Qwen 3.8 Max pricing for blog writing

Quick start

Call Qwen 3.8 Max with the OpenAI Python SDK pointed at DashScope:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
    api_key=os.environ["DASHSCOPE_API_KEY"],
)
response = client.chat.completions.create(
    model="qwen3.8-max",
    max_tokens=8000,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0500 per call at standard rates
# Input:  $2.00/MTok
# Output: $6.00/MTok

How Qwen 3.8 Max compares on cost

Per-call cost for all models scoring 50+ on blog writing benchmarks. Hover for details.

Cheapest viable option: Gemini 3.6 Flash at $0.0307/call (39% less, 76/100 quality).

What drives blog writing cost

The most output-heavy use case tracked here. A brief of a few hundred tokens produces an article of several thousand, so output pricing is essentially the entire cost and input rate is noise. Generating section by section rather than whole-article gives you more control over both quality and spend, at the cost of more calls.

For Qwen 3.8 Max specifically, that means output tokens cost 3.0x more than input, at $6.00 vs $2.00 per MTok. At this use case’s 1,000/8,000 token split, output accounts for 96% of the $0.0500 per-call cost.

Qwen 3.8 Max quality benchmarks for blog writing

Benchmark data for blog writing is pending for Qwen 3.8 Max. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for Qwen 3.8 Max. We recommend testing against your own evaluation criteria.

About Qwen 3.8 Max

Prices from help.aliyun.com/en/model-studio (qwen3.8-max, read 2026-10-11). Cache hit: $0.25/MTok input. Not yet benchmarked, so never recommended.

Other models from Alibaba (Qwen)

You can compare Qwen 3.8 Max head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions