Model comparison
GPT-5.4 vs GPT-5.4 Mini
The most significant observable difference between GPT-5.4 and GPT-5.4 Mini is their context handling capacity, with GPT-5.4 supporting 1,050,000 tokens compared to GPT-5.4 Mini's 400,000 tokens.
OpenAI
GPT-5.4
OpenAI's flagship — broadest modality and ecosystem coverage.
OpenAI
GPT-5.4 Mini
GPT-5 economics for high-volume routine tasks.
Specs
| Metric | GPT-5.4 | GPT-5.4 Mini |
|---|---|---|
| Context window | 1.1M tokens↑ | 400K tokens |
| Input $/1M tokens | $2.50 | $0.750↑ |
| Output $/1M tokens | $15.00 | $4.50↑ |
| Modalities | Text · Image · File | File · Image · Text |
| Open weights | No | No |
Capability differences
| Capability | GPT-5.4 | GPT-5.4 Mini |
|---|---|---|
| Extended thinking | Yes | No |
How they differ
Context handling
GPT-5.4
GPT-5.4 supports up to 1,050,000 tokens, enabling it to handle longer documents and extended interactions.
GPT-5.4 Mini
GPT-5.4 Mini supports up to 400,000 tokens, making it suitable for shorter contexts.
Cost profile
GPT-5.4
GPT-5.4 is more expensive, costing $2.5 per 1M input tokens and $15.0 per 1M output tokens.
GPT-5.4 Mini
GPT-5.4 Mini is more cost-efficient, costing $0.75 per 1M input tokens and $4.5 per 1M output tokens.
Reasoning approach
GPT-5.4
GPT-5.4 uses its large context capacity to synthesize deeper insights across complex tasks.
GPT-5.4 Mini
GPT-5.4 Mini is optimized for faster reasoning but sacrifices depth due to its smaller context size.
Speed
GPT-5.4
GPT-5.4's processing may involve more overhead due to its larger context window.
GPT-5.4 Mini
GPT-5.4 Mini is faster, particularly for smaller tasks, due to its streamlined design.
GPT-5.4 — what sets it apart
- +GPT-5.4 is suited for tasks that require extensive document analysis or long-term conversational memory.
- +Higher costs align with its ability to handle complex and large-scale processing.
GPT-5.4 Mini — what sets it apart
- +GPT-5.4 Mini provides a cost-effective solution for frequent or lightweight tasks.
- +Its design prioritizes speed over extensive context coverage.
The primary difference lies in the context handling capacity (1,050,000 vs. 400,000 tokens), influencing suitability for large-scale versus cost-sensitive applications.
Analysis synthesized from gpt-4o, llama-4-maverick, phi-4, etc.