Qwen3 Coder 480B — free API, the largest open coding model

Alibaba's Qwen3 Coder 480B (`qwen/qwen3-coder-480b-a35b-instruct`) is the largest open-weight coding model available for free via NVIDIA NIM through InferAll. 480 billion total parameters, 35 billion active (Mixture of Experts). No credit card, no setup: ```python from openai import OpenAI client = OpenAI( base_url="https://api.inferall.ai/v1", api_key="ifu_your_key_here", # get one at inferall.ai/keys ) response = client.chat.completions.create( model="qwen/qwen3-coder-480b-a35b-instruct", messages=[{ "role": "user", "content": "Write a Python function that implements binary search and handles edge cases." }], max_tokens=1024, ) print(response.choices[0].message.content) ``` --- ### What is Qwen3 Coder 480B? Qwen3 Coder 480B is Alibaba's largest instruction-tuned coding model. The `480b-a35b` naming describes its Mixture of Experts architecture: 480 billion total parameters across expert networks, with 35 billion activated per token. This gives it strong coding ability — reasoning, generation, debugging, and code review — while keeping inference cost manageable. At 480B total parameters it is, as of this writing, the largest open-weight coding model available for free anywhere. It's specifically trained on code-heavy data and outperforms many closed models on coding benchmarks. --- ### TypeScript / Node.js ```typescript import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://api.inferall.ai/v1", apiKey: process.env.INFERALL_API_KEY, }); const response = await client.chat.completions.create({ model: "qwen/qwen3-coder-480b-a35b-instruct", messages: [ { role: "system", content: "You are an expert programmer. Return only code, no explanations unless asked." }, { role: "user", content: "Write a TypeScript function to deep-merge two objects recursively." } ], }); console.log(response.choices[0].message.content); ``` ### Streaming ```python with client.chat.completions.create( model="qwen/qwen3-coder-480b-a35b-instruct", messages=[{"role": "user", "content": "Implement a simple Redis client in Python."}], stream=True, ) as stream: for chunk in stream: print(chunk.choices[0].delta.content or "", end="") ``` --- ### Use cases **Code review:** ```python with open("my_module.py") as f: code = f.read() response = client.chat.completions.create( model="qwen/qwen3-coder-480b-a35b-instruct", messages=[ {"role": "system", "content": "Review this code for bugs, edge cases, and improvements."}, {"role": "user", "content": code}, ], ) ``` **Debugging:** ```python error_context = """ Error: TypeError: 'NoneType' object is not iterable Stack trace: ... Code: for item in get_items(): process(item) """ response = client.chat.completions.create( model="qwen/qwen3-coder-480b-a35b-instruct", messages=[{"role": "user", "content": f"Debug this:\n{error_context}"}], ) ``` --- ### Free coding models on InferAll | Model | Size | Focus | |---|---|---| | `qwen/qwen3-coder-480b-a35b-instruct` | 480B / 35B active | Code generation, largest open coder | | `qwen/qwen3-next-80b-a3b-instruct` | 80B / 3B active | Faster Qwen3 | | `meta/llama-4-maverick-17b-128e-instruct` | 17B / 128E | General + code | | `google/codegemma-7b` | 7B | Google's code model | | `deepseek-ai/deepseek-coder-6.7b-instruct` | 6.7B | Compact coder | All free, hosted on NVIDIA NIM. --- ### Get started [inferall.ai/keys](https://inferall.ai/keys) — no credit card required. 200 free requests to evaluate, then add a card to unlock the full free allowance (still $0) and access paid providers (OpenAI, Anthropic, Google) at the published per-token rate with zero markup.