Qwen3-Coder-480B-A35B-Instruct
About Qwen3-Coder-480B-A35B-Instruct
Qwen3-Coder-480B-A35B-Instruct is the most agentic code model released by Alibaba to date. It is a Mixture-of-Experts (MoE) model with 480 billion total parameters and 35 billion activated parameters, balancing efficiency and performance. The model natively supports a 256K (approximately 262,144) token context length, which can be extended up to 1 million tokens using extrapolation methods like YaRN, enabling it to handle repository-scale codebases and complex programming tasks. Qwen3-Coder is specifically designed for agentic coding workflows, where it not only generates code but also autonomously interacts with developer tools and environments to solve complex problems. It has achieved state-of-the-art results among open models on various coding and agentic benchmarks, with performance comparable to leading models like Claude Sonnet 4. Alongside the model, Alibaba has also open-sourced Qwen Code, a command-line tool designed to fully unleash its powerful agentic coding capabilities
Discover how Qwen3-Coder's agentic capabilities and vast context revolutionize complex coding, debugging, and system development.
Agentic Code Refactoring
Autonomously refactor large codebases, improve architecture, and integrate new features by interacting with development tools and environments.
Use Case Example:
"Refactored a legacy Python Flask application to a modern FastAPI microservice, automatically updating dependencies and ensuring backward compatibility through iterative testing."
Repository-Scale Debugging
Identify and resolve subtle bugs across entire codebases, leveraging 256K+ context to trace execution flow and pinpoint issues in complex systems.
Use Case Example:
"Diagnosed a critical race condition in a multi-threaded Java financial trading system by analyzing logs and code across 50+ files, proposing a precise synchronization fix."
Automated API & Tool Integration
Generate, test, and deploy API integrations or automate complex workflows by interacting with external documentation, APIs, and command-line tools.
Use Case Example:
"Developed an automated data pipeline in Go, integrating with three different cloud APIs (AWS S3, Google Cloud Storage, Azure Blob) by reading their documentation and generating robust client code."
Proactive Security Patching
Scan codebases for potential security vulnerabilities, reason through exploit vectors, and autonomously generate and apply patches to enhance system security.
Use Case Example:
"Identified and patched an SQL injection vulnerability in a Node.js backend by analyzing user input sanitization logic and proposing parameterized queries, then verifying the fix with automated tests."
Metadata
Specification
State
Deprecated
Architecture
Mixture of Experts
Calibrated
No
Mixture of Experts
Yes
Total Parameters
480B
Activated Parameters
35B
Reasoning
No
Precision
FP8
Context length
262K
Max Tokens
262K
Compare with Other Models
See how this model stacks up against others.
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Total Context:
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Max output:
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Input:
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Output:
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Max output:
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Total Context:
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Max output:
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Input:
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Total Context:
262K
Max output:
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Input:
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/ M Tokens
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Total Context:
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Max output:
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Input:
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Total Context:
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Max output:
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Input:
$
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/ M Tokens
Output:
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/ M Tokens
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Total Context:
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Max output:
262K
Input:
$
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/ M Tokens
Output:
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/ M Tokens
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Release on: Oct 21, 2025
Total Context:
262K
Max output:
262K
Input:
$
0.2
/ M Tokens
Output:
$
0.6
/ M Tokens
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Release on: Oct 21, 2025
Total Context:
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Max output:
262K
Input:
$
0.2
/ M Tokens
Output:
$
1.5
/ M Tokens
