Replacing GPT-4.1 Nano?
Ranked replacement candidates, each shown with exactly what changes if you switch. Same-provider matches surface first because switching cost is lower; capability regressions are flagged in red.
GPT-4.1 Nano
GPT-4.1 Mini
| Field | GPT-4.1 Nano | → | GPT-4.1 Mini | Impact |
|---|---|---|---|---|
| Input price | $0.1/M | → | $0.4/M | +300% more expensive |
| Output price | $0.4/M | → | $1.6/M | +300% more expensive |
| Context window | 1M tokens | → | 1M tokens | Same capacity |
| Vision input | ✓ supported | → | ✓ supported | Preserved |
| Function calling | ✓ supported | → | ✓ supported | Preserved |
| Structured output (JSON) | ✓ supported | → | ✓ supported | Preserved |
| Lifecycle | Active | → | Active | Same status |
How candidates are ranked
Candidates are ranked by how much of the source model's profile each one preserves — weighing switching cost (same provider or model family surface first), capability parity (what you keep versus what you'd lose), context window, and pricing direction. A candidate that is itself deprecated is pushed down the list; one that's already retired is never recommended.
Ranking is purely algorithmic — no editorial weighting, no paid placement. Every value is pulled from each provider's own documentation; click any model name to see the source-linked detail.