Qwen family

Alibaba Cloud’s Qwen text models, including the Qwen3.8, Qwen3.7 and Qwen3.6 series, Qwen Flash and Qwen Plus. They are called through the OpenAI-compatible API, and the Qwen3.x series also supports the Anthropic-native API. For image generation models, see Qwen-Image.

Common tasks

Models

For the full model list and live prices, see the model page , or query the Models API. You can omit the qwen/ prefix from model IDs.

Qwen models are served by Alibaba Cloud Model Studio through two providers, the China site (aliyun) and the international site (alicloud), selected automatically by default. For how to select one, see Provider routing.

Examples

import os
from openai import OpenAI
 
client = OpenAI(base_url="https://api.ofox.io/v1", api_key=os.environ["OFOX_API_KEY"])
 
resp = client.chat.completions.create(
    model="qwen/qwen3.8-flash",
    messages=[{"role": "user", "content": "Explain idempotency in one sentence."}],
    extra_body={"enable_thinking": False},
)
print(resp.choices[0].message.content)

curl https://api.ofox.io/v1/chat/completions \
  -H "Authorization: Bearer $OFOX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.8-flash",
    "messages": [{"role": "user", "content": "Explain idempotency in one sentence."}],
    "enable_thinking": false
  }'

Thinking mode

In our tests on the same math problem, Qwen3.8 Flash thought for about 5,800 tokens by default and took about 1.5 minutes, while Qwen3.6 Flash thought for over 30,000 tokens and took more than 4 minutes; with thinking off, both returned within a few seconds.

Capping thinking length

When you need thinking but want to control latency and cost, pass thinking_budget (a cap on thinking tokens):

import os
from openai import OpenAI
 
client = OpenAI(base_url="https://api.ofox.io/v1", api_key=os.environ["OFOX_API_KEY"])
 
resp = client.chat.completions.create(
    model="qwen/qwen3.8-flash",
    messages=[{"role": "user", "content": "Find all positive integers n less than 50 for which n² + n + 41 is not prime."}],
    extra_body={"enable_thinking": True, "thinking_budget": 1000},
)
msg = resp.choices[0].message
details = getattr(msg, "reasoning_details", None) or []
print("[Thinking]", "".join(d.get("text", "") for d in details)[:200], "…")
print("[Answer]", msg.content)
print("Thinking tokens:", resp.usage.completion_tokens_details.reasoning_tokens)

• In our tests thinking_budget is enforced strictly: set to 200, thinking used exactly 200 tokens.
• enable_thinking and thinking_budget are not standard OpenAI SDK parameters, so put them in extra_body; when sending raw HTTP requests, put them at the top level of the request body.
• Thinking tokens are billed at the output rate; the count is in usage.completion_tokens_details.reasoning_tokens.

Reading the thinking content

• In streaming responses, thinking content is in delta.reasoning_content, the same as the official Alibaba Cloud Model Studio API.
• In non-streaming responses, thinking content is in message.reasoning_details; concatenate the text of each item in order (see the example above).

Image input

Models that support image input use the standard image_url content block:

import base64
import os
from openai import OpenAI
 
client = OpenAI(base_url="https://api.ofox.io/v1", api_key=os.environ["OFOX_API_KEY"])
img_b64 = base64.b64encode(open("photo.png", "rb").read()).decode()
 
resp = client.chat.completions.create(
    model="qwen/qwen3.8-flash",
    messages=[{"role": "user", "content": [
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_b64}"}},
        {"type": "text", "text": "Describe this image."},
    ]}],
    extra_body={"enable_thinking": False},
)
print(resp.choices[0].message.content)

Qwen3.7 Max, Qwen Plus and Qwen Flash are text-only; use one of the image-capable models in the table above instead.

Use in Claude Code

The Qwen3.x series and Qwen Flash support the Anthropic-native API at https://api.ofox.io/anthropic:

• On the Anthropic API, thinking content is returned as thinking blocks.
• The Qwen3.8 series thinks a lot, so set a large enough max_tokens. In our tests, with max_tokens at 4000 the response could contain only a thinking block and no answer.
• Qwen Plus does not support the Anthropic API; calls return protocol_not_supported.

For full Claude Code setup steps, see Claude Code setup.

JSON output and tool calling

response_format supports {"type": "json_object"} and {"type": "json_schema", ...}; tool calling uses the standard tools parameter. When using json_object, state “return JSON” and the required fields in the prompt. For the syntax, see Structured output and Function calling.

Prompt caching

Qwen automatically caches repeated request prefixes, with no changes to your request. The cached amount is in usage.prompt_tokens_details.cached_tokens, and the cached portion is billed at the cache read rate. Caching is best-effort; requests with the same prefix may still miss.

Common errors

For other general errors, see Error Handling.

Official docs

• Alibaba Cloud Model Studio · Models 
• Alibaba Cloud Model Studio · Deep thinking 
• Alibaba Cloud Model Studio · OpenAI compatibility 

The official docs describe behavior when calling Alibaba Cloud Model Studio directly. When calling through OfoxAI, model IDs and endpoints on this page take precedence.

FAQ