OpenAI's o1 vs. GPT-4o: When to Use Each Model

OpenAI's latest release —the o1 models —mark a significant shift towards specialization. While existing and new models showcase impressive capabilities, they are designed for different tasks and come with distinct costs. This article will help you understand when to use each model, considering their performance and price.

OpenAI o1: The Reasoning Specialist

The o1 models, like o1-preview and o1-mini, are tailored for tasks requiring advanced reasoning, especially in STEM fields. Utilizing a chain-of-thought process, these models mimic human problem-solving strategies. This makes them excel in complex tasks, achieving:

• 89th percentile score on Codeforces
• 83% accuracy on the American Invitational Mathematics Examination (AIME)

In contrast, GPT-4o only achieved 13% accuracy on AIME, highlighting o1's edge in complex reasoning.

Costs and Performance

However, this specialized ability comes with trade-offs:

• Slower speed: o1 models can take up to 30 times longer for complex queries.
• Higher cost: Pricing is $15 per million input tokens and $60 per million output tokens.

o1-mini offers a more affordable option, being approximately 80% cheaper than o1-preview, but still about 20% more expensive than GPT-4o.

GPT-4o: The Familiar, Versatile Multitasker

GPT-4o, on the other hand, excels in general-purpose AI tasks. It offers:

• Greater speed and versatility
• Affordable pricing: $5 per million input tokens and $15 per million output tokens
• Multimodal capabilities: Handles text, images, and audio inputs

This makes GPT-4o ideal for a wide range of applications, from writing emails to analyzing spreadsheets.

Choosing the Right Tool

Deciding between o1 and GPT-4o depends on your specific needs:

• Complex reasoning tasks in STEM fields: Choose o1 for its unparalleled reasoning abilities, despite the higher cost and slower processing.
• General-purpose AI and daily tasks: Opt for GPT-4o for a cost-effective and faster solution.
• Multimodal applications: Use GPT-4o for tasks involving text, images, and audio.

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If you are offering a feature that is in the serving path (as opposed to a batch job) and is performed frequently, GPT-4o is the clear choice. However, if you need to produce rich, long-form output that considers a great deal of detail, and your users are willing to wait, o1 is the better option. Although o1 is slower than existing models, it can generate high-quality results.

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At Korl,  we use multiple models including Claude and Gemini when it makes sense.  We have also been really impressed by o1 in our early testing. It has been able to generate Product Requirements Docs (PRD) that are well thought out, informed by both what you and your industry is building. If you give us a couple of lines of what you want to build, we can do the rest! We think this feature is important enough to get right and people are willing to wait for the PRD to get produced. Even with o1 being much slower than existing models, Korl can produce PRDs about 100x faster than the fastest human with similar results in quality. Of course, like any initial PRDs, it requires a lot of collaboration with other humans - which we love. Because ultimately, that’s what we want to free up your time for - more time with each other!