GPT-4o vs GPT-4o mini
| Spec | GPT-4o | GPT-4o mini |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Input price (per 1M) | $2.50 | $0.15 |
| Output price (per 1M) | $10.00 | $0.60 |
| Context window | 128,000 | 128,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Verdict
Default to GPT-4o mini and only upgrade to GPT-4o on prompts where you've measured mini falling short. Most production workloads don't need GPT-4o's reasoning quality — and the 17× price gap is real money at scale.
Cost example
For a 1,000-token prompt with a 200-token reply:
GPT-4o: 1000 × $2.50/M + 200 × $10/M = $0.0045 per call
GPT-4o mini: 1000 × $0.15/M + 200 × $0.60/M = $0.000270 per callMini costs 17× less per call. For 1,000,000 calls per month: $4,500 vs $270 — a $4,230 difference. At 100M calls/month, that's $423,000 saved per month.
What you give up with mini
The capability gap is real but narrower than the price gap. Mini falls behind GPT-4o on:
- Multi-step reasoning — chain-of-thought tasks with 4+ steps.
- Complex instruction-following — prompts with many simultaneous constraints.
- Code generation on harder problems — algorithmic challenges, careful refactors.
- Subtle creative tasks — voice consistency in long-form writing.
What stays the same:
- Same tokenizer (
o200k_base) — token counts are identical - Same 128k context window
- Same vision capability
- Same function-calling / structured outputs API
- Same latency tier (mini is actually slightly faster)
When to use which
Use GPT-4o mini when:
- High-volume classification, extraction, or labeling
- Short Q&A in chatbots
- Real-time UX where latency matters
- First-pass routing in agent systems
- RAG over routine documents
Use GPT-4o when:
- Multi-step reasoning is required
- The task has many constraints to balance simultaneously
- Code generation on non-trivial problems
- Quality measurably matters more than per-call cost on a labeled eval set
How to decide
Run both on a labeled eval. If mini hits your accuracy bar, ship it — the savings are massive. If it doesn't, escalate to GPT-4o (or even Claude Sonnet) and revisit periodically as mini gets better.
The single most common mistake teams make: defaulting to GPT-4o because "it's the better model" without measuring whether their actual workload needed the upgrade.