In the AI landscape, change happens fast. Companies promise bigger, better, and smarter models with each new release. New models arrive, we nod, and move on. This time, though, OpenAI is signaling that something’s different. Their latest release, GPT-o1 (Sept 2024), arrives with the claim of significant advancements in artificial intelligence technology. But does it live up to the hype?
Let’s dive in and see if OpenAI’s new o1 model really delivers that “oh, wow” moment—or if it’s just another incremental update. We’ll break down what’s new in GPT-o1 vs. GPT-4o, assess the improvement, see who should be paying attention to new o1 models and GPT-4o, and wonder where the AI revolution might be heading next.
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What’s new in OpenAI o1 vs GPT-4? Understanding GPT 4o mini and 4o preview
With GPT o1, OpenAI is doubling down on the old advice: think before you speak. According to OpenAI, these new models (o1 mini and o1 preview) are designed to spend more time reasoning through complex tasks before they respond, making them better equipped to tackle tougher challenges in science, coding, and math. Instead of rushing to an answer, GPT-o1 takes its time, refining its strategies and learning from mistakes—much like a human would in a brainstorming session.
That said, OpenAI acknowledges that this early model doesn’t yet have all the features of GPT-4o, like web browsing or handling file uploads. For now, GPT 4o, as a general-purpose AI, may still be the better choice for everyday tasks.
So, what exactly has changed with the release of the new AI model, and what does it mean for users? Does it really represent significant advancements in AI technology? Let’s dive into a short evaluation and break down the key differences.
1. GPT-o1 deep reasoning capabilities evaluation
As already said, what sets OpenAI o1 apart from earlier models is its enhanced ability to reason through complex problems using a “chain of thought” approach. Much like how a person might pause and think carefully before answering a tough question, GPT-o1 takes its time to break down difficult tasks into simpler steps. Take a look at the example excerpt from a conversation with ChatGPT:
Graphic source: https://x.com/matthewberman/status/1834295485773054312
Through reinforcement learning, the model has been trained to refine its problem-solving strategies, recognize mistakes, and even switch approaches if the current one isn’t working. This iterative process allows OpenAI o1 to solve problems more efficiently and accurately than previous models, particularly in challenging areas like math, coding, and science. The o1 outperforms GPT-4o on the vast majority of complex reasoning tasks – both in human exams and ML benchmarks.
From a business perspective, these complex reasoning and problem-solving capabilities can be a significant milestone in AI development. When tackling complex tasks, such as analyzing financial trends or debugging a complicated codebase, GPT-o1 doesn’t just dive in with brute force. Instead, it processes each step carefully, evaluates the available information, and adjusts its approach if something doesn’t add up. Unlike previous models, which would sometimes follow a faulty line of reasoning all the way to the end, OpenAI o1 model, can spot when it’s veering off track and adjust its strategy mid-task, meaning fewer mistakes in outputs like forecasts or data-driven recommendations. This ability to reassess and course-correct is crucial in reducing errors – and making artificial intelligence utilization more cost-effective.
2. Cost of GPT-o1 vs. GPT-4o and GPT-4o-mini
Of course, these impressive reasoning capabilities come at a price—literally. When compared to GPT-4o model, the input costs of GPT-o1 are 6 times higher, while output tokens are a whopping 6 times more expensive. Even when stacked against the smaller GPT-4o-mini, GPT-o1 costs around 100 times more per input token and 100 times more per output token.
Model Input Tokens Cost (per 1M) | Output Tokens Cost (per 1M) | |
GPT-o1 | $15.00 | $60.00 |
GPT-4o | $2.50 | $10.00 |
GPT-4o-mini | $0.150 | $0.600 |
Now, let’s put that into perspective for a real-world scenario. Let’s take a 20-page (~50,000 tokens) document that we need to translate. (For a moment, let’s not consider whether this is the right application for each model.)
To translate this 20-page document, each model processes the entire text as input (50,000 tokens) and produces a translated version (50,000 tokens) as output. Of course, GPT-4o and GPT-4o-mini will require more calls due to the limits of the output tokens.
GPT-o1 (1 call) | GPT-4o (12 calls) | GPT-4o-mini (3 calls) | |
Input Tokens (50K) | $0.75 | $0.025 * 12 = $0.30 | $0.0075 * 3 = $0.02 |
Output Tokens (50K) | $3.00 | $0.10 * 12 = $1.20 | $0.03 * 3 = $0.09 |
Total Cost (20 pages) | $3.75 | $1.50 | $0.11 |
And while a $2 difference may not sound like a big deal, these cost differences can scale quickly for businesses handling high volumes of documents.
While GPT-o1’s reasoning capabilities might be worth the extra investment for complex tasks, this cost comparison illustrates the steep premium businesses must be prepared to pay for its advanced AI functionality. For straightforward tasks, GPT-4o-mini might be more than enough at a fraction of the cost.
3. How big is the GPT-o1 context window?
Beyond reasoning capabilities and cost, another critical distinction between GPT-o1, GPT-4o, and GPT-4o-mini lies in their maximum output token window—a key factor in how these models handle extended tasks. While all three share the same Max Input Token capacity of 128,000 tokens (around 51 A4 pages of text), their ability to generate output differs substantially.
- GPT-o1 can produce up to 65,536 output tokens—roughly 26 A4 pages—in a single call.
- GPT-4o-mini handles up to 16,384 tokens, or about 6.5 A4 pages.
- GPT-4o is limited to 4,096 tokens, or just 1.6 A4 pages.
These differences in output capacity directly affect how each model performs in real-world tasks. GPT o1 is designed for handling lengthy and complex tasks that require deep analysis and large context window, such as translating entire reports, generating in-depth analyses, or writing long-form content in one go. Its advanced reasoning capabilities, discussed earlier, allow it to tackle intricate problems more effectively, and the larger output window means that the o1 excels in providing detailed answers without breaking the task into multiple calls.
However, not every task demands such a large output capacity. For use cases like generating short summaries, answering straightforward customer queries, or producing brief reports, the smaller output windows of GPT-4o and GPT-4o-mini are more than sufficient. These models can handle these simpler tasks at a fraction of the cost without needing the more complex and expensive features that GPT-o1 offers. GPT-4o-mini, in particular, provides an economical option for businesses that regularly deal with shorter, more transactional tasks, making it a smart choice for day-to-day operations where extended output isn’t necessary.
4. How much slower is the OpenAI o1 model compared to GPT-4o?
While GPT-o1 boasts impressive reasoning capabilities and the ability to generate longer, more detailed responses, these superpowers come at a cost—not just in price, but also in speed. According to data from Vellum.ai, OpenAI o1-mini model is approximately 16 times slower than GPT-4o-mini and 30 times slower than GPT-4o!
This difference in speed becomes critical in time-sensitive scenarios. For example, in customer service environments, high-volume real-time document processing, or tasks involving frequent back-and-forth interactions – like chatbot conversations – openAI o1’s slower speed could lead to delays, making it less suitable for such applications. If your business relies on lightning-fast responses, you might not be willing to trade speed for brainpower.
However, in the applications where depth of analysis is more important than speed—such as legal research, scientific reporting, or strategic decision-making—the extra time GPT-o1 takes to generate its output seems to be a fair trade for what it brings to the table. After all, not every task is a race. In these scenarios, the model’s superior reasoning capabilities and ability to handle complex problems in one go are worth the extra wait, especially if the output requires a higher degree of accuracy or insight.
5. Capabilities of GPT o1 and GPT 4o
Despite its advanced reasoning capabilities and ability to tackle complex tasks, both o1 models (o1-preview and o1-mini) lack some user-friendly features that make GPT-4o such a versatile tool. Features like browsing the web for real-time information or uploading files and images—standard in GPT-4o—are not yet available in GPT-o1. This means that for tasks requiring up-to-date information or those involving document uploads or image analysis, GPT-o1 falls short of its predecessor.
Using GPT-4o is like having a digital assistant—one that can write your emails, crunch numbers in your spreadsheets, summarize meeting notes, and even mock up some creative designs. With its broad set of capabilities, GPT-4o is a go-to for handling the variety of tasks businesses need every day. It can jump between different types of tasks seamlessly, giving users more flexibility in handling various workflows without switching AI tools.
In contrast, GPT-o1 is more like a highly specialized professional—perfect for solving complex problems but not designed for simpler tasks. Its time is too valuable to waste on mundane work, and in many cases, it may not excel at it. However, when you need something that requires serious brainpower—whether it’s solving a difficult problem, generating detailed reports, or conducting deep analysis—GPT-o1 steps in as the expert that can’t be replaced. The challenge for businesses will be to recognize when its advanced capabilities are worth the trade-off.
Read also: GPT-4o vs Claude 3.5 Sonnet
Is GPT-o1 better than GPT-4o? Choose your fighter for AI development!
Deciding between OpenAI’s o1 vs older models like GPT-4o isn’t about which one is “better” – it’s about choosing the right tool for the job.
The new OpenAI model is designed for highly specialized, complex tasks. It shines when tackling complex, multi-step problems in fields that require deep reasoning and advanced analysis. Its unique reasoning abilities make it irreplaceable in situations where accuracy and depth matter more than speed.
GPT-o1 outperforms GPT-4o and other models in advanced coding tasks like building multi-step algorithms or debugging intricate workflows. It shines in scenarios requiring human PhD-level accuracy, such as generating complex mathematical equations or analyzing large scientific datasets.
On the other hand, for general-purpose artificial intelligence tasks, GPT-4o is the clear winner. It’s faster, cheaper, and more versatile for day-to-day business needs. From writing emails and analyzing data to summarizing reports, GPT-4o easily handles a wide range of common tasks. And when it comes to multimodal applications, GPT-4o’s ability to process text, images, and even audio makes it ideal for situations that require multiple input types, making it the more practical choice for many businesses.
To sum it up:
When OpenAI o1 mini excels | Where GPT-4o excels |
Complex problem-solving (e.g., advanced coding challenges, multi-step workflows) | General-purpose tasks (e.g., writing emails, summarizing reports) |
Scientific research (e.g., generating complicated formulas in physics, analyzing complex datasets) | Routine data analysis (e.g., basic number crunching, spreadsheet tasks) |
Advanced mathematical reasoning (e.g., creating complex algorithms or solving intricate equations) | Multimodal tasks (e.g., processing text, images, and audio inputs simultaneously) |
Healthcare research (e.g., annotating cell sequencing data, detailed bioinformatics) | Customer service applications (e.g., chatbot interactions, quick responses) |
Strategic decision-making (e.g., deep analysis and long-form reports) | Real-time information retrieval (e.g., browsing the web for up-to-date information) |
Long-form content generation (e.g., creating detailed technical or legal documents) | Creative tasks (e.g., designing mockups, generating creative content across mediums) |
Read also: GPT-3 Use Case Examples
Is “small” the new big thing in large language models?
For the early months of 2024, the AI community has been debating a classic dilemma: should we rely on massive, general-purpose models, or is the future in smaller, more focused ones? It’s the age-old battle of one-size-fits-all versus custom-built solutions.
In one corner, we have the giants—LLMs, large language models that can tackle a wide range of tasks with their broad applicability. In the other corner, we have the nimble contenders—small language models (SLMs) that may lack the versatility of their bigger counterparts but make up for it with speed, efficiency, and precision.
While some predicted that LLMs were nearing their performance limits and that smaller models would become the next big thing in AI, a new fighter has entered the ring and flipped the script by bridging the gap between large-scale AI capabilities with finely tuned reasoning. With the rise of the OpenAI’s o1 models, we’re moving from a one-size-fits-all approach to tailored solutions for specific problems – but on a large scale.
As the battle between large and small models rages on, GPT-o1 emerges as proof that the future of AI isn’t just about size—it’s about smart strategy. By combining the vast reach of LLMs with the precision of smaller models, the o1 represents a shift toward models that are both powerful and adaptable.
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