Qwen3 Coder 480B A35B Instruct
Qwen3 Coder 480B A35B Instruct is an agentic-focused code model from Alibaba Cloud, built for complex multi-step software engineering workflows and accessible through AI Gateway without requiring separate provider accounts.
View API reference- Input and output price
- Prices from: Input $0.40, Output $1.60, Per 1M tokens
- 24h uptime
- Loading AI Gateway uptime
import { streamText } from 'ai'
const result = streamText({ model: 'alibaba/qwen3-coder', prompt: 'Why is the sky blue?'})Copy link to headingPlayground
Try out Qwen3 Coder 480B A35B Instruct by Alibaba Cloud. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
Qwen3 Coder 480B A35B Instruct
Copy link to headingProviders
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
| Provider |
|---|
Copy link to headingUptime24 hours
Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.
Copy link to headingThroughput24 hours
P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.
Copy link to headingLatency24 hours
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.
Getting started
Call Qwen3 Coder 480B A35B Instruct through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.
Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-coder', prompt: 'Why is the sky blue?', });
console.log(result.text);}
main().catch(console.error);Top-level parameters
The same Qwen3 Coder 480B A35B Instruct request in each API format AI Gateway supports.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-coder', system: 'You are a concise technical assistant.', prompt: 'Summarize the tradeoffs between static generation and SSR.', maxOutputTokens: 1024, temperature: 0.5, });
console.log(result.text);}
main().catch(console.error);Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.
| Parameter | Type | Required | Description |
|---|---|---|---|
model | string | Yes | Model ID in the form creator/model, e.g. alibaba/qwen3-coder. AI Gateway routes the request to an available provider. |
maxOutputTokens | number | No | Hard cap on generated tokens. Qwen3 Coder 480B A35B Instruct supports up to 66,536 output tokens. |
providerOptions | Record<string, JSONValue> | No | AI Gateway routing options under gateway, plus any provider-native options under the provider’s own namespace — see the table below. |
Input limits
| Input | Formats | Sources | Max count | Max size | Limits |
|---|---|---|---|---|---|
| Text | — | — | — | — | Prompt and response share the 262K-token context window |
Provider options
Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.
Learn more in the AI SDK alibaba provider docs.
import { generateText } from 'ai';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-coder', prompt: 'Why is the sky blue?', providerOptions: { gateway: { only: ['deepinfra', 'novita'], }, }, });
console.log(result.text);}
main().catch(console.error);These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.
| Parameter | Type | Required | Description |
|---|---|---|---|
providerOptions.gateway.only | string[] | No | Restrict routing to these provider slugs. Requests fail over only within the listed providers. |
providerOptions.gateway.order | string[] | No | Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks. |
providerOptions.gateway.sort | 'cost' | 'ttft' | 'tps' | No | Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order. |
providerOptions.gateway.zeroDataRetention | boolean | No | Route only to providers with a zero-data-retention policy for this model. |
Routing across providers
AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.
Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.
Tool calling
Expose tools the model can call. Define each tool’s inputs with a Zod schema.
import { generateText, tool } from 'ai';import { z } from 'zod';import 'dotenv/config';
async function main() { const result = await generateText({ model: 'alibaba/qwen3-coder', prompt: 'What is the weather in San Francisco?', tools: { getWeather: tool({ description: 'Get the current weather for a location', inputSchema: z.object({ location: z.string() }), execute: async ({ location }) => ({ location, temperatureC: 18 }), }), }, });
console.log(result.text);}
main().catch(console.error);Copy link to headingAbout Qwen3 Coder 480B A35B Instruct
Qwen3 Coder 480B A35B Instruct targets agentic coding: multi-step software engineering that involves reading files, running tools, debugging failures, and iterating across a real codebase. It's available on AI Gateway for teams building complex, multi-step coding workflows.
The underlying architecture is a 480-billion-parameter MoE model with 35 billion activated per token. This balances deep coding specialization against general reasoning and mathematics, with strong reported results among open models on SWE-Bench Verified.
Qwen3 Coder 480B A35B Instruct integrates with Qwen Code, Claude Code, and Cline. These developer toolchains require agentic coding models to handle tool calls, environment feedback, and iterative plan-execute-debug loops. The model natively supports context of 262.1K tokens, extendable to 1M tokens via YaRN extrapolation, giving it the working memory to hold substantial codebases or long conversation histories during extended development sessions.
Accessing Qwen3 Coder 480B A35B Instruct through AI Gateway removes the friction of managing separate provider credentials. Automatic retries and failover mean that infrastructure issues at any individual provider don't interrupt ongoing coding sessions or break CI pipelines.
Copy link to headingWhat To Consider When Choosing a Provider
- Configuration: For coding-focused workloads, provider latency can affect the feel of interactive development tools. Consider testing provider-specific response times against your throughput requirements.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Copy link to headingWhen to Use Qwen3 Coder 480B A35B Instruct
Best for
- Agentic software engineering tasks: Workflows where the model needs to read code, plan changes, execute tools, interpret results, and iterate, rather than one-shot generation, are exactly what Qwen3 Coder 480B A35B Instruct is tuned for
- SWE-bench-style repository-level tasks: Strong SWE-Bench Verified results among open models indicate solid performance on the kinds of multi-file, multi-step bug fixing and feature implementation tasks representative of real software maintenance
- Building AI-powered developer tools: Teams constructing coding assistants, automated PR review, or code migration tools can use Qwen3 Coder 480B A35B Instruct as the reasoning backbone, with the long context window holding full file context
- Tool-calling pipelines in development environments: Qwen3 Coder 480B A35B Instruct's orientation toward agentic tool use makes it reliable in frameworks that expose file system, shell, and test runner tools to the model during a coding session
Consider alternatives when
- The task is simple code completion or short snippet generation: Smaller, faster models can handle routine code completion without the overhead of an agentic model. Cost and latency are lower for routine generation
- Non-coding tasks dominate the workload: Qwen3 Coder 480B A35B Instruct is specialized for code and agentic scenarios. For general text generation, summarization, or multilingual tasks not involving code, general-purpose Qwen3 variants will serve better
- You need image or multimodal input handling: Qwen3 Coder 480B A35B Instruct is a text model; it doesn't process screenshots, diagrams, or visual inputs
Copy link to headingConclusion
Qwen3 Coder 480B A35B Instruct fills a specific niche: agentic software engineering at scale, where the model participates in extended coding sessions rather than answering isolated questions. AI Gateway exposes it for exactly this use case, and the provider network's automatic retries make it more reliable in CI and interactive tooling contexts than a direct single-provider integration would be.
Copy link to headingFrequently Asked Questions
Why use Qwen3 Coder 480B A35B Instruct through AI Gateway?
You can access Qwen3 Coder 480B A35B Instruct through AI Gateway without separate provider accounts. Built-in retries and failover keep coding pipelines running without interruption.
What makes Qwen3 Coder 480B A35B Instruct "agentic" compared to other coding models?
Agentic coding means the model can operate in multi-turn, tool-using environments where it reads context, decides on actions, executes tools, interprets the results, and adapts its plan accordingly. Qwen3 Coder 480B A35B Instruct is tuned for this loop rather than one-shot code completion, which shows up in its tool-calling reliability and multi-step task performance.
What context length does Qwen3 Coder 480B A35B Instruct support?
262.1K tokens natively, extendable to one million tokens using YaRN extrapolation. This context window lets agentic tasks hold entire repositories or extended tool-call histories in context.
Which developer tools is Qwen3 Coder 480B A35B Instruct integrated with?
The model works with Qwen Code, Claude Code, and Cline out of the box. On AI Gateway, you can use the
qwen3-codermodel identifier from AI SDK, Chat Completions API, Responses API, Messages API, or other API formats, from TypeScript or Python.How does AI Gateway improve reliability for coding pipeline use cases?
AI Gateway adds automatic retries and failover across DeepInfra, Novita AI, Google Vertex AI, Alibaba Cloud. If a provider returns an error during a long agentic session, AI Gateway handles the retry transparently. This prevents the kind of mid-session failures that break automated coding workflows.
What benchmarks has Qwen3 Coder 480B A35B Instruct been evaluated on?
The model reports strong results among open models on Agentic Coding, Agentic Browser-Use, and Agentic Tool-Use benchmarks, with SWE-Bench Verified performance cited as a specific highlight. See https://deepinfra.com/Qwen/Qwen3-Coder-480B-A35B-Instruct for detailed results.
Can I use Qwen3 Coder 480B A35B Instruct for non-coding tasks?
Qwen3 Coder 480B A35B Instruct retains general reasoning and math capability alongside its coding specialization and can handle general tasks, but dedicated general-purpose Qwen3 models are better suited when code isn't the primary use case.
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