Qwen 3.8 Max pricing for sentiment analysis

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=100,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.001600 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 sentiment analysis benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.000220/call (86% less, 91/100 quality).

What drives sentiment analysis cost

Short text in, a label or score out: the cheapest shape an API call can take. Output pricing is irrelevant at this size. Because these run in bulk over historical data as often as in real time, the batch API discount typically applies, and the effective rate lands well below list.

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 500/100 token split, output accounts for 38% of the $0.001600 per-call cost.

Qwen 3.8 Max quality benchmarks for sentiment analysis

Benchmark data for sentiment analysis 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