Point systems make multi-model AI easier to package, but they can also make usage feel opaque. LLMWise takes the opposite approach: show the model path, keep Auto cheap by default, and make cost visible after the response.
Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.
Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.
This comparison covers where teams typically hit friction moving from Poe Points to a multi-model control plane.
| Capability | Poe Points | LLMWise |
|---|---|---|
| Usage unit | Points | Transparent model and token-aware usage |
| Cheap default | Choose a lower-cost bot manually | Auto routing built in |
| Cost learning loop | Abstracted by points | Response-level cost feedback |
| Model comparison | Manual | Built-in Compare mode |
| Best for | Bot marketplace users | Cost-conscious multi-model users |
A point balance is simple, but it can hide why one message costs more than another. LLMWise keeps model and cost feedback closer to the actual response.
Auto routing reduces the need to manually hunt for cheaper bots. The default behavior is to try to keep routine work on lower-cost model paths.
The strongest LLMWise use case is not just cheaper chat; it is learning which model is worth paying for on which task.
POST /api/v1/chat
{
"model": "auto",
"optimization_goal": "cost",
"messages": [{"role": "user", "content": "..." }],
"stream": true
}Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.
Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.
Pricing changes, new model launches, and optimization tips. No spam.