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Qwen: Qwen3 Coder 480B A35BAgentic Coding Scaled

Try Qwen: Qwen3 Coder 480B A35BAPI Documentation

Code Like Never Before

MoE Power

480B Total 35B Active

Sparse Mixture-of-Experts activates 35B params per inference for dense-scale performance at lower cost.

Ultra Context

256K Native 1M Extended

Handles vast codebases with 262K token window via RoPE and YaRN extrapolation.

Agentic Core

Multi-Turn Tool Use

Executes repository analysis, PR generation, and terminal workflows with Agent RL training.

Examples

See what Qwen: Qwen3 Coder 480B A35B can create

Copy any prompt below and try it yourself in the playground.

Repo Refactor

“Analyze this Python repository codebase. Identify inefficiencies in the data pipeline module across files. Propose refactored structure with type hints, async improvements, and generate pull request diff.”

Bug Hunt

“Examine this Rust CLI tool source. Trace memory leak in async runtime integration. Output fixed code, test cases, and explanation of root cause with stack traces.”

API Build

“Design FastAPI backend for user auth system. Include JWT, rate limiting, database schema in SQLAlchemy. Generate full server code with Docker setup and deployment script.”

Algo Optimize

“Implement efficient graph traversal for social network recommendations in Go. Optimize for 1M nodes using adjacency lists. Benchmark against BFS and provide Big-O analysis.”

For Developers

A few lines of code.
Autonomous code. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen: Qwen3 Coder 480B A35B

Read the docs

Qwen: Qwen3 Coder 480B A35B is Alibaba's MoE LLM with 480B total parameters and 35B active. It specializes in agentic coding tasks like multi-turn reasoning and tool use. Native 256K context supports large repos.

Qwen: Qwen3 Coder 480B A35B API tops SWE-bench at 69.6% for 500 turns and Terminal-bench at 37.5. It rivals proprietary models in code generation and agentic workflows. Benchmarks show strong math and coding indices.

Native context is 262K tokens, extendable to 1M with YaRN. Uses 62 layers, GQA with 96 query heads. Supports long-horizon tasks like full repo analysis.

Qwen: Qwen3 Coder 480B A35B matches Claude Sonnet in agentic coding via Code RL post-training. Trained on 7.5T tokens with 70% code across 358 languages. Open weights enable custom fine-tuning.

Decoder-only transformer with SwigLU activation and RMS norm. 6144 hidden dim, 8 active experts from 160 total. Function calling supported on select providers.

Integrate via LLM endpoint for text completion. Supports JSON mode on some hosts. Deploy with dedicated GPUs for high-throughput code synthesis.

Ready to create?

Start generating with Qwen: Qwen3 Coder 480B A35B on ModelsLab.

Try Qwen: Qwen3 Coder 480B A35BAPI Documentation