Qwen 3.8 Max pricing for resume parsing

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=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.009800 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 resume parsing benchmarks. Hover for details.

Cheapest viable option: Gemini 3 Flash at $0.003650/call (63% less, 86/100 quality).

What drives resume parsing cost

Resumes are longer than invoices and less uniformly structured, so input runs higher while output stays compact. Like invoice parsing this is a bulk, latency-tolerant workload where the batch API discount applies to the entire request. The parsing instructions are identical across every candidate, which puts them squarely in the cached prefix.

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 2,500/800 token split, output accounts for 49% of the $0.009800 per-call cost.

Qwen 3.8 Max quality benchmarks for resume parsing

Benchmark data for resume parsing 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