:

• VS Code extensions like Continue.dev or Cline can often be configured with a custom base URL and model name.
• Qwen’s own ecosystem mentions compatibility with CLI/IDE platforms such as Qwen Code, Claude Code, Qoder, Kilo, Trae, Cline, etc.​

In a typical configuration:

1. Start llama-server with Qwen3-Coder-Next.
2. In your IDE extension settings:
   • Set “API Provider” to “Custom/OpenAI-compatible”.
   • Set Base URL to http://localhost:8080/v1.
   • Set Model Name to qwen3-coder-next (or whatever you configured).
3. Disable streaming if the extension has issues, or enable it for faster UI feedback.

6.2 Tool calling (agents)

Unsloth’s documentation gives examples of tool-calling with Qwen3-Coder-Next:​

• Executing Python code generated by the model
• Running shell commands
• Reading/writing files

The typical workflow:

1. Define tools in OpenAI-style JSON schema.
2. Let the model choose tools via function/tool calls.
3. Your agent runtime executes the tool, returns the result back to the model, and continues.

Because Qwen3-Coder-Next has been trained to recover from tool failures and handle multi-step flows, it is very good at “try–fix–retry” loops in coding agents.

7. Example: Realistic Local Coding Session

To make this concrete, imagine this workflow:

7.1 Prompt setup

System prompt (for coding):

You are Qwen3-Coder-Next, an expert software engineer.Respond with concise, correct code.Prefer standard libraries.When editing, show only the code or unified diff, no explanations.

User prompt:

I have a Python project. Create a new module search_utils.py with:A binary search function with type hintsA function to search a sorted list of dicts by key
Then generate a tests/test_search_utils.py file using pytest.

7.2 Expected behavior

Thanks to its coding-focused training and agentic design, Qwen3-Coder-Next should:

• Generate well-structured Python functions with type hints.
• Create a pytest-compatible test suite.
• If used in an agent, optionally:
  • Write the files to disk.
  • Run pytest.
  • Inspect failures.
  • Propose fixes (e.g., off-by-one errors).

You can use this kind of scenario as a local “smoke test” to validate that your hardware, quantization, and parameters are producing high-quality results.

8. Benchmark & Comparison: Qwen3-Coder-Next vs Competitors

8.1 Intelligence and behavior benchmark

ArtificialAnalysis’ Intelligence Index for Qwen3-Coder-Next:​

• Score: 28 (above-average in its class)
• Average of peers: 13
• Notes:
  • Very verbose (26M tokens vs 2.8M average during evaluation)
  • Described as slower than average and “somewhat expensive” compared to other open-weight non-reasoning models in cloud-hosted scenarios.

However, Qwen’s own materials highlight that Qwen3 Coder (the larger 480B A35B instruct variant) already reaches performance comparable to top proprietary coding models on tasks like SWE-Bench Verified using execution-driven RL and long-horizon training. Qwen3-Coder-Next brings that agentic expertise into a much more efficient, local-friendly MoE design.

8.2 Quick comparison chart

The table below compares Qwen3-Coder-Next with some close relatives and competitors. Values are simplified based on public information and should be considered approximate, but they give a clear positioning:

From this table, Qwen3-Coder-Next clearly occupies the niche of:

• Much lighter to run than Qwen3 Coder 480B, thanks to 3B active vs 35B active parameters.
• More specialized than Qwen3 8B, focusing strongly on coding, agents, and large contexts.
• Competitive with DeepSeek-style MoE coders in terms of architecture and capability, but with a particularly strong emphasis on 256K context and agent robustness.

9. Pricing & Cost Considerations (Local vs API)

9.1 Local deployment cost

When you run Qwen3-Coder-Next locally:

• There is no per-token fee. Your “cost” is:
  • Hardware purchase or rental
  • Electricity
  • Your time to maintain the setup

MoE with 3B active parameters means, on the same hardware, you can often reach or beat the throughput of much larger dense models while keeping quality similar, which makes local deployment cost-effective for heavy coding usage.

9.2 Cloud/API pricing

At the time of writing:

• ArtificialAnalysis reports no widely tracked general-purpose API providers yet for Qwen3-Coder-Next.​
• Some niche providers, like Puter, list Qwen3-Coder-Next (or equivalent naming) around:
  • $0.20 per 1M input tokens
  • $1.50 per 1M output tokens​

For comparison, Qwen3 Coder 480B A35B Instruct via Qwen’s first-party API is priced roughly at:​

• $1.50 per 1M input tokens
• $7.50 per 1M output tokens

This suggests:

• Qwen3-Coder-Next is positioned as a more affordable, efficient coding option than the full 480B Coder variant when delivered via APIs.
• If you have ongoing heavy coding workloads (e.g., an AI pair-programmer used daily), running it locally can be extremely cost-effective compared to high-end cloud models.

10. How to Test and Benchmark Qwen3-Coder-Next Locally

To make sure your setup is actually good—not just “working”—you should test both performance and quality.

10.1 Measuring performance (tokens per second)

You can:

• Use llama.cpp’s logs (many builds report tokens per second in the console).
• Run fixed-length generations (e.g., ask it to generate 1,000 tokens) and time the response.

Recommended steps:

1. Note the elapsed time from first token to last token.
2. Compute approximate tokens/sec = total_tokens / seconds.

Run a prompt that asks the model to generate about 1,000–2,000 tokens, such as:

Generate a detailed step-by-step technical tutorial with code samples about building a REST API in Python using FastAPI.

Compare different settings:

• 3‑bit vs 4‑bit quantization
• Different -ngl GPU offload levels
• Different context sizes (-c 16384 vs -c 32768)

This will help you find the best trade-off between speed and quality on your hardware.

10.2 Testing coding quality

For quality, run realistic coding tasks, for example:

1. Refactor a module
   • Ask Qwen3-Coder-Next to refactor a messy module into smaller functions.
   • Check:
     • Does it respect types?
     • Does it preserve behavior?
     • Are new names readable?
2. Unit-test generation
   • Ask it to generate pytest unit tests for an existing file.
   • Run the tests:
     • Do they pass?
     • Do they cover edge cases?
3. Bug fixing
   • Provide a failing test output.
   • Ask it to diagnose and fix the bug.
   • Re-run tests to see if it truly fixed the problem.

You can compare its performance with another local model (e.g., Qwen3 8B or DeepSeek-Coder-V2) on exactly the same tasks to get a subjective benchmark that is directly relevant to your own stack.

11. Tuning Qwen3-Coder-Next for Best Results

11.1 Recommended sampling parameters

Unsloth and Qwen materials suggest starting values like:​

• Temperature: 1.0
• Top_p: 0.95
• Top_k: 40
• Min_p: 0.01 (llama.cpp default is often 0.05)

You can then tune:

• Lower temperature (0.4–0.8) if you want more deterministic, stable outputs.
• Lower top_p (0.8–0.9) to reduce randomness further for critical production code.

11.2 Reducing verbosity

Because Qwen3-Coder-Next tends to be quite verbose, especially in natural language answers, use:​

• System prompt constraints:
  • “Answer concisely.”
  • “Only output code, no explanations, unless explicitly requested.”
• Post-processing in your agent or IDE:
  • Strip comments for diff-only workflows.
  • Collapse explanations into expandable sections in the UI.

This can significantly improve UX, especially in agents reading and writing large files.

12. Troubleshooting Common Issues

12.1 Out-of-memory (OOM) errors

Symptoms:

• llama.cpp exits with CUDA OOM.
• System becomes unresponsive when loading the model.

Solutions:

• Use a smaller quantization (e.g., more aggressive 3‑bit).
• Lower the context size (-c 8192 or -c 16384 instead of 32768–256K).
• Reduce batch size or concurrency.
• Increase swap/pagefile on Linux/Windows (helps slightly for RAM but not VRAM).

12.2 Very slow generation

If you see slow token speeds:

• Reduce context length.
• Increase the number of threads (-t).
• Ensure your GPU is actually being used (check nvidia-smi or macOS Activity Monitor).
• Avoid running other heavy GPU tasks (games, training jobs) at the same time.

12.3 Poor or inconsistent code quality

If the model output looks off:

• Verify that:
  • You are using a recent llama.cpp build with MoE support.
  • You downloaded a correct, Qwen3-Coder-Next-specific GGUF.
• Try higher precision (e.g., 4‑bit instead of an ultra-aggressive quant).
• Use clearer, more constrained prompts:
  • Specify language, style, test frameworks, and whether you want diffs vs full files.

13. When Should You Choose Qwen3-Coder-Next?

Qwen3-Coder-Next is a great fit if:

• You want a powerful local coding agent with:
  • Long context (256K),
  • Strong tool-using behavior,
  • Good performance with MoE efficiency.
• You already have (or plan to acquire) 48–64GB+ of RAM or VRAM.
• You care about:
  • Data privacy (no code sent to the cloud),
  • Latency and offline availability,
  • Cost control for heavy daily usage.

You might choose something else if:

• Your hardware is very limited (e.g., only 16GB RAM and no GPU).
• You mostly need a light assistant for general chat and simple code; a smaller 7–8B model might be easier.
• You are okay with cloud-only solutions and want absolute frontier performance, in which case models like top-tier GPT or Claude variants are still ahead on some coding benchmarks.

14. Final Thoughts

Qwen3-Coder-Next sits in a very attractive sweet spot in early 2026:

• Intelligence: Above-average among open-weight non-reasoning models, with strong coding orientation.
• Efficiency: Only 3B active parameters in an 80B MoE design, enabling serious coding power on high-end consumer hardware.
• Local-first: Excellent documentation and community support for running it with llama.cpp, llama-server, and OpenAI-compatible APIs.
• Agent-ready: Trained for long-horizon coding agents, robust tool use, and recovery from failures.

By following the steps in this guide—choosing the right hardware, using a good 4‑bit GGUF, configuring llama.cpp/llama-server correctly, and testing with realistic coding tasks—you can build a state-of-the-art local coding assistant that respects your privacy and gives you frontier-level power without a frontier-level cloud bill.