Gemini 3.1 Pro Preview API
Released N/A | Up to 1M Tokens context | N/A parameters
Gemini 3.1 Pro Preview API enables Vision question answering over images and documents, Multi-step agent workflows that require reliable tool usage, Structured extraction (e.g., JSON) from screenshots and pages, and Coding assistance with multimodal context. Gemini 3.1 Pro Preview is a multimodal vision model for complex, agentic workflows with strong reasoning and tool/structured-output support. g., JSON) from screenshots and pages. Standout strengths include Large context window (up to 1M input tokens) and Strong reasoning and multimodal understanding. It is well suited for multimodal assistants that combine image understanding with grounded text reasoning in real-time workflows.
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="google/gemini-3.1-pro-preview", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image? Describe the main elements." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ], max_tokens=8192, temperature=0.2, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n")from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="google/gemini-3.1-pro-preview", messages=[ { "role": "user", "content": [ { "type": "text", "text": "What is in this image? Describe the main elements." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ], max_tokens=8192, temperature=0.2, top_p=1, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
Don't let your AI control you. Control your AI the Qubrid way!
Have questions? Want to Partner with us? Looking for larger deployments or custom fine-tuning? Let's collaborate on the right setup for your workloads.
"Qubrid's medical OCR and research parsing cut our document extraction time in half. We now have traceable pipelines and reproducible outputs that meet our compliance requirements."
Clinical AI Team
Research & Clinical Intelligence