Qwen · Frontier

Qwen3.8 Max 0902

Added Sep 2, 2026

Qwen3.8 Max 0902 is Alibaba's September 2 checkpoint of its flagship Qwen3.8 Max model for coding, knowledge work, data analysis, and long-running agent workflows. It supports text, image, video, PDF input, selectable thinking, tool calling, structured output, and a near-million-token context window.

Reasoning
Tools
Vision
Structured output
PDF input
Video input

Input: Text, Images, Video
Output: Text
Reasoning effort: none · low · medium · high · max
Discovery tier: Frontier

Pricing

USD per 1M tokens

Input: $2.00
Output: $6.00
Cache read: $0.25
Cache write: $2.50

Reference prices as published by the model provider. API example

Specifications

Context window: 991.8K
Max output: 131.1K

Side by side

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Public evaluations

Benchmarks

There are no public benchmark results for Qwen3.8 Max 0902 yet.

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits: 88.0%; About $0.46 per 1M input tokens with caching
Request success (24h): 97.5%; Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 84.8%

Healthy (98% or more)
Degraded (90–98%)
Down (under 90%)
No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart

curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.8-max-0902",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'

import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="qwen/qwen3.8-max-0902",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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