External Model - Language Model - Gemini

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Frontier Text Reasoning via Google's Gemini Models

Read, reason over, or write text inside a CNAPS Studio flow with Google's Gemini family — Pro tiers for advanced reasoning, Flash tiers for fast everyday tasks, with next-gen Gemini 3 preview options. Runs on Google's servers via API.

🔑 Requires a Google API key

Click to use Gemini on CNAPS Studio

What it does

This node connects your CNAPS Studio flows to Google's Gemini large language models through the Gemini API. Use it whenever a flow needs a model to read, reason over, or write text — interpreting the output of a vision model, drafting or enhancing prompts for image/video generators, summarizing, translating, or restructuring data. Because these run on Google's servers, you get frontier general-purpose reasoning without hosting a model locally.

Problem it solves

• Frontier reasoning without infrastructure — No local GPU or weights; the node calls Google's hosted models directly.
• Stable and preview tracks — Choose stable gemini-2.5 tiers for production flows, or gemini-3 preview tiers for next-gen capability and speed.
• Glue between vision and generation nodes — Turn raw model output into readable reports, or expand simple text into detailed generation prompts.

Which tier to pick

Reach for a pro tier for the hardest multi-step reasoning, math, and coding; gemini-2.5-pro as the balanced everyday default for production; and a flash tier when speed and cost matter more than depth on simple tasks.

Configuring the Node

Basic Settings

Model name

System prompt or instruction — Tell the model how to behave. Examples:

• "Summarize the detection results in 2-3 sentences."
• "Convert these labels into a product description."
• "Translate the following text to French."

Advanced Options

Maximum tokens to generate (default: 2048)

• Controls response length. Increase for longer outputs.

Sampling temperature (default: 0.7, range: 0.0–1.0)

• Lower (0.0–0.3): Focused, consistent responses
• Higher (0.7–1.0): Creative, varied responses

Use Cases

Direction 1: Vision Model → Language Model (Analysis)

Best when vision output is complex or needs interpretation.

Example: Scene Segmentation → Report

Input - Image → Scene Segmentation (MaskFormer) → Language Model → Output - Text Viewer

System prompt:

Convert this segmentation data into a structured scene report. Include: 1) Setting type, 2) People count, 3) Key objects detected, 4) Overall scene summary. Use plain language, not raw data.

Direction 2: Language Model → Image Generation (Prompt Enhancement)

Input - Text Input → Language Model → Image Generation (Z-Image-Turbo) → Output - Image Viewer

System prompt:

You are an expert image prompt engineer. Transform the user's simple description into a detailed, vivid prompt optimized for AI image generation. Include: subject details, setting/environment, lighting, artistic style, mood/atmosphere, and composition. Output only the enhanced prompt, nothing else.

How Gemini compares

Gemini is one of three external LLM options in CNAPS Studio:

Compliance & Provenance

For more on how we classify models and mark outputs, see our AI Policy.

Run it now

Click to use Gemini