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High Reasoning

Qwen 3 Coder Next

Qwen3-Coder-Next is a groundbreaking open-weight AI model with 80B total parameters but only 3B activated, delivering flagship-level coding performance at a fraction of the cost for agents and local development. Excelling in long-horizon reasoning, tool usage, failure recovery, and seamless IDE integration with a 256k context, it empowers developers to tackle complex tasks efficiently.

256k Context
High Intelligence
Unknown The search results do not specify a knowledge cutoff date for Qwen3-Coder-Next. One source explicitly states "Neither model specifies a knowledge cutoff date," and while the model was released in February 2026, no cutoff date is provided in the available information. Knowledge

Available for Chat, Vision, and File Uploads.

Performance Benchmarks

MMLU
87.73%
LiveCodeBench (v6)
58.93%
GPQA
74.49%

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Have a natural conversation, brainstorm ideas, draft emails, or ask for advice.

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Use a Persona

Specialized Experts.
Instruct the AI to act as a Coding Tutor, Marketing Expert, or Travel Guide.

Pick a Persona

Why use Qwen 3 Coder Next?

Super Efficient Inference

Achieves high coding performance with only 3B activated parameters out of 80B total via MoE architecture, rivaling models 10-20x larger

Advanced Agentic Capabilities

Excels in long-horizon reasoning, complex tool usage, and recovery from execution failures for robust coding agents

Long Context Integration

Supports 256k context length for seamless integration with real-world IDEs and diverse development scaffolds

Capability Examples

Complex Tool Usage
Develop a Python script that uses the requests library to fetch data from a REST API, parse JSON, handle errors with retries, and save to CSV. Include logging.
python\nimport requests\nimport json\nimport csv\nimport logging\nimport time\n\nlogging.basicConfig(level=logging.INFO)\n\ndef fetch_with_retry(url, max_retries=3):\n for attempt in range(max_retries):\n try:\n response = requests.get(url)\n response.raise_for_status()\n return response.json()\n except requests.exceptions.RequestException as e:\n logging.error(f"Attempt {attempt+1} failed: {e}")\n if attempt < max_retries - 1:\n time.sleep(2 attempt)\n raise Exception("Max retries exceeded")\n\ndata = fetch_with_retry("https://jsonplaceholder.typicode.com/posts")\n\nwith open("posts.csv", "w", newline="") as f:\n writer = csv.DictWriter(f, fieldnames=["id", "title", "body", "userId"])\n writer.writeheader()\n writer.writerows(data)\n\nlogging.info("Data saved to posts.csv")
Long-Horizon Repo Bug Fix
You are in a Git repo with a bug: function calculate_total(items) returns wrong sum due to unhandled None values. Existing code: def calculate_total(items): return sum(item.price for item in items). Diagnose, fix, add test, and explain changes.
Diagnosis: The sum() fails if any item.price is None, causing TypeError.\n\nFixed Code:\npython\ndef calculate_total(items):\n return sum(item.price or 0 for item in items if item is not None)\n\n\nTest Added:\npython\nclass Item:\n def __init__(self, price):\n self.price = price\n\nassert calculate_total([Item(10), Item(None), None]) == 10\n\n\nExplanation: Filters None items, treats None prices as 0. Robust for incomplete data. Commit as "Fix calculate_total for None handling + test".

How to use

1
Go to Chat

Navigate to the "AI Chat" page.

2
Select Model

Ensure Qwen 3 Coder Next is selected.

3
Type Prompt

Ask a question or paste code.

4
Interact

Refine the answer by replying to the AI.

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