Artificial intelligence has come a long way in recent years, and the capabilities of available large language models (LLMs) are nothing short of impressive. We’ve seen AI generate human-like text, write functional code, and analyze complex documents. But there’s always been something missing, something crucial to making AI feel truly intelligent: reasoning.
While large language models have excelled at producing fluent responses, they’ve struggled when it comes to tasks that require deeper, more human-like thought. Think of problems that involve multiple steps, logical deductions, or abstract reasoning. Until recently, AI has fallen short of mastering these.
But what if an AI could not only generate text but also "think" its way through a problem before responding? Enter OpenAI’s o1 series—models designed to fill that critical gap. The o1 series brings us closer to this vision by introducing a new layer of reasoning, a breakthrough that might just solve AI's biggest puzzle piece yet.
In this article, we’ll explore how the o1 models are changing the game, diving deep into why reasoning matters and when you need it most in your projects. Buckle up, because the future of AI is about to get a whole lot smarter!
Tasks where LLMs fall short (and why)
Large language models like GPT-4o, Claude 3.5 Sonnet, and Gemini are pretty good at things like generating coherent text, analyzing complex documents, and even writing simple, functional code. However, when it comes to tasks that demand deep, human-like reasoning, they often fall short.
For a long time, LLMs have struggled with a few key areas: calculations, multi-step problems, and visual-spacing understanding. Let’s take a look at a few examples.
Calculations
AI models view math as a language. While at times they can find accurate solutions to some mathematical problems, they don’t truly grasp the mathematical concepts behind those calculations.
Example:
Which is larger: 9.11 or 9.8?
What typically happens: LLM responds that 9.11 > 9.8
Why does it happen?
LLMs break down text into smaller units called tokens. For instance, 9.11 might be split into tokens like ['9', '.', '11'] or even ['9', '.', '1', '1'], while 9.8 could become ['9', '.', '8']. Once tokenized, the model loses the inherent numerical value of these numbers. They're treated more like strings of characters rather than quantitative values.
Multi-step problems
LLMs aren’t naturally good at breaking down complex problems into smaller, manageable steps. Whether it’s a tricky logic puzzle, a multi-part math problem, or even an ethical dilemma, they often struggle to find their way through it.
Examples are complex scheduling challenges, multi-step math problems, or moral quandaries.
Visual-spacing understanding
LLMs struggle to explain or execute tasks that require a coherent mental model of the physical world. Tasks that require understanding the spatial relationships between objects—things we humans can easily visualize in our minds—are where they fail.
Example:
If you put a strawberry in a cup, overturn the cup, and put the cup in the fridge, where is the strawberry?
What typically happens: LLM responds that strawberry is in the cup.
Why? LLMs are unlikely to reason about the physical forces at play, like gravity, or the fact that turning the cup over would cause the strawberry to fall out. They don't have a built-in mental model of objects in 3D space, so they focus on the actions described but miss the spatial consequences a human would automatically deduce.
Key areas impacted by limitations of LLMs
These challenges naturally led to lower performance in certain key areas: coding, math, and science questions.
Coding
Writing code isn’t just about putting together some syntax; it requires breaking down high-level tasks into clear, logical steps, grasping abstract programming concepts, and mentally visualizing how data structures and algorithms work together. Without solid multi-step reasoning, LLMs often hit a wall when trying to write anything beyond basic scripts.
Math
Mathematics is all about understanding numerical relationships, abstract ideas, and methodically solving problems step-by-step. Early LLMs simply didn’t have the numerical reasoning skills needed to reliably solve even basic math problems, let alone tackle more advanced concepts.
Science
Scientific reasoning is a complex process that involves creating hypotheses, designing experiments, analyzing data, and drawing conclusions based on evidence. This type of systematic, logical reasoning—and the quantitative thinking behind it—was something early models just couldn't handle well.
However, OpenAI’s latest model release marks a huge step forward. With more human-like reasoning capabilities, these LLMs are now better equipped to handle tasks in coding, math, and science—domains where logical reasoning is key. So, what is known about the newest GPT-o1?
The new era of LLMs: GPT-o1 model explained
OpenAI's recent release of the o1 series feels like a true breakthrough in AI, especially when it comes to handling complex reasoning. Models like GPT o1-preview and o1-mini introduce a revolutionary feature—“thinking.” They utilize a more advanced Chain of Thought (CoT) process that brings them closer to human-like reasoning than any previous models.
Before, LLMs mainly relied on pattern recognition to generate responses, but the o1 models are designed to spend more time thoughtfully working through problems before providing an answer—much like how a human would approach a challenging task.
This new approach directly addresses the limitations of earlier LLMs, particularly when it comes to multi-step problem-solving and in-depth analytical thinking.
Think of these models as the difference between a student who memorizes facts and one who genuinely understands the concepts behind them. The o1 series isn’t just about generating text—it’s about thinking through the problem and providing a reasoned solution.
OpenAI claims their new model series is vastly superior to GPT-4o in science, technology, engineering, and mathematics (STEM) and other reasoning-heavy tasks. Indeed, GPT-o1 benchmarks look rather impressive:
How GPT-o1 works under the hood
To address these reasoning gaps, developers have introduced advanced prompting techniques like Chain of Thought (CoT), Skeleton of Thought, and Tree of Thought. These methods are designed to guide LLMs in breaking down complex tasks into smaller, more manageable steps.
While these techniques have proven effective in boosting performance on certain tasks, they’re often seen as temporary solutions, or “crutches.” Yes, they help LLMs handle multi-step reasoning and produce more coherent outputs, but they don’t tackle the core issue—the model’s fundamental limitations. These methods rely on carefully crafted prompts and don't actually enhance the LLM's innate reasoning abilities.
At the core of OpenAI's GPT-o1 model lies a sophisticated approach to artificial reasoning: the Chain of Thought (CoT).
When presented with a problem, o1 doesn't immediately jump to an answer. Instead, it takes some time time to “think” and engages in a multi-step thinking process:
- Breaking down the problem into smaller, manageable components.
- Considering various approaches to solving each component.
- Testing different solutions, evaluating their effectiveness.
- Identifying when a chosen approach isn't working and moving to a new strategy.
The real magic happens when CoT is paired with reinforcement learning (RL). This combination creates a dynamic problem-solving engine. Here’s how reinforcement learning kicks things up a notch:
- o1 explores different problem-solving strategies.
- The model receives feedback based on the success or failure of the problem-solving attempt.
- o1 learns which reasoning strategies are most effective for different types of problems. It adjusts its approach based on what has worked well in the past.
- After exhaustive training, GPT-o1 model doesn't just memorize solutions to specific problems. Instead, it learns general principles of effective reasoning that it can apply to new, unseen problems.
- As o1 encounters more problems and receives more feedback, it continuously refines its reasoning process, becoming more efficient and effective over time.
A guide to effectively using GPT-o1
Since GPT-o1 incorporates Chain of Thought prompting internally, the way we interact with these "reasoning" models has fundamentally changed. Gone are the days of crafting intricate, multi-step prompts to coax the best results out of a model. Now, interaction is more straightforward, and o1 can handle much of the heavy lifting itself.
To help you get the most out of GPT-o1, OpenAI has provided some key prompting recommendations:
- Keep prompts simple. These models are trained to work best if you just write simple, straightforward prompts. They won’t need extensive guidance because they can find the most optimal path themselves.
- Avoid using CoT. Because the chain of thought technique is part of the model’s reasoning already, using your own reasoning in the prompts won’t work and might hinder performance.
- Use delimiters for quality. This technique applies to all previous models as well as this one. To clearly indicate parts of your prompt, use delimiters like “###”, XML tags, or section titles.
- Limit additional RAG. If you want to add more context to your prompt via retrieval-augmented generation (RAG), make sure you only include the most relevant information. Providing a lot of information at inference time might make the model “overthink” and take more time to get to the answer.
By following these best practices, you can streamline your interaction with GPT-o1, letting its advanced reasoning abilities take center stage.
GPT-o1 limits: What it can't do
All the reasoning in o1 comes with a cost. While it excels in logic and multi-step problem solving, there are areas where it falls short, and these limitations are important to keep in mind.
- Slower performance. o1 takes its time: it can be up to 30 times slower than GPT-4o, making it less suitable for tasks where speed is crucial, for example, real-time customer support or rapid content generation.
- Higher cost. GPT-o1 is more expensive, costing $15 per million input tokens and $60 per million output tokens, which adds up quickly if you’re running large-scale operations.
- Hidden actual CoT reasoning. The chain of thought reasoning happens behind the scenes, so there’s no clear way to estimate how long an answer will take or trace the steps the model used to get there.
- Not for every task. Human evaluators have noted that GPT-o1 isn’t the top pick for everything. Tasks like creative writing, where fluidity and style matter more than logic, may be better suited to other models.
So, while GPT-o1 is a powerhouse in many ways, understanding these limitations helps you know when it’s the right tool for the job—and when it’s better to look elsewhere.
GPT-o1 or GPT-4o: Which is better?
GPT 4o still shines in a wide variety of tasks. While o1 is busy solving complex analytical problems, 4o can handle everything else.
So, how do you decide which model to use? Here’s a simple guide:
- For complex reasoning tasks (coding, scientific research, or anything requiring step-by-step problem-solving) – GPT-o1 is the better choice. Yes, it’s slower and more expensive, but the deeper reasoning capabilities make it worth it for those situations.
- For general-purpose tasks – GPT-4o is faster, cheaper, and more versatile. It’s ideal for day-to-day tasks where speed matters more than in-depth problem-solving.
The development of o1 isn’t just a release of a bigger model with more parameters. OpenAI has made it clear that o1 is not GPT-5. By resetting the numbering to 1, they’re signaling the start of a new era in AI, where models are designed for specialized reasoning rather than just scaling up.
The AI landscape is evolving. Instead of one-size-fits-all models, we’re moving toward specialized tools that are built for specific tasks. The real challenge ahead will be learning to combine the strength of models like o1, which excels in reasoning, with the versatility of models like 4o.
For example: You can use o1 for planning and complex decision-making, then switch to GPT-4o for faster execution and more general tasks. By using both, you get the best of both worlds.
Combining GPT-o1 with other tools for better results
While GPT-o1’s reasoning abilities are impressive on their own, the real power comes when you combine it with other AI tools. By integrating o1 with specialized models, you can create a more versatile and capable AI system that goes beyond what a single model can do.
Here are a few ways you can get optimal results by combining GPT-o1 with other models:
Multimodality
When you pair o1 with models like computer vision (CV) or speech recognition, you create an AI system that understands the world in a more human-like way. By combining these tools, the AI can analyze visual and auditory data while applying o1’s reasoning for more accurate decision-making.
Specialized models
o1 can also enhance performance when integrated with domain-specific AI, like models for medical diagnosis or scientific research. In these cases, o1 handles the reasoning while the specialized models focus on their niche tasks, resulting in deeper insights and more precise outcomes.
AI ecosystems
Picture a “team” of AI models, each with its own area of expertise, all managed by GPT-o1’s advanced reasoning and decision-making skills. This ecosystem approach lets you leverage the strengths of multiple models, with o1 guiding the overall strategy to deliver smarter, more holistic results.
By combining GPT-o1 with other tools, you create an AI system that’s greater than the sum of its parts—smarter, more adaptive, and capable of tackling complex, multi-faceted problems.
Applying GPT-o1's reasoning to your business
o1 is not a general-purpose tool that gives quick conversational answers like a chatbot. Instead, it excels in scenarios that require deep reasoning or complex problem-solving. o1 made a huge jump in like coding, math and science. But what about your business? Here are some key areas where GPT-o1 model can add real value:
Experts facing complex challenges
o1 shines when tackling specialized problems that even a human expert might find time-consuming. Imagine a situation in scientific research or advanced mathematics where nuanced, thought-through responses are needed. o1 can methodically work through the problem, offering solutions that are well-considered and logical.
Long-term projects with complex requirements
If you're managing a project that requires a deep dive into data analysis, hypothesis generation, or creative strategy development, o1 might be the model to use. It allows for exploration of multiple angles and provides detailed reasoning that can be invaluable for long-term planning.
High-stakes business decisions
When faced with critical business choices involving numerous variables and potential outcomes, o1 can be a game-changer. o1 can analyze complex business landscapes, considering multiple factors simultaneously. It provides comprehensive risk assessments, identifies hidden pitfalls, and suggests mitigation strategies, all backed by detailed rationales that can inform and support your decision-making process.
Software development conundrums
In the world of coding, some problems can stump even the most experienced developers. o1 can methodically analyze the entire system, identifying subtle inefficiencies and suggesting optimizations that might escape human notice. For those particularly stubborn bugs in complex systems, o1 can trace through logic, considering myriad interactions to pinpoint root causes that have evaded traditional debugging methods.
Final thoughts
OpenAI’s o1 series is a game-changer for projects that need complex reasoning and deep problem-solving. But not every task requires that level of sophistication. For many use cases, tried-and-true methods like retrieval-augmented generation (RAG), advanced prompt engineering, and domain-specific fine-tuning are still more than enough. These models continue to shine when it comes to generating content, managing customer interactions, and handling general-purpose tasks efficiently.
The key is knowing when to use the right tool. If your business is facing challenges that go beyond the basics and you need AI that can really think things through, o1 could be the solution you're looking for. But if speed and versatility are your priorities, other LLMs still hold incredible value.
At Tensorway, we know the deal. Whether you're curious about integrating o1 or just want to get more out of your current AI setup, we're here to help. Reach out to us, and let’s figure out the best approach for your business.
