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Qwen/Qwen3-Max

DeepInfra Inc./🇺🇸 US/chat

The latest flagship model in the Qwen family. State-of-the-art results across a comprehensive suite of benchmarks: including knowledge, reasoning, coding, instruction following, human preference alignment, agent tasks, and multilingual understanding.More

Which id to call

deepinfra/Qwen/Qwen3-Max

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

Input /1M

$1.20

$0.24 cached

Output /1M

$6.00

5.0x input

Context

256K

tokens

Added

Sep 2025

chat

Capabilities 2/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What Qwen/Qwen3-Max 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

$1.20

Output /1M

$6.00

Cache write /1M

-

Cache read /1M

$0.24

What a workload costs

100K input + 10K output
$0.18
1M input + 100K output
$1.80
10M input + 1M output
$18.00

At the rates above, before caching. A cache read costs $0.24 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-Max. 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-Max", 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 window256K tokens
Max output-
API typechat
AddedSep 2025
Model id
Data retentionNone
Used for trainingNo
Served from🇺🇸 US

Released 2026-01-26

Benchmark scores

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

GPQA Diamondreasoning
86.1%

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

Intelligence Indexreasoning
25.0%

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.

Reference

Qwen/Qwen3-Max questions

How much does Qwen/Qwen3-Max cost?

Qwen/Qwen3-Max is priced at $1.20 per million input tokens and $6.00 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-Max?

Qwen/Qwen3-Max has a context window of 256K tokens. That's roughly 341 words of input you can fit in a single prompt.

How does Qwen/Qwen3-Max perform on benchmarks?

Qwen/Qwen3-Max scores 86.1% on GPQA Diamond, 83.6% on τ²-Bench, 28.0% on Humanity's Last Exam. 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-Max do?

Qwen/Qwen3-Max supports tool calling, prompt caching. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use Qwen/Qwen3-Max 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-Max". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run Qwen/Qwen3-Max through Requesty?

Yes. Qwen/Qwen3-Max 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-Max", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call Qwen/Qwen3-Max through one endpoint

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