DashScope native SDK & async tasks

Alibaba Cloud Bailian (DashScope) Qwen models can be called via the OpenAI SDK like OpenAI (translation mode), or via the DashScope native SDK through the passthrough entry /v1/services/{*rest}. The native SDK needs just one variable changed:

pip install dashscope requests

import dashscope

dashscope.base_http_api_url = "http://localhost:7890/v1"   # point at the gateway
dashscope.api_key = "<your access key>"

Passthrough vs translation: choosing a path

Entry	Behavior	When to use
/v1/services/{*rest}	Passthrough: body forwarded to DashScope as-is, no protocol conversion	DashScope native SDK, or DashScope-specific fields / async tasks
/v1/chat/completions	Translation: OpenAI ↔ DashScope format	OpenAI SDK, calling Qwen as an OpenAI model
/v3/{model}/{*rest}	identity passthrough (client protocol == target protocol)	when you need to bypass the /v1/services prefix

Text generation

from dashscope import Generation

resp = Generation.call(
    model="qwen3.8-max",
    messages=[{"role": "user", "content": "Introduce yourself in one sentence"}],
)
print(resp.output.choices[0].message.content)

Gotcha: message.content is an array. DashScope passthrough returns output.choices[0].message.content as an array ([{"text": "..."}]), not a string, and may also carry reasoning_content (the thinking process). Printing it as a string the way older docs do yields [{...}]. Extract the text with content[0]["text"] or "".join(p.get("text","") for p in content).

Image (synchronous API)

from dashscope.aigc.image_generation import ImageGeneration

resp = ImageGeneration.call(
    model="wan2.6-image",
    messages=[{"role": "user", "content": [{"text": "A cat swimming in an ocean of code, cyberpunk style"}]}],
)
# The synchronous API returns in one shot; the image is in message.content
print(resp.output.choices[0].message.content)

Video (async task)

Video generation is a two-stage "submit task → poll result" flow. The SDK's fetch polling path differs from the gateway's task-query path (/v1/services/{model}/tasks/{task_id}), so raw HTTP is used to demonstrate the full flow:

import time, requests

BASE = "http://localhost:7890/v1/services"
H = {"Authorization": "Bearer <your access key>", "Content-Type": "application/json"}

# 1) Submit the async video task (the model must be configured with direct_path + async_mode,
#    see the DashScope quick deploy)
r = requests.post(f"{BASE}/aigc/video-generation/video-synthesis", headers=H, json={
    "model": "wanx2.1-t2v",
    "input": {"prompt": "A cat swimming in an ocean of code, cyberpunk style"},
    "parameters": {"size": "1280*720"},
})
task_id = r.json()["output"]["task_id"]

# 2) Poll the task status and take the video URL on success
while True:
    s = requests.get(f"{BASE}/wanx2.1-t2v/tasks/{task_id}", headers=H).json()
    status = s["output"]["task_status"]
    if status == "SUCCEEDED":
        print(s["output"]["video_url"])
        break
    if status in ("FAILED", "CANCELED"):
        raise RuntimeError(s)
    time.sleep(5)

Video model config (direct_path + async_mode) — see Connect a DashScope upstream → Video generation. "Billing per second" only holds for models that declare a per_second billing mode; wanx2.1-t2v bills per your actual config, see Pricing & billing.

Embeddings / rerank / ASR

# Embeddings
from dashscope import TextEmbedding
r = TextEmbedding.call(model="text-embedding-v4", input="hello")
print(r.output["embeddings"][0]["embedding"])

# Rerank
from dashscope import Rerank
r = Rerank.call(model="gte-rerank", query="cat", documents=["a cat", "a car"])
print([x["relevance_score"] for x in r.output["results"]])

Embeddings/rerank can also go over the OpenAI protocol (/v1/embeddings, /v1/rerank) — the gateway interoperates openai_embeddings/openai_rerank with dashscope. Either path works; see Protocol interop matrix.

Identity passthrough /v3/{model}/{*rest}

When the client protocol equals the model's configured target protocol, use /v3/{model}/{*rest} — the body is forwarded as-is, no conversion:

curl -X POST "http://localhost:7890/v3/qwen3.8-max/services/aigc/text-generation/generation" \
  -H "Authorization: Bearer <your access key>" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.8-max","input":{"messages":[{"role":"user","content":"hi"}]},"parameters":{"result_format":"message"}}'

Next

• Full upstream config steps and kind variants: see Connect a DashScope upstream.
• DashScope realtime models (dashscope_realtime) via /v1/realtime: see Realtime & audio.