o4-mini vs GPT-4 Turbo
Side-by-side comparison of o4-mini (OpenAI) and GPT-4 Turbo (OpenAI). Exact API pricing per million tokens, context windows, output speed, and total cost on real-world prompts.
Specifications
| Spec | o4-mini | GPT-4 Turbo |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model id | o4-mini | gpt-4-turbo |
| Input price (per 1M tokens) | $1.10 | $10.00 |
| Output price (per 1M tokens) | $4.40 | $30.00 |
| Context window | 200,000 | 128,000 |
| Output speed (tokens/sec) | ~80 | ~40 |
| Tokenizer encoding | o200k_base | cl100k_base |
Cost on real prompts
Total cost = (input tokens × input price) + (output tokens × output price). Numbers below use the exact pricing tables published by each provider.
| Scenario | Input | Output | o4-mini | GPT-4 Turbo | Cheaper |
|---|---|---|---|---|---|
| Short question + answer | 50 | 150 | $0.000715 | $0.005000 | o4-mini |
| Code review on one file | 500 | 1,500 | $0.007150 | $0.050000 | o4-mini |
| Long document summary | 5,000 | 500 | $0.007700 | $0.065000 | o4-mini |
| Heavy reasoning task | 2,000 | 8,000 | $0.037400 | $0.260000 | o4-mini |
| Full codebase analysis | 50,000 | 10,000 | $0.099000 | $0.800000 | o4-mini |
Want the exact cost for your prompt instead of these examples? Open the cost calculator pre-loaded with both models →
When to pick which
Heuristics derived from the spec table above. Always validate on your own prompts before committing — these are starting points, not verdicts.
Pick o4-mini for
- •output-heavy workloads (long-form generation, code, summaries) — o4-mini is meaningfully cheaper per output token
- •input-heavy workloads (long context, RAG, document QA) — o4-mini is cheaper per input token
- •tasks needing a larger context window — o4-mini fits 2x more tokens than gpt-4-turbo
- •latency-sensitive UX (chat, autocompletion) — o4-mini streams faster (~80 vs ~40 tok/s)
- •complex multi-step reasoning (math, debugging, planning) — o4-mini uses chain-of-thought internally
Pick GPT-4 Turbo for
No clear advantage on the data points we measure. Compare on your actual prompts.
Switching between them
For most use cases, switching providers means updating the model id and the request shape if the providers differ. Within the same provider, it's usually a single-line change.
From o4-mini to GPT-4 Turbo
# Before
model = "o4-mini"
# After
model = "gpt-4-turbo"If the providers differ (OpenAI vs OpenAI), you'll also need to swap the SDK / endpoint URL. Cross-provider migrations usually take 30 minutes to a few hours depending on how many features (streaming, function calling, tool use) you depend on.
Calculate cost on your own prompt
The examples above use generic input/output ratios. For an exact comparison, paste your real prompt into the calculator — it counts tokens with the right tokenizer for each model and shows side-by-side cost.
Open the calculator with both models →