Qwen 3.8 Max·续
</details>
<details>
<summary>Streaming Response — Enable streaming for real-time output</summary>
const response = await env.AI.run(
'alibaba/qwen3.8-max',
{
messages: [{ content: 'Explain the concept of recursion with a simple example.', role: 'user' }],
stream: true,
stream_options: { include_usage: true },
},
)
console.log(response)
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "alibaba/qwen3.8-max",
"messages": [
{
"content": "Explain the concept of recursion with a simple example.",
"role": "user"
}
],
"stream": true,
"stream_options": {
"include_usage": true
}
}'
Recursion is a way of solving a problem where a function calls itself to solve smaller versions of the same problem.
A recursive function usually has two parts:
1. **Base case** — when to stop
2. **Recursive case** — calling itself with a smaller input
Example: calculating `3!` factorial.
```python
def factorial(n):
if n == 1: # base case
return 1
else:
return n * factorial(n - 1) # recursive case
```
Calling:
```python
print(factorial(3))
```
This works like:
```python
factorial(3) = 3 * factorial(2)
factorial(2) = 2 * factorial(1)
factorial(1) = 1
```
So:
```python
3 * 2 * 1 = 6
```
Recursion is useful when a problem can be broken into smaller repeated subproblems.
[
{
"model": "qwen3.8-max",
"id": "chatcmpl-93de192c-09a4-9b99-8c9a-55f6302cc5c5",
"created": 1786048673,
"object": "chat.completion.chunk",
"usage": null,
"choices": [
{
"logprobs": null,
"index": 0,
"delta": {
"content": "",
"role": "assistant",
"reasoning_content": ""
},
"finish_reason": null
}
]
},
{
"model": "qwen3.8-max",
"id": "chatcmpl-93de192c-09a4-9b99-8c9a-55f6302cc5c5",
"choices": [
{
"delta": {
"content": "",
"reasoning_content": "We"
},
"index": 0,
"finish_reason": null,
"logprobs": null
}
],
"created": 1786048673,
"object": "chat.completion.chunk",
"usage": null
},
"... 73 more chunks omitted ...",
{
"choices": [],
"created": 1786048673,
"id": "chatcmpl-93de192c-09a4-9b99-8c9a-55f6302cc5c5",
"model": "qwen3.8-max",
"object": "chat.completion.chunk",
"usage": {
"completion_tokens": 263,
"completion_tokens_details": {
"reasoning_tokens": 47,
"text_tokens": 263
},
"prompt_tokens": 59,
"prompt_tokens_details": {
"cached_tokens": 0,
"text_tokens": 59
},
"total_tokens": 322
}
}
]
</details>
Parameters
Schema variant
Chat CompletionsResponses
▶messages[]
arrayrequired
temperature
numberminimum: 0maximum: 2
max_tokens
numberexclusiveMinimum: 0
max_completion_tokens
numberexclusiveMinimum: 0
top_p
numberminimum: 0maximum: 1
frequency_penalty
numberminimum: -2maximum: 2
presence_penalty
numberminimum: -2maximum: 2
stream
boolean
▶stream_options{}
object
▶tools[]
array
tool_choice
response_format
▶modalities[]
array
▶audio{}
object
reasoning_effort
stringOptional reasoning control; availability and accepted values are model-dependent.
▶input
one ofrequired
instructions
string
temperature
numberminimum: 0maximum: 2
max_output_tokens
numberexclusiveMinimum: 0
top_p
numberminimum: 0maximum: 1
stream
boolean
▶tools[]
array
tool_choice
▶text{}
object
▶reasoning{}
object
id
string
object
string
created
number
model
string
▶choices[]
array
▶usage{}
object
id
string
object
stringconst: response
created_at
number
model
string
▶output[]
array
output_text
string
status
stringenum: in_progress, completed, failed, incomplete
▶usage{}
object
API Schemas (Raw)
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description: Alibaba's Wan 2.6 text-to-image model generating images from text prompts with optional negative prompts and customizable dimensions. title: Wan 2.6 Image image: https://developers.cloudflare.com/og-docs.png
Documentation Index
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Use this file to discover all available pages before exploring further.

