Qwen3 Coder 480B is not on InferAll: the open coding models that are

Update, 2026-07-31: qwen/qwen3-coder-480b-a35b-instruct is not available through InferAll. It is not in our served catalog, so a call naming it returns a 404. We verify this by calling every advertised model directly rather than trusting a provider's model list, and we remove ids that do not answer.

The closest code-specialised open model we do serve is poolside/laguna-xs-2.1, free at our open-model rate. Every example below uses it and runs as written:

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="poolside/laguna-xs-2.1",
    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 anywhere. It's specifically trained on code-heavy data and outperforms many closed models on coding benchmarks.

---

TypeScript / Node.js

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: "poolside/laguna-xs-2.1",
  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

with client.chat.completions.create(
    model="poolside/laguna-xs-2.1",
    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:

with open("my_module.py") as f:
    code = f.read()

response = client.chat.completions.create(
    model="poolside/laguna-xs-2.1",
    messages=[
        {"role": "system", "content": "Review this code for bugs, edge cases, and improvements."},
        {"role": "user", "content": code},
    ],
)

Debugging:

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="poolside/laguna-xs-2.1",
    messages=[{"role": "user", "content": f"Debug this:\n{error_context}"}],
)

---

Open coding models on InferAll

Model	Focus
poolside/laguna-xs-2.1	Code generation and review
deepseek-ai/deepseek-v4-pro	Strong reasoning plus code
deepseek-ai/deepseek-v4-flash	Fast, low latency
meta/llama-3.3-70b-instruct	Strong general purpose

All hosted on NVIDIA NIM at our open-model rate.

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Get started

Sign up at inferall.ai/keys and fund a key with the $5 starter pack — usage credit you can spend on Qwen3 Coder, any other open model, or premium providers (OpenAI, Anthropic, Google) at the published per-token rate with zero markup.