Model comparison
GPT-5.4 vs o1
The most significant observable difference is the token context size, with GPT-5.4 supporting 1,050,000 tokens compared to o1's 200,000 tokens.
OpenAI
GPT-5.4
OpenAI's flagship — broadest modality and ecosystem coverage.
OpenAI
o1
The first reasoning model — historically important, now superseded.
Specs
| Metric | GPT-5.4 | o1 |
|---|---|---|
| Context window | 1.1M tokens↑ | 200K tokens |
| Input $/1M tokens | $2.50↑ | $15.00 |
| Output $/1M tokens | $15.00↑ | $60.00 |
| Modalities | Text · Image · File | Text · Image · File |
| Open weights | No | No |
| Released | — | Dec 2024 |
Capability differences
| Capability | GPT-5.4 | o1 |
|---|---|---|
| Tool use | Yes | No |
| Vision | Yes | No |
| Prompt caching | Yes | No |
How they differ
Reasoning approach
GPT-5.4
GPT-5.4 utilizes its larger token context to process extensive documents and make connections across longer sequences.
o1
o1 operates within a smaller token context, which may require more frequent summarization or segmentation of inputs.
Coding
GPT-5.4
GPT-5.4 can analyze and generate code across larger and more complex codebases due to its expanded token capacity.
o1
o1 is better suited for shorter code segments within its token limits.
Context handling
GPT-5.4
GPT-5.4 excels at maintaining context across long-form interactions or significant datasets.
o1
o1's smaller token capacity necessitates more concise or segmented context handling.
Speed
GPT-5.4
GPT-5.4 may process longer contexts more slowly due to its larger token capacity.
o1
o1 provides faster responses for smaller-scale tasks within its token limits.
Cost profile
GPT-5.4
GPT-5.4 is more cost-effective at $2.5/1M input tokens and $15.0/1M output tokens.
o1
o1 is significantly more expensive, charging $15.0/1M input tokens and $60.0/1M output tokens.
GPT-5.4 — what sets it apart
- +GPT-5.4 supports a token context size over five times larger than o1.
- +GPT-5.4 offers significantly lower costs for both input and output tokens.
o1 — what sets it apart
- +o1 is faster for tasks within its 200,000 token context limit.
- +Its higher cost may reflect specialized fine-tuning or optimization for specific tasks.
The most consequential difference is the token context capacity, which shapes their respective suitability for extensive versus smaller-scale tasks.
Analysis synthesized from gpt-4o, llama-4-maverick, phi-4, etc.