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Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo

DeepInfra Inc./🇺🇸 US/chat

Qwen3-Coder-480B-A35B-Instruct is the Qwen3's most agentic code model, featuring Significant Performance on Agentic Coding, Agentic Browser-Use and other foundational coding tasks, achieving results comparable to Claude Sonnet.More
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Which id to call

deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo

This exact deployment on DeepInfra Inc., with no routing and no failover. Send it as the model field.

Input /1M

$0.30

$0.10 cached

Output /1M

$1.00

3.3x input

Context

262K

tokens

Added

Jul 2025

chat

Capabilities 3/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo costs

Provider prices per 1M tokens, updated September 8, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

Input /1M

$0.30

Output /1M

$1.00

Cache write /1M

-

Cache read /1M

$0.10

What a workload costs

100K input + 10K output
$0.0400
1M input + 100K output
$0.40
10M input + 1M output
$4.00

At the rates above, before caching. A cache read costs $0.10 per 1M, so repeated context lands under these figures.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo. Existing OpenAI SDK code needs no other edit.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window262K tokens
Max output-
API typechat
AddedJul 2025
Model id
Data retentionNone
Used for trainingNo
Served from🇺🇸 US
Privacy policyDeepInfra Privacy Policy →

Released 2025-07-22

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

GPQA Diamondreasoning
61.8%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
11.9%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

More from DeepInfra Inc.

Newest first, on the same provider and the same key.

glm-5.3-flash

1.0M ctx/$0.15/1M

glm-5.3

1.0M ctx/$1.20/1M

deepseek-v4-pro-0813

1.0M ctx/$1.30/1M

qwen3.8

262K ctx/$2.00/1M

deepseek-v4-flash-0731:flex

1.0M ctx/$0.07/1M

deepseek-v4-flash-0731

1.0M ctx/$0.09/1M

Reference

Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo questions

How much does Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo cost?

Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo is priced at $0.30 per million input tokens and $1.00 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo?

Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo has a context window of 262K tokens. That's roughly 350 words of input you can fit in a single prompt.

How does Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo perform on benchmarks?

Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo scores 78.8% on MMLU Pro, 61.8% on GPQA Diamond, 58.5% on LiveCodeBench. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.

What can Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo do?

Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo supports tool calling, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo with the OpenAI SDK?

Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo through Requesty?

Yes. Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo runs through Requesty's OpenAI-compatible API, served from DeepInfra Inc.. You do not host the model yourself: point base_url at Requesty, set the model to "deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.

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