OpenAI introduced o1-preview and o1-mini on September 12, 2024 as a new model family with a philosophical difference from GPT-4o: they reason internally before emitting the final answer. It’s not just visible chain-of-thought; it’s a hidden process where the model elaborates, reconsiders, explores paths, and only then responds. Results in mathematics and code are dramatic. For other tasks, the latency and cost trade-off isn’t always worth it.
What Makes It Different
Traditional models (GPT-4o, Claude 3.5 Sonnet) generate response tokens sequentially from the first token. o1 introduces a prior user-invisible “thinking” phase where the model can reformulate the problem, explore approaches, discard strategies, before elaborating the answer. This internal reasoning consumes additional tokens — so-called reasoning tokens — which are billed but not shown to the user.
The approach is partially inspired by techniques like Tree of Thoughts and MCTS but integrated at training level, not prompt. OpenAI doesn’t detail exact architecture; we know there’s a reinforcement-learning phase teaching the model to reason efficiently on complex problems.
Where It Excels
Benchmarks tell a clear story. On maths problems like AIME (American Invitational Mathematics Examination), o1-preview scores 83% vs GPT-4o’s 13%. On PhD-level physics problems, o1 reaches 78% vs 57% of human experts. In programming competitions like Codeforces, o1-preview sits at 89th percentile vs GPT-4o’s 11th.
For tasks requiring long chains of reasoning — olympiad maths, proofs, complex debugging, deep causal analysis — the qualitative leap is real and not just statistical. Users report o1 solves problems where GPT-4o went in circles.
Where It Doesn’t Add Much
For conversational tasks, creative writing, simple summaries, direct factual questions, o1 offers no significant advantage over GPT-4o, while adding latency and cost. A response GPT-4o generates in one second may take ten to twenty in o1 while it “thinks”. For fluid conversation, that breaks the experience.
Additionally, o1 has architectural limitations. It doesn’t (at least in preview) support function calling the same way as GPT-4o, doesn’t stream, isn’t multimodal. For workflows depending on those capabilities, it’s not a direct replacement.
The Cost Factor
o1-preview costs $15 per million input tokens and $60 per million output. o1-mini is cheaper — $3 input, $12 output — and is typically the sweet spot for most uses benefiting from reasoning. For comparison, GPT-4o costs $2.50 and $10 respectively.
Real cost is higher than nominal because hidden reasoning tokens consume output billing. An apparently short response may have internally consumed ten times the visible tokens. For high-volume applications, this adds up.
When to Use and When Not
The pragmatic rule emerging after weeks of use: for problems where the answer requires several chained reasoning steps and where correctness matters more than speed, o1 is worth it. Complex technical research, legal analysis with multiple premises, maths or programming problems with rich structure, strategic planning with interdependent variables.
For chatbots, content generation, summaries, translations, quick questions, function calling, o1 introduces cost and latency without proportional benefit. GPT-4o or Claude 3.5 Sonnet are better choices.
The Industry Effect
o1 marked a conceptual shift. Previously, progress came primarily from scaling parameters and data (GPT-3 → GPT-4). o1 shows scaling inference-time compute — giving the model more tokens to think — also produces qualitative leaps. This opens a new scaling dimension.
Shortly after, Anthropic announced similar capabilities in later Claude versions. Google prepares its response in the Gemini family. Reasoning-model competition is already active battleground. For 2025 we expect multiple options with similar paradigms.
Limitations and Criticism
Being honest about problems. Internal reasoning isn’t transparent — OpenAI explicitly hides reasoning tokens from users. This generates legitimate concerns about auditing and debugging. How to debug an error when you can’t see the reasoning that produced it?
Published benchmarks are somewhat cherry-picked. o1 isn’t universally superior; on many everyday tasks it ties or loses to GPT-4o when normalised for cost. The “o1 is better at everything” narrative is incorrect.
There are also open questions about the approach’s sustainability. If each model generation requires ten times more inferential tokens, carbon footprint and economic cost scale exponentially. At some point this growth stabilises or gets rethought.
Practical Integration
For teams wanting to incorporate o1 into their stack, the pragmatic approach is multi-model routing: use GPT-4o or Claude for most queries and escalate to o1 only when the task justifies. Tools like LiteLLM facilitate this pattern with a unified proxy.
Another useful pattern is “reviewer” mode: o1 reviews responses produced by cheaper models and flags reasoning errors. Per-review cost is low because responses are already structured; quality benefit can be high for critical tasks.
Conclusion
o1 represents an inflection point in how we think about language models. It’s not a universal GPT-4o replacement but a specialised complement for deep reasoning. For problems where correctness matters more than speed, it’s worth every extra cent. For most everyday uses, traditional models remain more efficient. The direction it marks — scaling inference compute for reasoning — is probably the next dominant paradigm. Knowing when to apply it forms part of essential technical repertoire for any engineer integrating LLMs in production.
Follow us on jacar.es for more on reasoning models, frontier LLMs, and multi-model strategies.
