Provider comparison workflow
Compare outputs from different LLMs before choosing one.
- 1Send the same prompt to OpenAI and Gemini
- 2Ask a review model to score both answers
- 3Keep the best result
- 4Export the final response
AI-Flow
Multi AI Workflow Builder
Chain different AI models in one visual workflow. Use cheaper models for simple steps and stronger models where quality matters most.
Nano Banana 2
Google
GPT Image 2
OpenAI
Claude Sonnet 4.6
Anthropic
“Subtle fashion film. Woman wearing tortoiseshell sunglasses, slight confident head turn, warm studio light gliding across the lenses. Hair moves gently. Slow motion, luxury campaign feel. No background change, keep studio setting.”
Seedance 2
ByteDance
Flux 2 Max
Replicate
Nano Banana 2Google
GPT Image 2OpenAI
Claude Sonnet 4.6Anthropic
“Subtle fashion film. Woman wearing tortoiseshell sunglasses, slight confident head turn, warm studio light gliding across the lenses. Hair moves gently. Slow motion, luxury campaign feel. No background change, keep studio setting.”
Seedance 2ByteDance
Flux 2 MaxReplicate
One workflow, many AI models
Combine providers and model types while keeping control over cost, speed, and output quality at each step.Chain different AI models
Use cheaper models for simple steps, stronger models for quality-critical steps, and compare providers without rebuilding everything.
- GPT
- Claude
- Gemini
- GPT
- Claude
- Gemini
Balance cost, speed, and quality
Route classification, drafting, review, image generation, and formatting steps to different models based on what each step actually needs.
Multi-model workflow examples
Practical workflows with clear inputs, model steps, and final outputs.
Quality escalation workflow
Use inexpensive models first, then escalate only when needed.
- 1Classify the request with a small model
- 2Draft with a fast model
- 3Improve important sections with a stronger model
- 4Format the final output
Text and image workflow
Use a language model to guide visual generation.
- 1Write image prompts with an LLM
- 2Generate images with an image model
- 3Create variations
- 4Collect the best outputs
Research workflow
Split research, synthesis, and formatting across models.
- 1Collect source text
- 2Summarize with one model
- 3Critique with another model
- 4Produce a final brief
Related workflow pages
Frequently Asked Questions
Can I use OpenAI and Gemini in the same workflow?
Yes. AI-FLOW lets you combine providers such as OpenAI, Gemini, Anthropic, Replicate, and others in the same visual workflow.
Can I compare outputs from different AI models?
Yes. You can send the same input to multiple models, compare the outputs, and keep the version that fits your quality, speed, or cost target.
Can I use image and text models together?
Yes. A workflow can use text models to draft prompts, image models to generate visuals, and other models to analyze, summarize, or format results.
Why use multiple AI models instead of one?
Different steps need different strengths. You might use a cheaper model for classification, a stronger model for final copy, and an image model for visual generation.
Can I change providers later?
Yes. You can iterate on your workflow by swapping models and comparing results without rebuilding the whole process from scratch.