Alibaba Cloud Model Studio:OpenAI compatible - Chat

Request body

modelstring(Required)

The model name.

Supported models: Qwen Large Language Model (commercial and open source versions), Qwen-VL, Qwen-Coder, Qwen-Omni, Qwen-Math, DeepSeek, Kimi, GLM, and MiniMax.

For specific model names and billing details, see the Model Studio console.

messagesarray(Required)

The context passed to the large language model, arranged in conversational order.

[
    "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/xzsgiz/football1.jpg",
    "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/tdescd/football2.jpg",
    "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/zefdja/football3.jpg",
    "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241108/aedbqh/football4.jpg"
]

streamboolean(Optional) Default value: false

Specifies whether to reply in streaming output mode. For more information, see Streaming output.

Valid values:

• false: The model returns the complete content after generation is finished.
• true: The model outputs content as it is generated. A data chunk is returned each time a part of the content is generated. You must read these chunks to assemble the complete reply.

We recommend that you set this to true to improve the user experience and reduce the risk of timeouts.

stream_optionsobject(Optional)

Configuration items for streaming output. This parameter takes effect only when stream is set to true.

modalitiesarray(Optional) Default value: ["text"]

The modality of the output data. This parameter applies only to Qwen-Omni models. For more information, see Non-real-time (Qwen-Omni).

Valid values:

• ["text","audio"]: Output text and audio. Applies to models that support audio output.
• ["text"]: Output text only.

qwen3.8-omni-flash produces text only. See the Qwen3.8-Omni-Flash example.

audioobject(Optional)

The voice and format of the output audio. This parameter applies to Qwen-Omni models that support audio output and requires the modalities parameter to be set to ["text","audio"]. For more information, see Non-real-time (Qwen-Omni).

temperaturefloat(Optional)

The sampling temperature, which controls the diversity of the text generated by the model.

A higher temperature results in more diverse text, while a lower temperature results in more deterministic text.

Value range: [0, 2)

Both temperature and top_p can control the diversity of the generated text. We recommend that you set only one of them. For more information, see Overview.

For Qwen3.8-Omni-Flash, the default is 0.6 in thinking mode and 0.7 in non-thinking mode.

Do not modify the default temperature value for QVQ models.

top_pfloat(Optional)

The probability threshold for nucleus sampling, which controls the diversity of the text generated by the model.

A higher top_p results in more diverse text. A lower top_p results in more deterministic text.

Value range: (0, 1.0]

Both temperature and top_p can control the diversity of the generated text. We recommend that you set only one of them. For more information, see Overview.

For Qwen3.8-Omni-Flash, the default is 0.95 in thinking mode and 0.8 in non-thinking mode.

Do not modify the default top_p value for QVQ models.

top_kinteger(Optional)

Specifies the number of candidate tokens to sample from during generation. A larger value results in more random output, while a smaller value results in more deterministic output. If set to null or a value greater than 100, the top_k strategy is disabled, and only the top_p strategy takes effect. The value must be an integer greater than or equal to 0.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"top_k":xxx}.

Do not modify the default top_k value for QVQ models.

repetition_penaltyfloat(Optional)

The repetition penalty for consecutive sequences during model generation. Increasing repetition_penalty can reduce repetition in the model's output. A value of 1.0 means no penalty. There is no strict value range, as long as it is greater than 0.

For Qwen3.8-Omni-Flash, the default is 1.05 in both thinking and non-thinking modes.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"repetition_penalty":xxx}.

When you use the qwen-vl-plus_2025-01-25 model for text extraction, set repetition_penalty to 1.0.

Do not modify the default repetition_penalty value for QVQ models.

presence_penalty float(Optional)

Controls the content repetition when the model generates text.

Value range: [-2.0, 2.0]. Positive values reduce repetition, while negative values increase it.

In scenarios that require diversity, fun, or creativity, such as creative writing or brainstorming, you can increase this value. In scenarios that emphasize consistency and term accuracy, such as technical documents or formal texts, you can decrease this value.

For Qwen3.8-Omni-Flash, the default is 0.0 in thinking mode and 1.5 in non-thinking mode.

When you use the qwen-vl-plus model for text extraction, set presence_penalty to 1.5.

Do not modify the default presence_penalty value for QVQ models.

response_formatobject (Optional) Default value: {"type": "text"}

The format of the response. Valid values:

• {"type": "text"}: Outputs a text reply.
• {"type": "json_object"}: Outputs a standard JSON formatted string.
• {"type": "json_schema", "json_schema": {...}}: Outputs a JSON string that strictly conforms to the specified JSON Schema, which gives you precise control over the output structure and field types.

For more information, see Structured output. The json_object and json_schema modes support different models. For more information, see Supported models.

If you specify {"type": "json_object"}, you must explicitly instruct the model to output JSON in the prompt, such as "Please output in JSON format". Otherwise, an error occurs. If you specify {"type": "json_schema", ...}, the prompt does not need to contain the JSON keyword.

max_tokensinteger(Optional, to be deprecated)

This parameter will be deprecated. For new integrations, use max_completion_tokens.

The meaning of this parameter varies by model:

• deepseek-v4.1-flash, deepseek-v4-pro, deepseek-v4-pro-0813, deepseek-v4-flash, deepseek-v4-flash-0731: The maximum number of tokens for the sum of the model's answer and chain-of-thought content. If the model's output exceeds this value, generation stops early, and the returned finish_reason is length.
• glm-5.3: max_tokens is the maximum number of tokens for the sum of the model's answer and chain-of-thought content. If the model's output exceeds this value, generation stops early and the returned finish_reason is length. glm-5.3 ignores the thinking_budget parameter.
• glm-5.2: When the thinking_budget parameter is not passed, max_tokens is the maximum number of tokens for the sum of the model's answer and chain-of-thought content, and if the model's output exceeds this value, generation stops early with the returned finish_reason being length. When the thinking_budget parameter is passed, max_tokens is the maximum number of tokens for the model's answer only, and the number of tokens for the chain-of-thought part is controlled separately by thinking_budget.
• Other models: The maximum number of tokens for the model's answer (excluding chain-of-thought). If the generated content exceeds this value, generation stops early, and the returned finish_reason is length.

The default and maximum values are both the model's maximum output length.

max_completion_tokensinteger(Optional)

The maximum length of the model's output, including the chain-of-thought and the model's answer. If the model's output exceeds this value, generation stops early, and the returned finish_reason is length.

The default and maximum values are both the model's maximum output length.

Difference from max_tokens: max_completion_tokens limits the complete model output (chain-of-thought + answer), while max_tokens only limits the answer part. For thinking models, we recommend that you use max_completion_tokens.

The following models are supported:

• Qwen Max: Qwen3.7-Max and later models
• Qwen Plus: Qwen3.5-Plus and later models
• Qwen Flash: Qwen3.5-Flash and later models
• Kimi: kimi-k2.5 and later models
• GLM: glm-5 and later models
• MiniMax: MiniMax-M2.5 and later models
• DeepSeek: deepseek-v3, deepseek-r1, deepseek-r1-0528, deepseek-v3.1, deepseek-v3.2, deepseek-v3.2-exp, deepseek-v4-pro, deepseek-v4-flash, and later models

The models listed above do not include models supplied directly by third parties.

There may be a difference of up to 10 tokens between the actual output token count and the specified max_completion_tokens value.

vl_high_resolution_imagesboolean(Optional) Default value: false

Specifies whether to increase the pixel limit for input images to the pixel count that corresponds to 16384 tokens. For more information, see Processing high-resolution images.

• vl_high_resolution_images: true uses a fixed resolution strategy and ignores the max_pixels setting. If the resolution is exceeded, the total pixel count of the image is scaled down to stay within this limit.

  Click to view the pixel limits for each model

  When vl_high_resolution_images is True, the pixel limits vary by model:

  • For the qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash, qwen3.8-2.4t-a95b, qwen3.8-27b, qwen3.8-omni-flash, Qwen3.7 series, Qwen3.6 series, Qwen3.5 series, Qwen3-VL series, qwen-vl-max, qwen-vl-max-0813, qwen-vl-plus, qwen-vl-plus-0815, and models, the value is 16777216. (Each Token corresponds to 32*32 pixels. The total value is calculated as 16384*32*32.)
  • QVQ series, other Qwen2.5-VL series models: 12845056 (1 token corresponds to 28*28 pixels, which is 16384*28*28)

• vl_high_resolution_images is false, the pixel limit is determined by max_pixels. If the input image's pixel count exceeds max_pixels, the image is scaled down to within the max_pixels limit. The default pixel limit for each model is the default value of max_pixels.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"vl_high_resolution_images":xxx}.

ninteger(Optional) Default value: 1

The number of responses to generate. The value range is 1-4. This is suitable for scenarios that require multiple candidate responses, such as creative writing or ad copy.

Only supported by Qwen3 (non-thinking mode) models.

If the tools parameter is passed, set n to 1.

Increasing n increases the output token consumption but not the input token consumption.

enable_thinking boolean (Optional)

When you use a mixed-thinking model, which supports both thinking and non-thinking modes, this parameter specifies whether to enable thinking mode. This applies to Qwen3.7, Qwen3.6, Qwen3.5, Qwen3, Qwen3-Omni-Flash, and Qwen3-VL models, along with the DeepSeek-V4.1-Flash, DeepSeek-V4-Pro/V4-Flash series, DeepSeek-V3.2/V3.2-exp/V3.1 series, Kimi-K2.7-code (thinking model only), Kimi-K2.6/K2.5 series, and GLM series. The DeepSeek-V4 series enables thinking by default. You can adjust the inference intensity with the reasoning_effort parameter.

Valid values:

• true: Enable

  When enabled, the thinking content is returned in the reasoning_content field.

• false: Disable

Default values for different models: Supported models

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"enable_thinking": xxx}.

If you call over HTTP directly (for example, with curl) instead of using the OpenAI SDK, do not use extra_body. Simply place enable_thinking at the top level of the request body (body), alongside parameters such as model and messages, for example "enable_thinking": true.

The MiniMax and MiniMax-M3 models from Xiyu Technology do not use this parameter. Instead, use the thinking parameter.

thinking object (Optional) Default value: {"type":"adaptive"}

Controls the thinking mode of MiniMax/MiniMax-M3 supplied by MiniMax.

thinking.type valid values:

• adaptive: Automatic (default). The model decides whether to think.
• disabled: Disables thinking and replies directly.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"thinking": {"type": "adaptive"}}.

preserve_thinking boolean (Optional) Default value: false (Default value for qwen3.8-max/qwen3.8-flash:true)

Specifies whether to append the reasoning_content from assistant messages in the conversation history to the model input. This is suitable for scenarios where the model needs to refer to the historical thinking process.

To reference reasoning from a previous turn, include it in the historical assistant message's reasoning_content field in messages. See Pass the thinking process.

Currently supported by qwen3.7-max, qwen3.7-max-2026-05-20 and subsequent snapshots, qwen3.6-max-preview, qwen3.7-plus, qwen3.7-plus-2026-05-26, qwen3.6-plus, qwen3.6-plus-2026-04-02, qwen3.7-flash, qwen3.7-flash-2026-07-15, qwen3.6-flash, qwen3.6-flash-2026-04-16, qwen3.8-max series (enabled by default), qwen3.8-flash (enabled by default), qwen3.8-omni-flash (enabled by default), kimi-k2.6 (deployed on Alibaba Cloud Model Studio), kimi-k2.7-code (deployed on Alibaba Cloud Model Studio, enabled by default), kimi/kimi-k2.7-code-highspeed (supplied by Moonshot AI, enabled by default), and kimi/kimi-k2.7-code (supplied by Moonshot AI, enabled by default).

Important (qwen3.8-max/qwen3.8-flash): When using qwen3.8-max/qwen3.8-flash, preserve_thinking defaults to true. You must send back all historical reasoning_content in the reasoning_content field. Do NOT concatenate reasoning_content into the content field. Doing so may degrade model performance.

• For the supported qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash, Qwen3.7, Qwen3.6, and Kimi models listed above, enabling this parameter when historical messages lack reasoning_content does not cause an error.
• When enabled, the reasoning_content from the historical conversation is included in the input token count and is billed.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"preserve_thinking": True}.

thinking_budget integer (Optional)

The maximum number of tokens for the thinking process. This applies to the qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash, qwen3.8-2.4t-a95b, qwen3.8-27b, as well as Qwen3.7, Qwen3.6, Qwen3.5, Qwen3-VL, Qwen3, GLM and Kimi models, except kimi-k3, which does not support this parameter. For more information, see Limit thinking length.

For these models, the default value is the model's maximum chain-of-thought length. For more information, see the model list.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"thinking_budget": xxx}.

use_multichannel boolean (optional)

Controls whether spatial information in multichannel audio is parsed. Applies to audio input for qwen3.8-omni-flash. Defaults to false, which parses all audio as mono. When true and the input has two channels (left and right) or four channels (FOA, in WYZX order), the model parses spatial audio information. Set it at the top level of the HTTP request body, or pass extra_body={"use_multichannel": True} with the OpenAI Python SDK.

reasoning_effort string (Optional)

Controls the inference intensity of models. The valid values and default values vary by model.

DeepSeek-V4 and GLM series (Default value: high)

Valid values:

• high: High-intensity inference
• max: Maximum-intensity inference

low and medium are mapped to high, and xhigh is mapped to max.

This applies to glm-5.2, glm-5.1, glm-5, deepseek-v4-pro, and deepseek-v4-flash (excluding deepseek-v4-flash-0731).

glm-5.3、ZHIPU/GLM-5.3、kimi-k3model(supplied by Alibaba Cloud): Default value:max

Valid values:

• max (default): deep reasoning
• high: enhanced reasoning
• low: light reasoning

The glm-5.3 series models always perform thinking. enable_thinking supports only true, and passing false causes the API request to fail. kimi-k3 supports passing false to disable thinking.

deepseek-v4-flash-0731 & deepseek-v4-pro-0813: Default value:high

Valid values:

• max (default): Maximum-intensity inference
• high: Standard inference
• low: Low-intensity inference

OpenAI standard value mapping: medium is mapped to high, xhigh is mapped to high.

deepseek-v4.1-flash: Default value:high

Valid values:

• max: Maximum-intensity inference
• high (default): High-intensity inference
• low: Low-intensity inference

minimal is mapped to low, medium and xhigh are mapped to high, and ultra is mapped to max.

kimi/kimi-k3 (Default value: max; only max is supported)

Valid value:

• max: Maximum-intensity inference

qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash, qwen3.8-2.4t-a95b, qwen3.8-27b: Default value:xhigh

Valid values:

• xhigh (default): Maximum-intensity inference
• medium: Standard inference
• low: Low-intensity inference

OpenAI standard value mapping: max is mapped to xhigh, high is mapped to xhigh, minimal is mapped to low, and none is mapped to enable_thinking=False.

Setting values other than the above valid values and mapped values will cause an error.

For the qwen3.8-max, qwen3.8-max-0902, qwen3.8-flash, qwen3.8-2.4t-a95b, qwen3.8-27b, reasoning_effort and thinking_budget cannot be set at the same time. Setting both will cause an error. However, they support mutual conversion:

• When thinking_budget is not set, the reasoning_effort levels are automatically mapped to thinking_budget: low corresponds to 4096, medium corresponds to 16384, and xhigh corresponds to 262144.
• When reasoning_effort is not set, thinking_budget is automatically mapped back to reasoning_effort: 0–4096 corresponds to low, 4097–16384 corresponds to medium, and 16385–262144 corresponds to xhigh.
• When neither is set, the default thinking_budget (131072) and default reasoning_effort (xhigh) are used.

qwen3.8-omni-flash has thinking enabled by default, with reasoning_effort defaulting to xhigh. In Chat Completions, reasoning_effort accepts none, minimal, low, medium, high, xhigh, and max; do not set it together with thinking_budget. For parameter placement and examples, see Qwen3.8 Omni.

Choose low, medium, or xhigh for the thinking effort. For compatibility, minimal maps to low, high and max map to xhigh, and none disables thinking.

reasoning_effort is a standard OpenAI parameter. With the Python SDK, pass reasoning_effort="high" directly instead of placing it in extra_body.

tool_stream boolean (Optional) Default value: false

Takes effect only when stream=true. This parameter is currently supported only by Qwen and GLM series.

• qwen-max series: text modality of the qwen3.8-max and qwen3.7-max series
• qwen-plus series: text modality of the qwen3.7-plus and qwen3.6-plus series, and omni-modality of the qwen3.5-plus series
• qwen-flash series: omni-modality of the qwen3.8-flash, qwen3.7-flash, qwen3.6-flash and qwen3.5-flash series

tool_stream only affects complex tool parameters. For normal tool parameters, streaming output is enabled as long as stream=true. Complex tools are tools where some parameter types in the tool definition are array or object.

• tool_stream=false: Complex tool parameters are output at once. This is the default behavior, and complex formats are more accurate.
• tool_stream=true: Complex tool parameters are output in a stream, which avoids timeout risks for complex formats.

GLM series support list: glm-4.6, glm-4.7, glm-5, and glm-5.1.

• tool_stream=false: Tool parameters are output at once. This is the default behavior, and complex formats are more accurate.
• tool_stream=true: Tool parameters are output in a stream, which avoids timeout risks for complex formats.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"tool_stream": true}.

enable_code_interpreter boolean (Optional) Default value: false

Specifies whether to enable the code interpreter feature. For more information, see Code interpreter.

Valid values:

• true: Enable
• false: Disable

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"enable_code_interpreter": xxx}.

seedinteger(Optional)

A random number seed. This parameter is used to ensure reproducible results with the same input and parameters. If you pass the same seed value in a call and other parameters remain unchanged, the model returns the same result as much as possible.

Value range: [0,2<sup>31</sup>−1].

logprobs boolean (Optional) Default value: false

Specifies whether to return the log probabilities of the output tokens. Valid values:

• true

  Return

• false

  Do not return

Content generated during the thinking phase (reasoning_content) does not return log probabilities.

top_logprobs integer (Optional) Default value: 0

Specifies the number of most likely candidate tokens to return at each generation step.

Value range: [0, 5]

This parameter takes effect only when logprobs is true.

stopstring or array(Optional)

Used to specify stop words. When a string or token_id specified in stop appears in the generated text, generation stops immediately.

You can pass sensitive words to control the model's output.

When stop is an array, you cannot input both token_id and strings as elements. For example, you cannot specify ["Hello",104307].

toolsarray(Optional)

An array that contains one or more tool objects for the model to call in Function Calling. For more information, see Function calling.

If tools is set and the model determines that a tool needs to be called, the response returns tool information in tool_calls.

tool_choice string or object(Optional) Default value: auto

The tool selection strategy. To force a specific tool calling method for a certain type of problem, such as always using a specific tool or disabling all tools, you can set this parameter.

Valid values:

• auto

  The large language model chooses the tool strategy.

• none

  If you do not want to call a tool, you can set the tool_choice parameter to none.

• required

  To force at least one tool to be called, set the tool_choice parameter to required, and the model will always return tool call information.

  The Qwen series models do not support required: in non-thinking mode it cannot guarantee that a tool is always called, and in thinking mode required is not supported.

• {"type": "function", "function": {"name": "the_function_to_call"}}

  If you want to force a specific tool to be called, you can set the tool_choice parameter to {"type": "function", "function": {"name": "the_function_to_call"}}, where the_function_to_call is the name of the specified tool function.

  Thinking mode models do not support forcing a specific tool to be called.

parallel_tool_calls boolean (Optional) Default value: false

Specifies whether to enable parallel tool calling. For more information, see Parallel tool calling.

Valid values:

• true: Enable
• false: Disable

enable_search boolean(Optional) Default value: false

Specifies whether to enable web search. For more information, see Web search.

Valid values:

• true: Enable.

  If web search is not performed after enabling, you can optimize the prompt or set the forced_search parameter in search_options to enable forced search.

• false: Disable.

Enabling the web search feature may increase token consumption.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"enable_search": True}.

search_optionsobject(Optional)

The strategy for web search. For more information, see Web search.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"search_options": xxx}.

clear_thinkingboolean(Optional) Default value: false

Controls whether the reasoning_content (thinking process) from previous turns in a multi-turn conversation is used as context input for the model. This parameter is supported only by the GLM series models glm-5.3, glm-5.2, glm-5.1, glm-5, and glm-4.7. The default value is true for glm-5.3 and false for the other models.

This parameter is not a standard OpenAI parameter. When you call using the Python SDK, place it in the extra_body object. Configuration: extra_body={"skill": [...]}.

• true: Ignores the reasoning_content from previous turns and uses only visible text, tool calls, results, and other non-inference content as context input. This can reduce the context length and cost.
• false (Default): Retains the reasoning_content from previous turns and provides it to the model along with the context. If you want to enable Preserved Thinking, you must pass the historical reasoning_content completely, unmodified, and in the original order within messages. Missing, trimming, rewriting, or reordering degrades performance or causes it to fail.

Chat response object (non-streaming output)

idstring

The unique identifier for this call.

choicesarray

An array of content generated by the model.

createdinteger

The Unix timestamp, in seconds, when the request was created.

modelstring

The model that is used for this request.

object string

The value is always chat.completion.

service_tier string

This parameter is currently fixed to null.

system_fingerprintstring

This parameter is currently fixed to null.

usage object

The token consumption information for this request.

{
    "choices": [
        {
            "message": {
                "role": "assistant",
                "content": "I am a large-scale language model developed by Alibaba Cloud. My name is Qwen."
            },
            "finish_reason": "stop",
            "index": 0,
            "logprobs": null
        }
    ],
    "object": "chat.completion",
    "usage": {
        "prompt_tokens": 3019,
        "completion_tokens": 104,
        "total_tokens": 3123,
        "prompt_tokens_details": {
            "cached_tokens": 2048
        }
    },
    "created": 1735120033,
    "system_fingerprint": null,
    "model": "qwen3.8-max",
    "id": "chatcmpl-6ada9ed2-7f33-9de2-8bb0-78bd4035025a"
}

Chat response chunk object (streaming output)

idstring

The unique identifier for this call. Each chunk object has the same ID.

choicesarray

An array of content generated by the model, which can contain one or more objects. If the include_usage parameter is set to true, choices is an empty array in the last chunk.

createdinteger

The timestamp when this request was created. Each chunk has the same timestamp.

modelstring

The model that is used for this request.

object string

The value is always chat.completion.chunk.

service_tier string

This parameter is currently fixed to null.

system_fingerprintstring

This parameter is currently fixed to null.

usage object

The tokens consumed by this request. It is displayed only in the last chunk when include_usage is set to true.

{"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"","function_call":null,"refusal":null,"role":"assistant","tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen3.8-max","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
{"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":"I am","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen3.8-max","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
{"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":" a large-scale","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen3.8-max","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
{"id":"chatcmpl-e30f5ae7-3063-93c4-90fe-beb5f900bd57","choices":[{"delta":{"content":" language","function_call":null,"refusal":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null}],"created":1735113344,"model":"qwen3.8-max","object":"chat.completion.chunk","service_tier":null,"system_fingerprint":null,"usage":null}
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