Feature request for product/service
– Other –
Describe the request
TL;DR: Instead of forcing users to choose which LLM to use (GPT-5, Claude 4.5, Gemini, etc.), we should let AI agents automatically route requests to the most suitable and cost-efficient model, based on intent, complexity, and context.
The Problem
Right now, most AI coding platforms and assistants require the user to manually pick a model.
That means users need to understand:
- Which model is best for which task,
- How much each model costs, and
- When to switch between them.
This leads to:
- Friction for non-technical users,
- Wasted tokens on expensive models for trivial queries, and
- Inconsistent quality when users make the wrong call.
The Proposal: Intelligent Model Routing Layer
Imagine a meta-AI layer sitting between the user and the models.
This layer:
- Interprets what the user wants to do,
- Classifies the intent (debugging, writing frontend, explaining code, etc.),
- Automatically routes the request to the most appropriate model.
The user just interacts with one “AI Coder,” while behind the scenes the system chooses the right model for the job.
Example Routing Logic
| Task | Best Model | Reason |
|---|---|---|
| Debugging complex backend logic | GPT-5 Codex | Deep reasoning and code understanding |
| Frontend/UI generation | Claude 4.5 | Strong in design-oriented, structured output |
| Quick factual Q&A | Cheaper LLM (e.g. GPT-3.5 Turbo or Claude 3 Haiku) | Low cost and fast response |
| Architecture planning or multi-file reasoning | GPT-5 or Gemini 1.5 Pro | Handles long context windows |
| Linting or test-generation | Llama 3 / Mixtral | Fast, cost-efficient for boilerplate tasks |
The routing layer could even combine models — for example:
- Claude drafts UI code,
- GPT-5 refines logic,
- A small model summarizes results.
Why It Matters
- Better UX: Users never need to think about “which model to pick.”
- Smarter Costs: Expensive models are used only when necessary.
- Consistent Output: Each model plays to its strengths.
- Easier Scaling: New models can be plugged in without changing user experience.
Future Possibilities
- Context-aware dynamic routing (e.g. track session history and switch models mid-conversation).
- Model performance analytics: learn which combinations produce the best results.
- Fine-tuned open models for low-cost fallback in offline or private environments.
What do you think, should we move toward agent-level orchestration rather than user-level model selection?
How would you design the routing logic or cost heuristics?
Would an open standard for Model Orchestration Protocols make sense here?