The First AI That Thinks Before It Speaks.
On September 12, 2024, OpenAI released o1-preview and o1-mini, and something genuinely new entered the AI landscape. Not a bigger model. Not a faster model. A model that reasons.
Every model before o1 worked essentially the same way: receive input, generate output, one token at a time, left to right. The models were impressive pattern matchers, but they did not plan. They did not decompose problems into steps. They did not check their own work.
o1 changed that. It spends time "thinking" before responding -- breaking complex problems into steps, considering multiple approaches, verifying its reasoning. The output takes longer to arrive, but when it does, the quality on certain task types is in a different league entirely.
We have been testing o1-preview and o1-mini on real business tasks since launch day. The results are a split decision: dramatically better for some things, dramatically worse for others. Here is the honest breakdown.
o1 is not a better chatbot. It is a different category of tool. Using it for everyday writing is like hiring a PhD mathematician to answer your email -- technically capable, absurdly inefficient.
Where o1 Is Genuinely Transformative
Mathematics and Quantitative Analysis
The benchmark numbers are staggering. On the American Invitational Mathematics Examination, o1 solved 83 percent of problems compared to GPT-4o's 13 percent. That is not an incremental improvement. That is a category change.
In practical terms, this means o1 can handle the kind of multi-step quantitative analysis that previously required specialized tools or human experts. We tested it on a client's financial modeling task -- projecting revenue scenarios across multiple variables with interdependent assumptions. GPT-4o produced plausible-looking but mathematically inconsistent projections. o1 produced projections that our client's CFO verified as sound.
Science and Technical Reasoning
On the GPQA Diamond benchmark -- a test of graduate-level science questions -- o1 became the first model to surpass PhD-level human expert performance. For businesses in technical domains -- pharma, biotech, engineering, advanced manufacturing -- this opens up use cases that were simply not viable before. Analyzing research papers, evaluating experimental methodologies, cross-referencing technical specifications across complex systems.
Complex Multi-Step Logic
This is where we see the most practical business value. Tasks that require chaining multiple logical steps -- "analyze this data set, identify the three most significant anomalies, determine whether each anomaly is explained by known seasonal patterns, and for unexplained anomalies, propose three testable hypotheses" -- are dramatically better with o1. The model plans its approach before executing, which means fewer logical dead ends and more coherent multi-step outputs.
We explored this kind of structured reasoning in our piece on Claude vs ChatGPT for business, where we noted that instruction following was a key differentiator. o1 adds another dimension to that comparison: not just following instructions, but reasoning through them.
Where o1 Falls Short
Speed
o1 is slow. Not slightly slow -- dramatically slow. A response that GPT-4o generates in 3 seconds might take o1 30 to 60 seconds. The "thinking" process that makes it good at reasoning also makes it unusable for any task where speed matters. Customer-facing chatbots, real-time data processing, interactive applications -- o1 is the wrong tool.
Cost
The reasoning tokens are not free. o1 uses significantly more compute per query, and OpenAI prices accordingly. For high-volume tasks -- processing thousands of support tickets, analyzing hundreds of documents -- the cost difference between o1 and GPT-4o makes o1 impractical unless the quality improvement is genuinely necessary.
Everyday Business Tasks
Drafting emails, summarizing documents, writing marketing copy, generating social media posts -- o1 is overkill for all of these. It does not write better emails than GPT-4o or Claude. Its summaries are not meaningfully better. And the extra latency makes the user experience worse for interactive, conversational work.
This is the mistake we see businesses making: assuming that the "smarter" model is better for everything. It is not. As we discussed in our piece on what AI agents actually are, the right tool depends on the task. o1 is a specialist, not a generalist.
When to Use o1 vs GPT-4o vs Claude
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START: What is the task?
|
|--- Complex math, logic, or science?
| |
| YES ---> Use o1
| (Accept slower speed for better reasoning)
|
|--- Multi-step analysis with chained logic?
| |
| YES ---> Is speed critical?
| |
| YES ---> Use Claude (fast + strong reasoning)
| NO ---> Use o1 (deepest reasoning available)
|
|--- Business writing, emails, summaries?
| |
| YES ---> Use Claude or GPT-4o
| (o1 is overkill, slower, more expensive)
|
|--- Coding or software development?
| |
| YES ---> Use Claude (Claude Code is unmatched)
| o1 is strong but lacks agent tooling
|
|--- Customer-facing, real-time interaction?
| |
| YES ---> Use GPT-4o or Claude
| (Speed matters more than depth)
|
|--- Document analysis at scale?
|
YES ---> Use Claude for consistency
Use Gemini 1.5 Pro for massive docs
Use o1 only for high-stakes analysis
The Reasoning Paradigm Is the Real Story
Here is what matters more than o1 itself: the paradigm it introduces. The idea that AI models can be trained to reason -- to think before responding, to plan multi-step approaches, to verify their own work -- is the most important development in AI since the transformer architecture.
o1 is the first generation. It is slow, expensive, and narrowly better. But reasoning models will get faster, cheaper, and more broadly capable. The full o1 model released in December 2024 already showed significant improvements over the September preview -- 34 percent fewer errors. That trajectory matters more than any single benchmark number.
Anthropic is building reasoning capabilities into Claude. Google is doing the same with Gemini. Within a year, reasoning will not be a feature of one model family -- it will be table stakes for all frontier models. The question for businesses is not whether reasoning AI matters, but how to prepare workflows for models that can genuinely think.
Reasoning models are the most important development in AI since the transformer. o1 is the first generation -- slow, expensive, narrowly better. But the trajectory is what matters. Within a year, every frontier model will think before it speaks.
Our Practical Recommendation
For most businesses reading this blog, o1 is not your primary tool. Claude remains our recommendation for the vast majority of business AI work -- it is faster, more consistent, better at following complex instructions, and the Claude Code ecosystem is unmatched for development work. We laid out the full reasoning in our comparison of AI automation approaches.
But o1 has earned a permanent place in our toolkit for specific tasks. When a client needs complex financial modeling, when we are debugging a particularly thorny logical issue, when a task requires genuine multi-step reasoning that benefits from slower, deeper thinking -- o1 is the tool we reach for.
The mistake is treating o1 as a ChatGPT replacement. It is not. It is a specialist tool for specialist tasks. Use it like a consultant: bring it in for the hard problems, and let your everyday tools handle the everyday work.
OpenAI built something genuinely new with o1. Credit where it is due. The reasoning paradigm is real, it works, and it will reshape how AI is used for complex analysis. The execution needs refinement -- faster, cheaper, more accessible -- but the direction is right. We will be watching the next generation closely.