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Qwen/Qwen3-Max

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.

Tool callingCachingJSON schema
Try Qwen/Qwen3-MaxCompareDocs

Specifications

Context window256K tokens
Max output—
API typechat
AddedMay 27, 2026
Model IDdeepinfra/Qwen/Qwen3-Max
Data retentionNo
Used for trainingNo
Provider location🇺🇸 US
Privacy policyDeepInfra Privacy Policy→

Benchmarks

Released 2026-01-26
Coding Indexcoding
30.5%

Artificial Analysis Coding Index — a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
86.1%

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

Intelligence Indexreasoning
39.8%

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

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality — always test on your own workload.

Pricing

Input / 1M
$1.20
Output / 1M
$6.00
Cache write
—
Cache read / 1M
$0.24
Estimated cost
100K input + 10K output$0.18
1M input + 100K output$1.80
10M input + 1M output$18.00

Requesty charges exactly what the upstream provider charges — no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to deepinfra/Qwen/Qwen3-Max.

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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)

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Frequently asked 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. Requesty charges exactly what the upstream provider charges — we don't add markup.
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, 43.1% on SciCode. 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, 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-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.

Access Qwen/Qwen3-Max through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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