LLM Hub: Multi-Model AI Orchestration
TL;DR: Built a platform that intelligently routes tasks to the best AI model among 20+ options, or combines multiple models in parallel. Beats using any single AI.
The Problem
Every LLM excels at different things. GPT-5 handles complex reasoning, Claude writes cleanly, Gemini processes numbers well, Perplexity researches. Using just one means leaving performance on the table.
The Solution
LLM Hub automatically analyzes your task and routes it to the right model(s). No need to guess. It works in four modes:
1. Single Mode - Just use one model (standard chatting)
2. Sequential Mode - Models work in pipeline: research → analysis → synthesis → report
3. Parallel Mode - Multiple models tackle the same task simultaneously, then an aggregator combines results
4. Specialist Mode - The interesting one. For complex tasks, the system:
Decomposes the request into specialized sub-tasks
Routes each piece to the best model for that type of work
Runs everything in parallel
Synthesizes results into one coherent answer
Example: "Build a price-checking tool and generate a market report with visualizations"
Code generation → Claude
Price analysis → Claude Opus
Business writing → GPT-5
Data visualization → Gemini
All run simultaneously. You get expert-level output for each component, faster than doing it sequentially.
How Mode Selection Works
The router evaluates:
Task complexity (word count, number of steps, technical density)
Task type (code, research, creative writing, data analysis, math, etc.)
Special requirements (web search? deep reasoning? multiple perspectives? images?)
Time vs. quality tradeoff
Language (auto-translates)
Then automatically picks the optimal mode and model combination.
Current Features
20+ AI Models: GPT-5, Claude Sonnet 4.5, Opus 4.1, Gemini 2.5 Pro, Grok 4, Mistral Large, etc.
Real-time Web Search: Integrated across all models
Image & Video Generation: DALL-E 3, Sora 2, Imagen 3
Visual Workflow Builder: Drag-and-drop task automation
Scheduled Tasks: Set and forget recurring jobs
Export: Word, PDF, Excel, JSON, CSV
Performance Tracking: See which models work best for your use cases
Pricing
Free tier: 10 runs/day. Pay-as-you-go credits (no subscription). Fast models are free. Premium models (Claude Opus, GPT-5, etc.) cost 2-3.5 credits.
Open Questions
How are others solving the multi-model routing problem?
Any thoughts on the decomposition strategy for Specialist Mode? We're using prompt-based analysis right now but open to better approaches.
For those working with multiple LLMs, what's your biggest pain point?
Try it: https://llm-hub.tech
Feedback welcome, especially from anyone working on similar orchestration problems.
LLM Hub: Multi-Model AI Orchestration TL;DR: Built a platform that intelligently routes tasks to the best AI model among 20+ options, or combines multiple models in parallel. Beats using any single AI. The Problem Every LLM excels at different things. GPT-5 handles complex reasoning, Claude writes cleanly, Gemini processes numbers well, Perplexity researches. Using just one means leaving performance on the table. The Solution LLM Hub automatically analyzes your task and routes it to the right model(s). No need to guess. It works in four modes: 1. Single Mode - Just use one model (standard chatting) 2. Sequential Mode - Models work in pipeline: research → analysis → synthesis → report 3. Parallel Mode - Multiple models tackle the same task simultaneously, then an aggregator combines results 4. Specialist Mode - The interesting one. For complex tasks, the system:
Decomposes the request into specialized sub-tasks Routes each piece to the best model for that type of work Runs everything in parallel Synthesizes results into one coherent answer
Example: "Build a price-checking tool and generate a market report with visualizations"
Code generation → Claude Price analysis → Claude Opus Business writing → GPT-5 Data visualization → Gemini
All run simultaneously. You get expert-level output for each component, faster than doing it sequentially. How Mode Selection Works The router evaluates:
Task complexity (word count, number of steps, technical density) Task type (code, research, creative writing, data analysis, math, etc.) Special requirements (web search? deep reasoning? multiple perspectives? images?) Time vs. quality tradeoff Language (auto-translates)
Then automatically picks the optimal mode and model combination. Current Features
20+ AI Models: GPT-5, Claude Sonnet 4.5, Opus 4.1, Gemini 2.5 Pro, Grok 4, Mistral Large, etc. Real-time Web Search: Integrated across all models Image & Video Generation: DALL-E 3, Sora 2, Imagen 3 Visual Workflow Builder: Drag-and-drop task automation Scheduled Tasks: Set and forget recurring jobs Export: Word, PDF, Excel, JSON, CSV Performance Tracking: See which models work best for your use cases
Pricing Free tier: 10 runs/day. Pay-as-you-go credits (no subscription). Fast models are free. Premium models (Claude Opus, GPT-5, etc.) cost 2-3.5 credits. Open Questions
How are others solving the multi-model routing problem? Any thoughts on the decomposition strategy for Specialist Mode? We're using prompt-based analysis right now but open to better approaches. For those working with multiple LLMs, what's your biggest pain point?
Try it: https://llm-hub.tech Feedback welcome, especially from anyone working on similar orchestration problems.