OpenAI's o1 and o3 models are fundamentally different from GPT-4o in how they generate responses. They spend time reasoning internally before answering — a process analogous to thinking through a problem step by step before writing. This produces dramatically better results on math, logic, and complex coding tasks, but at higher cost and slower response times. For simple tasks, GPT-4o is the better choice.
What Makes Reasoning Models Different
Standard models like GPT-4o generate output token by token in a single forward pass. The model produces the next token based on all previous tokens, generating its answer as it writes it. This works well for most tasks, but it struggles with problems that require extended planning, hypothesis testing, and error correction.
Reasoning models (o1, o3) use a different approach. Before producing a visible response, they run an internal chain-of-thought process. This "thinking" phase is not shown to you in the response, but it allows the model to explore multiple approaches, check its own work, and backtrack when it makes a mistake. The result is that the model produces a more carefully considered answer.
The trade-off is time and cost. A reasoning model might spend 30 seconds to 2 minutes on its internal thinking before producing an answer. GPT-4o would answer the same question in 2 to 5 seconds.
o1: Performance on Hard Problems
o1 was released in September 2024 and demonstrated a step-change in performance on math and coding benchmarks.
AIME 2024 (American Invitational Mathematics Examination): o1 scored 83.3%. GPT-4o scored 9.3%. This is not a small improvement — it is a qualitative leap. AIME problems require multi-step mathematical reasoning, and 9.3% reflects GPT-4o hitting the ceiling of what single-pass generation can do on hard math.
AMC 2023: o1 scored 96.7%. The AMC is the qualifying exam for AIME, slightly easier but still college-level competition math. GPT-4o's score on AMC is significantly lower.
Code generation: On competitive programming problems, o1 substantially outperforms GPT-4o. The reasoning process helps with algorithm selection, edge case identification, and implementation verification.
GPQA Diamond (graduate-level science questions): o1 scores around 78%, surpassing expert human performance on some categories. GPT-4o scores around 53%.
