OpenAI o1: Chain-of-Thought Reasoning and What It Changes
OpenAI o1: Chain-of-Thought Reasoning Explained
In September 2024, OpenAI released o1 — a model designed not just to generate text but to reason through problems before answering. It represents a meaningful shift in how large language models handle difficult reasoning tasks, particularly in mathematics, coding, and science.
What Makes o1 Different
Previous language models like GPT-4 were trained primarily to predict the next token as accurately as possible given their context. They are remarkably capable, but they tend to answer "immediately" — without the kind of intermediate reasoning that humans use when tackling hard problems.
o1 changes this with extended chain-of-thought (CoT) reasoning. Before producing an answer, the model generates a long internal reasoning trace — thinking through the problem step by step, exploring approaches, catching mistakes, and backtracking when needed. This internal trace is not shown to users in the standard interface (you see only the final answer), but it runs for potentially hundreds or thousands of tokens before the response appears.
This approach mirrors what researchers call "slow thinking" — the deliberate, analytical mode of cognition as opposed to fast, intuitive response.
Benchmark Results
o1's reasoning capability is best illustrated by benchmark comparisons:
| Benchmark | GPT-4o | o1 | Human expert |
|---|---|---|---|
| AIME 2024 (math olympiad) | 13% | 83% | ~75% |
| Codeforces competitive programming | 11th percentile | 89th percentile | — |
| GPQA Diamond (science PhD-level) | 53% | 78% | 69% |
| MATH (competition math) | 76% | 94% | ~90% |
The AIME and GPQA results are particularly striking — o1 approaches or exceeds domain expert performance on tests that require multi-step reasoning rather than recall.
How Chain-of-Thought Reasoning Works
The key insight behind o1 is that more test-time compute — spending more computation at inference time rather than only at training time — can improve answer quality for hard problems.
Standard model (GPT-4o style):
Prompt → Immediate generation of answer
o1 style:
Prompt → [Extended internal reasoning: try approach A →
realize flaw → backtrack → try approach B →
verify step by step] → Final answer
This extended reasoning is trained using reinforcement learning from outcomes: the model is rewarded for correct final answers, which teaches it to generate reasoning traces that are actually useful rather than just plausible-sounding.
o1 vs GPT-4o: When to Use Each
o1 is not uniformly better — it is slower and more expensive. The right choice depends on the task:
| Task type | Better model | Why |
|---|---|---|
| Multi-step math proofs | o1 | Chain-of-thought dramatically helps |
| Complex algorithm design | o1 | Needs to reason about correctness |
| PhD-level science questions | o1 | Requires careful analytical reasoning |
| Writing and summarization | GPT-4o | Creative generation, o1 not faster here |
| Simple coding tasks | GPT-4o | Overkill for straightforward code |
| Customer support chatbot | GPT-4o | Speed matters; reasoning less valuable |
| Code debugging (complex) | o1 | Reasoning through multi-file logic |
As a rule of thumb: if a task requires multiple steps where an intermediate mistake would cause the whole answer to fail, o1's reasoning is valuable. For tasks where speed, cost, and fluency matter more, GPT-4o is typically the better choice.
Practical Implications for Developers
API Usage
o1 is available via the OpenAI API. At launch it was significantly more expensive than GPT-4o — typically 3–5× the cost per token. OpenAI released o1-mini as a cheaper variant that retains most of the reasoning capability for math and coding tasks.
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="o1-preview", # or "o1-mini" for cheaper option
messages=[
{
"role": "user",
"content": "Prove that the sum of the first n natural numbers equals n(n+1)/2 using mathematical induction."
}
]
)
print(response.choices[0].message.content)
Note: At launch, o1 did not support system messages or tools/function calling — these were added in later versions. Always check the current API docs for available parameters.
Prompt Engineering Differences
Because o1 does its own reasoning internally, you do not need to explicitly instruct it to "think step by step" — that is already built into how it operates. In fact, prompting o1 to explain its reasoning can be counterproductive since it adds to the visible output without changing the internal process.
For o1, prompts should:
- State the problem clearly and precisely
- Provide relevant constraints and requirements
- Avoid explicitly asking for "step by step" explanations (it does this internally)
- Focus on the desired output format
Latency Considerations
o1 generates a long internal reasoning trace before producing visible output. Latency for first token can be 10–30 seconds for complex problems, compared to under 1 second for GPT-4o. This makes o1 inappropriate for real-time user-facing applications but well-suited for:
- Background processing pipelines
- Offline analysis tasks
- Agentic systems where accuracy matters more than speed
Limitations
- Not more knowledgeable: o1 has the same training knowledge cutoff as GPT-4o. It reasons better with what it knows, but cannot access information it was not trained on.
- Still makes errors: Even with chain-of-thought reasoning, o1 makes mistakes on very hard math and can hallucinate. Verify outputs on anything critical.
- Slower and more expensive: Not suitable for high-volume or latency-sensitive applications.
- No multimodal reasoning benefit: The reasoning improvement is primarily in text-based logic; image understanding is handled separately.
The Broader Trend: Scaling Inference
o1 is significant not just as a product but as a signal: OpenAI (and the research community) have demonstrated that scaling test-time compute is a viable path to improving model capability. This is distinct from — and potentially more efficient than — scaling training compute. Future model generations are likely to push this further, with models that spend variable amounts of reasoning effort depending on how difficult the problem is.