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LLM models

Gemini 3.1 Pro

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Gemini 3.1 Pro — frontier reasoning and generation through one API.

modelIdgemini-3-1-pro
Modalitytext
PricingSee this model on the Pricing page for the current per-call price (with your markup).

Operations

OperationmodelIdEndpointRequired input
Defaultgemini-3-1-proPOST /api/v1/generateprompt
Poll taskGET /api/v1/task/{id}?model=gemini-3-1-pro

Input parameters

FieldTypeRequiredValues / example
promptstringYesText value (example: Explain how honeybees tell the hive where flowers are, using one clear everyday analogy anyone can picture.)
memorybooleanNoAutomatically append previous messages to maintain multi-turn context. May increase token usage. (true/false) (default: false)
thinkingbooleanNoInclude the model's thinking process in the response (true/false) (default: true)
reasoning_effortstringNoControl reasoning depth: Low for faster responses, High for deeper analysis (options: Low | High) (default: High)
imagesstring[]NoUp to 4 images per message — JPEG, PNG, GIF or WebP, 5 MB each. Send each one as a data: URL or an https URL. Images are billed as input tokens. (image URL)
systemstringNoSystem instruction for the model. Defaults to "You are Gemini 3.1 Pro, developed by Google." when omitted.
messagesarrayNoA conversation instead of a single `prompt`: up to 20 objects of { "role", "content" }, where role is user, assistant or system. Send `prompt` or `messages`, not both. Do not flatten a conversation into one prompt with "User:" / "Assistant:" labels — the model reads that as a single message, and a model using web search then searches for the whole block instead of your question.

Example request

bash
curl -X POST https://you.bot/api/v1/generate \
  -H "Authorization: Bearer $YOUBOT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"modelId":"gemini-3-1-pro","input":{"prompt":"Explain how honeybees tell the hive where flowers are, using one clear everyday analogy anyone can picture.","memory":false,"thinking":true,"reasoning_effort":"High","images":["data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAIAAAACCAIAAAD91JpzAAAAFklEQVR4nGP8z4AATAxIHFQeMg8OAgB1yQIDpk8YwwAAAABJRU5ErkJggg=="]}}'