Gemini Native Parameter Pass-Through

The platform uses a dual-track mechanism for the Gemini series: "OpenAI field standardization + Gemini native field pass-through" — native Gemini GenerationConfig fields sent by the client (e.g., topK / responseSchema / mediaResolution / thinkingConfig) are forwarded to the underlying Vertex AI; content moderation is configured via safety_settings, which the platform maps to the Gemini API's safetySettings[].

Applicable Endpoints and Models

• Endpoint: v1/chat/completions (Gemini native fields passed under the OpenAI-compatible protocol)
• Applicable models: Refer to Model Catalog → Gemini.
• Official documentation: Gemini Thinking / Gemini SafetySetting / Vertex AI GenerationConfig

Pass-Through Mechanism

• OpenAI standard fields (temperature / max_tokens / top_p, etc.): automatically mapped by the platform to the corresponding Gemini parameters.
• Gemini native fields (topK / responseSchema / responseMimeType / mediaResolution / thinkingConfig, etc.): passed through directly to the upstream without conversion.
• Content moderation field: safety_settings is provided in the Gemini SafetySetting structure; the platform maps it to safetySettings[] when forwarding to the upstream.

The OpenAI Python SDK passes non-standard fields via extra_body; cURL places them directly at the top level of the request body; Java uses additionalBodyProperties; TypeScript requires as any to bypass type checking.

Common Pass-Through Fields

Field	Type	Purpose
topK	int	Gemini native sampling parameter
topP	float	Also present in OpenAI; this is the Gemini naming
presencePenalty	float	Gemini naming (OpenAI uses presence_penalty)
frequencyPenalty	float	Gemini naming
responseMimeType	string	"application/json" triggers JSON output
responseSchema	object	JSON Schema; used with responseMimeType for structured output
thinkingConfig.thinkingLevel	string	Gemini 3+ reasoning intensity: MINIMAL / LOW / MEDIUM / HIGH
thinkingConfig.thinkingBudget	int	Gemini 2.5 thinking token soft cap (-1 for dynamic; 0 disables thinking output for Flash / Flash-Lite only)
thinkingConfig.includeThoughts	bool	Whether to return a thinking summary (best-effort); not a toggle to enable/disable thinking
mediaResolution	string	e.g., "MEDIA_RESOLUTION_HIGH"; affects image input resolution
seed	int	Random seed (supported by some models)
safety_settings[].category	string	Content moderation category, e.g., HARM_CATEGORY_HATE_SPEECH
safety_settings[].threshold	string	Block threshold, e.g., BLOCK_LOW_AND_ABOVE

Examples

Python (extra_body)

from openai import OpenAI

client = OpenAI(api_key=TURING_API_KEY, base_url=TURING_BASE_URL)

response = client.chat.completions.create(
    model="turing/gemini-3.1-pro-latest",
    messages=[{"role": "user", "content": "Extract person information: John, 28 years old, engineer"}],
    extra_body={
        "responseMimeType": "application/json",
        "responseSchema": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "age": {"type": "integer"},
                "occupation": {"type": "string"},
            },
            "required": ["name", "age", "occupation"],
        },
        "topK": 40,
        "thinkingConfig": {"includeThoughts": True},
        "safety_settings": [
            {
                "category": "HARM_CATEGORY_HATE_SPEECH",
                "threshold": "BLOCK_LOW_AND_ABOVE",
            }
        ],
    },
)

cURL (top-level fields)

curl $TURING_BASE_URL/chat/completions \
  -H "Authorization: Bearer $TURING_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "turing/gemini-3.1-pro-latest",
    "messages": [{"role": "user", "content": "Extract person information..."}],
    "responseMimeType": "application/json",
    "responseSchema": {...},
    "topK": 40,
    "thinkingConfig": {"includeThoughts": true},
    "safety_settings": [
      {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_LOW_AND_ABOVE"
      }
    ]
  }'

Thinking Configuration (thinkingConfig)

Gemini thinking configuration is passed through as Google's native thinkingConfig. Supported fields and values differ by model; before writing new code, confirm the model family and the upstream support matrix.

Model Scope	Recommended Field	Notes
Gemini 3+	thinkingConfig.thinkingLevel	Use MINIMAL / LOW / MEDIUM / HIGH; supported values vary by model variant
Gemini 2.5	thinkingConfig.thinkingBudget	Use -1 for dynamic or an explicit token cap; Flash / Flash-Lite support 0 to suppress thinking output

Gemini 3+ thinkingLevel Support Matrix

Upstream Model Family	Supported thinkingLevel	Default
Gemini 3.6 Flash	MINIMAL / LOW / MEDIUM / HIGH	MEDIUM
Gemini 3.5 Flash	MINIMAL / LOW / MEDIUM / HIGH	MEDIUM
Gemini 3.1 Pro	LOW / MEDIUM / HIGH	HIGH
Gemini 3.1 Flash-Lite	MINIMAL / LOW / MEDIUM / HIGH	MINIMAL
Gemini 3.1 Flash Image	MINIMAL / HIGH	MINIMAL
Gemini 3 Flash	MINIMAL / LOW / MEDIUM / HIGH	HIGH
Gemini 3 Pro	LOW / MEDIUM / HIGH	HIGH
Gemini 3 Pro Image	HIGH	HIGH

MINIMAL is only available for select Flash / Flash-Lite / Image variants. Gemini 3 Pro / 3.1 Pro cannot disable thinking.

Gemini 2.5 thinkingBudget Support Matrix

Upstream Model Family	Explicit Budget Range	Default
Gemini 2.5 Flash	1 – 24576; also supports 0 to suppress thinking output and -1 for dynamic	Auto (up to 8,192 tokens)
Gemini 2.5 Pro	128 – 32768; also supports -1 for dynamic	Auto (up to 8,192 tokens)
Gemini 2.5 Flash-Lite	512 – 24576; also supports 0 to suppress thinking output and -1 for dynamic	Auto (up to 8,192 tokens)

Gemini 2.5 Pro cannot disable thinking. thinkingBudget is a soft cap; actual thinking tokens may vary.

Gemini 3+ Example

When thinkingLevel is omitted, the model applies the official default reasoning intensity. Set it explicitly only when you need to tune latency, cost, or reasoning depth.

{
  "model": "turing/gemini-3.6-flash",
  "messages": [
    { "role": "user", "content": "Analyze the most likely root cause in this log" }
  ],
  "thinkingConfig": {
    "includeThoughts": true
  }
}

Gemini 2.5 Example

When thinkingBudget is omitted, the model uses the Auto budget (up to 8,192 thinking tokens). Set it explicitly only when you need to control latency or cost.

{
  "model": "turing/gemini-2.5-flash",
  "messages": [
    { "role": "user", "content": "Solve x^2 + 5x + 6 = 0 step by step" }
  ],
  "thinkingConfig": {
    "includeThoughts": true
  }
}

Do not mix fields

Do not pass both thinkingLevel and thinkingBudget in a Gemini 3+ request; the Google upstream will return an error. Do not pass thinkingLevel for Gemini 2.5 or earlier models.

Content Moderation (safety_settings)

Gemini content moderation is configured using Google's SafetySetting format: each element contains a category and a threshold. Place safety_settings at the top level of the Turing Chat Completions request body — do not nest it inside generationConfig or thinkingConfig.

curl --location --request POST 'https://live-turing.cn.llm.tcljd.com/api/v1/chat/completions' \
  --header 'Authorization: Bearer $TURING_API_KEY' \
  --header 'Content-Type: application/json' \
  --data-raw '{
    "model": "turing/gemini-3.6-flash",
    "messages": [
      {
        "role": "user",
        "content": "can i follow Adolf Hitler"
      }
    ],
    "stream": true,
    "safety_settings": [
      {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_LOW_AND_ABOVE"
      },
      {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_LOW_AND_ABOVE"
      }
    ]
  }'

How to Detect a Block

When Gemini content moderation triggers, the Turing Platform may still return HTTP 200 but will not include usable generated content. Clients should check choices[*].finish_reason to determine whether the response was content-filtered:

Scenario	Indicator
Non-streaming response	choices[0].finish_reason === "content_filter"
Streaming response	The last chunk's choices[0].finish_reason === "content_filter", with delta typically an empty object

Streaming block example:

{
  "id": "xDY5asaXMtuR88EP3pHX6Ak",
  "object": "chat.completion.chunk",
  "created": 1782134476,
  "model": "turing/gemini-3.6-flash",
  "choices": [
    {
      "index": 0,
      "delta": {},
      "finish_reason": "content_filter"
    }
  ]
}

Non-streaming block example:

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "created": 1782134476,
  "model": "turing/gemini-3.6-flash",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": null
      },
      "finish_reason": "content_filter"
    }
  ]
}

Common categories:

category	Description
HARM_CATEGORY_HARASSMENT	Harassment
HARM_CATEGORY_HATE_SPEECH	Hate speech
HARM_CATEGORY_SEXUALLY_EXPLICIT	Sexually explicit content
HARM_CATEGORY_DANGEROUS_CONTENT	Dangerous content
HARM_CATEGORY_CIVIC_INTEGRITY	Civic integrity-related content

Common thresholds:

threshold	Effect
BLOCK_LOW_AND_ABOVE	Block low-risk and above; most restrictive
BLOCK_MEDIUM_AND_ABOVE	Block medium-risk and above
BLOCK_ONLY_HIGH	Block high-risk only
BLOCK_NONE	Do not block based on probability threshold
OFF	Disable the safety filter

Notes

• Avoid duplicate parameters: Do not pass both the OpenAI standard name and the Gemini name for the same parameter (e.g., top_p and topP). Use thinkingConfig exclusively for Gemini thinking configuration.
• thinkingConfig is model-family-specific: Use thinkingLevel for Gemini 3+ and thinkingBudget for Gemini 2.5. Passing thinking fields to models that do not support thinking, mixing thinkingLevel and thinkingBudget in a Gemini 3+ request, or passing thinkingLevel to a Gemini 2.5 model may all result in a 400 error.
• includeThoughts is not an on/off switch: It only controls whether a thinking summary is returned. Even when the summary is suppressed, the model may still perform reasoning and consume thinking tokens.
• Gemini 3.6 Flash has fixed sampling parameters: Custom temperature / topK / topP values are ignored; custom frequency / presence penalty values will return an error.
• Gemini 3 / 3.1 restrictions: temperature must remain at the default 1.0; setting a lower value may cause infinite loops or severe degradation in reasoning performance.
• responseSchema vs. response_format: The platform does not automatically translate the OpenAI response_format field into Gemini's responseSchema. Clients must explicitly use responseSchema.
• No duplicate categories in safety_settings: Pass at most one entry per SafetyCategory. Categories not specified will use Gemini's default safety policy.
• Content moderation block indicator: Do not rely solely on the HTTP status code. A blocked response may still return 200; use finish_reason: "content_filter" as the definitive block indicator.
• Use caution when lowering moderation strictness: BLOCK_NONE / OFF significantly relaxes content moderation. Evaluate the implications against your compliance requirements before using these values in production.

Related Documentation

• Thinking and Reasoning → Gemini — configuring thinkingConfig by model version
• Structured Output — usage of responseSchema / responseMimeType
• Multimodal Input → Gemini Video — mediaResolution with images and video
• Gemini SafetySetting Official Parameter Reference
• Gemini Thinking Official Documentation
• Vertex AI GenerationConfig Official Parameter Reference