As a user I now see 6-8 different ChatGPT models (GPT-4o, o3, o4-mini-high, GPT-4.5 preview …) but there is no guidance on which one fits a given prompt.
Non-technical users can’t memorise every model’s quirks, so we either:
- stay on the default model and get a sub-optimal answer, or
- waste time guessing, then re-running prompts on other models.
It feels like asking a question about space exploration at a reception desk: the receptionist does their best, even though there’s a Space-Research Room down the hall that could give a 100 % answer.
Proposed solution
1. “Auto” button with smart routing
- A lightweight meta-layer analyses each prompt (keywords, length, presence of code blocks, image attachments, sentiment, etc.).
- It silently forwards the prompt to the model that historically performs best for that task (e.g. o4-mini for code, GPT-4.5 for creative writing, GPT-4o for multimodal).
- If the classifier’s confidence is low (e.g. can’t decide between o3 and GPT-4o) it asks a one-line clarifying question, then routes accordingly.
2. Transparent multi-model suggestions
When two (or more) models are equally plausible, surface a compact comparison card instead of an unlabelled dropdown. Example:
| Model | Strength | Expected accuracy | Avg. latency | Estimated cost |
|---|---|---|---|---|
| o3 | Deep reasoning | ★★★★☆ | 6 s | 1× |
| GPT-4o | Multimodal, fluent style | ★★★★☆ | 3 s | 0.5× |
A user immediately sees why model A might be slower but more precise, or why model B is cheaper but less rigorous, and can override the auto-choice if needed.
Benefits
- Better answers first try – fewer re-runs save tokens and user time.
- Lower cognitive load – users don’t need to learn a model matrix.
- Trust & transparency – visible reasoning behind the recommendation.
- Scales gracefully – if new models appear, the router absorbs the complexity.
Nice-to-haves (later)
- Allow power users to download routing logs for analytics.
- Let admins set organisation-wide cost/latency thresholds to bias routing.
- Optional “time-shift” flag: “Use the most accurate model even if it means queuing until tomorrow” (e.g. research reports).
In short: an automatic, explain-able model picker would turn the current “reception desk” into a concierge that either answers perfectly or, when needed, walks me straight to the exact expert room.