Alibaba Cloud AI Text Qwen Generation

Use when generating or reasoning over text with Alibaba Cloud Model Studio Qwen flagship text models (qwen3-max, qwen3.5-plus, qwen3.5-flash, snapshots...

Category: provider

Model Studio Qwen Text Generation

Validation

bash

mkdir -p output/aliyun-qwen-generation
python -m py_compile skills/ai/text/aliyun-qwen-generation/scripts/prepare_generation_request.py && echo "py_compile_ok" > output/aliyun-qwen-generation/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-generation/validate.txt is generated.

Output And Evidence

• Save prompt templates, normalized request payloads, and response summaries under output/aliyun-qwen-generation/.
• Keep one reproducible request example with model name, region, and key parameters.

Use this skill for general text generation, reasoning, tool-calling, and long-context chat on Alibaba Cloud Model Studio.

Critical model names

Prefer the current flagship families:

• qwen3-max
• qwen3-max-2026-01-23
• qwen3.5-plus
• qwen3.5-plus-2026-02-15
• qwen3.5-flash
• qwen3.5-flash-2026-02-23

Common related variants listed in the official model catalog:

• qwen3.5-397b-a17b
• qwen3.5-122b-a10b
• qwen3.5-35b-a3b
• qwen3.5-27b

Prerequisites

• Install SDK in a virtual environment:

bash

python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope

• Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.

Normalized interface (text.generate)

Request

• messages (array, required): standard chat turns.
• model (string, optional): default qwen3.5-plus.
• temperature (number, optional)
• top_p (number, optional)
• max_tokens (int, optional)
• enable_thinking (bool, optional)
• tools (array, optional)
• response_format (object, optional)
• stream (bool, optional)

Response

• text (string): assistant output.
• finish_reason (string, optional)
• usage (object, optional)
• raw (object, optional)

Quick start (OpenAI-compatible endpoint)

bash

curl -sS https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-plus",
    "messages": [
      {"role": "system", "content": "You are a concise assistant."},
      {"role": "user", "content": "Summarize why object storage helps media pipelines."}
    ],
    "stream": false
  }'

Local helper script

bash

python skills/ai/text/aliyun-qwen-generation/scripts/prepare_generation_request.py \
  --prompt "Draft a concise architecture summary for a media ingestion pipeline." \
  --model qwen3.5-plus

Operational guidance

• Use snapshot IDs when reproducibility matters.
• Prefer qwen3.5-flash for lower-latency simple tasks and qwen3-max for harder multi-step tasks.
• Keep tool schemas minimal and explicit when enabling tool calls.
• For multimodal input, route to dedicated VL or Omni skills unless the task is primarily text-centric.

Output location

• Default output: output/aliyun-qwen-generation/requests/
• Override base dir with OUTPUT_DIR.

References

• references/sources.md