Tier 2

Build with Gemini on Orbit

Orbit routes a large share of its generation and reasoning to Gemini models — including flash models for fast classification and vision models for screenshot-driven builds.

Gemini AI Builder

Gemini is a workhorse in Orbit's router: flash models handle fast intent classification, and vision models drive screenshot-to-app builds by analyzing attached images.

In plain English

Gemini is a Google workhorse. Orbit uses its flash models for instant classification, vision models for screenshot-to-app builds, and frontier models for code.

Gemini AI Builder — build, chat, research, and create media in one AI workspace on Orbit
Orbit routes a large share of its generation and reasoning to Gemini models — including flash models for fast classification and vision models for screenshot-driven builds.

How it works in Orbit

Gemini brings Google-scale multimodal ability — text, images, and audio in one model. Orbit builds apps that use Gemini's strengths: vision analysis, search-grounded answers, and media understanding.

Multimodal in one call

Gemini accepts images alongside text, which powers vision features. Orbit generates upload UIs and analysis flows that pass media to the model.

  • Image upload UI
  • Vision analysis
  • Mixed-media prompts

Google-scale grounding

Gemini's access to Google Search grounding suits recommendation and research apps. Orbit structures the queries and displays grounded answers with sources.

  • Search-grounded answers
  • Source links in UI
  • Research flows

Fast, integrated, global

Gemini's speed and availability make it a strong default for high-volume features. Orbit's provider interface keeps alternatives ready.

  • High-volume friendly
  • Provider-swappable
  • Global availability
  1. Describe the feature

    Vision, grounding, or chat.

  2. Generate the UI

    Upload and results screens.

  3. Wire Gemini

    Connect your Google AI key.

  4. Ship

    Multimodal features live.

The challenge

Try it yourself

Run this one on a competitor first — then on Orbit. Same prompt, and keep score.

Build

Try this one elsewhere first.

Screenshot an app and hand it to two builders. Compare who actually reads the image and who just guesses at the design.

Research

Ask a research tool to go deep on one topic.

Then ask Orbit. Compare how many rounds it runs, how many sources it actually reads — we target 65+ — and whether every claim in the final report has a link attached. Bring a bigger coffee mug.

Chat

Ask a plain chatbot the same question.

One just talks. Orbit's agent actually searches the web, runs code, reads your files, and comes back with something done. Compare how many tabs you opened and how many are still open when you're finished.

Image & video

Generate the same image, then edit it.

Ask two tools for the same hero shot, then ask both to recolor it, fill in the background, and animate it. Compare who hands you a first draft and who keeps refining in the same conversation.

150+

models routed per task

1h

cloud sandbox sessions

25

tool calls per chat turn

8–14

deep research rounds

What you can do with Gemini AI Builder on Orbit

150+ model router

Every task routed to the strongest model for the job.

Failover ladder

Provider outages fall back instead of erroring.

One interface

Chat, research, build, and media on a single model layer.

Compare sibling stacks

Frequently asked questions

How does Orbit use Gemini?

Fast flash models classify intents in milliseconds, vision models read screenshots for clone builds, and frontier Gemini models power code generation and deep research.

Does Gemini support image input on Orbit?

Orbit generates the media-upload interface and passes images with prompts to the model, which is where Gemini's multimodal ability shines.

Can I use Gemini for image understanding in my app?

Yes — Gemini's multimodal input handles text plus images in a single call, which Orbit wires for screenshot analysis, visual QA, and image-caption features straight out of the generated app.