Replacing GPT-4.1 Mini?
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 Mini
GPT-4.1 Nano
| Field | GPT-4.1 Mini | → | GPT-4.1 Nano | Impact |
|---|---|---|---|---|
| Input price | $0.4/M | → | $0.1/M | Save 75% |
| Output price | $1.6/M | → | $0.4/M | Save 75% |
| Context window | 1M tokens | → | 1M tokens | Same capacity |
| Vision input | ✓ supported | → | ✓ supported | Preserved |
| Function calling | ✓ supported | → | ✓ supported | Preserved |
| Structured output (JSON) | ✓ supported | → | ✓ supported | Preserved |
| Prompt caching | ✓ supported | → | ✗ not supported | LOST — re-evaluate before switching |
| Fine-tuning | ✓ supported | → | ✗ not supported | LOST — re-evaluate before switching |
| Lifecycle | Active | → | Active | Same status |
Gemini 2.5 Flash-Lite
Google GeminiGPT-5 Nano
OpenAIGPT-5.4 Nano
OpenAIHow 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.