GPT-4o-mini vs GPT-4o: When to Use Which (2026 Guide)
A developer's guide to choosing between GPT-4o and GPT-4o-mini. Benchmarks, pricing, use cases, and how to automate model selection for optimal cost/quality.
GPT-4o-mini costs 33x less than GPT-4o for input tokens ($0.15 vs $5.00 per million). But when is it good enough? This guide breaks down exactly when to use each model.
Pricing at a Glance
| Model | Input $/1M | Output $/1M | Speed | Quality (MMLU) |
|---|---|---|---|---|
| GPT-4o | $5.00 | $15.00 | ~50 tok/s | 88.7% |
| GPT-4o-mini | $0.15 | $0.60 | ~120 tok/s | 82.0% |
Use GPT-4o-mini For
Classification and labeling: Sentiment analysis, topic categorization, intent detection. Mini matches 4o quality on most classification benchmarks.
Translation: Standard translations between common languages. Quality is nearly identical to 4o for European and Asian languages.
Simple Q&A: Factual questions, dictionary lookups, simple calculations. "What is the capital of France?" doesn't need a $5/1M model.
Data extraction: Pulling structured data from text - names, dates, amounts. Mini handles this excellently.
Summarization: Basic summarization of documents under 2000 tokens. Mini produces summaries that are 95%+ as good as 4o's.
Use GPT-4o For
Code generation: Writing non-trivial code, especially multi-file or multi-step implementations. 4o's reasoning advantage is significant here.
Complex reasoning: Multi-step logical problems, math proofs, strategic analysis. The 6.7% MMLU gap matters most for these tasks.
Creative writing: Long-form content where nuance, voice, and creativity matter. 4o produces notably better prose.
Multi-step instructions: Tasks requiring the model to follow a complex chain of instructions accurately.
Ambiguous queries: When the prompt is unclear or requires inference, 4o's stronger reasoning handles it better.
The 40-60 Rule
In most production apps, 40-60% of requests are simple enough for GPT-4o-mini. The trick is accurately classifying which requests need 4o and which don't.
Manual classification is fragile and doesn't scale. Automated complexity analysis looks at request length, keywords, code presence, and question complexity to route each request optimally.
Promptly does this automatically with its smart routing feature. Your code always requests gpt-4o, but simple requests silently route to mini. You get the cost savings of mini for simple tasks and the full power of 4o for complex ones.
Ready to cut your LLM costs?
Promptly optimizes every API call automatically - smart routing, caching, prompt compression, and context pruning in one proxy.