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%.
o3: Further Improvements
o3 was released in 2025 as the successor to o1. On the ARC-AGI benchmark, a test of novel visual pattern reasoning, o3 scored 87.5% with high compute settings, compared to o1's 32% and GPT-4o's approximately 5%. This benchmark is significant because it was designed to be difficult to solve by memorization — it requires genuine reasoning about novel patterns.
On AIME 2025, o3 outperforms o1 by a meaningful margin. On software engineering benchmarks like SWE-Bench, o3 with agent scaffolding pushes above 70%, significantly ahead of standard models.
o3 is the most capable publicly available model for complex reasoning tasks as of May 2026.
Where Reasoning Models Win
Use o1 or o3 when your task has these characteristics:
Multi-step math and science problems. Any problem that requires chaining multiple reasoning steps, applying formulas correctly across a long derivation, or working through a proof. Homework-level math does not require a reasoning model. Competition math does.
Complex algorithm design. When you need to implement a non-trivial algorithm from scratch — a custom data structure, an optimization algorithm, a parser for a non-standard format — o1's ability to think through the approach before writing produces significantly better first drafts.
Debugging complex systems. When you have a bug that requires reasoning through a chain of causes and effects, and you cannot easily isolate it in a minimal example, a reasoning model can follow the logic further than a standard model.
Code review for correctness. o1 is better at catching logical errors in code — not syntax errors, but cases where the code is syntactically valid and will run but produces incorrect results in certain conditions.
Research and analysis. Long-form analysis tasks where you need the model to maintain consistent reasoning across a complex argument benefit from the internal thinking phase.
Where Standard Models Win
o1 and o3 are not the right choice for every task:
Simple tasks. Drafting an email, summarizing a document, translating text, answering a factual question — these do not benefit from extended reasoning. Using a reasoning model for simple tasks wastes time and money.
Creative writing. Creative work benefits from spontaneity and diversity of outputs. The careful, methodical approach of reasoning models is less suited to creative tasks than it is to analytical ones.
Conversational tasks. Back-and-forth conversation where response speed matters for naturalness. A 60-second wait for each response breaks conversational flow.
High-volume applications. If you are making thousands of API calls per hour, the cost difference becomes prohibitive.
Multimodal tasks. o1 has image understanding but is weaker than GPT-4o on complex visual reasoning tasks. For image-heavy workflows, GPT-4o remains the practical choice.
Pricing Comparison
| Model | Input | Output |
|---|---|---|
| GPT-4o | $2.50 / 1M tokens | $10 / 1M tokens |
| o1 | $15 / 1M tokens | $60 / 1M tokens |
o1 is 6x more expensive on input and 6x more expensive on output compared to GPT-4o. Additionally, the internal thinking tokens (which you do not see) are billed. A response that involves extensive internal reasoning may consume far more tokens than the visible output suggests.
At $60 per million output tokens, o1 is appropriate for high-value tasks where the quality improvement justifies the cost — a complex algorithm that would take a senior engineer hours to design, a math derivation you need to be correct, or a debugging session on a critical production issue. It is not appropriate as the default model for general application queries.
Practical Decision Framework
Start with GPT-4o as the default. Switch to o1 or o3 when you encounter problems where GPT-4o gives unsatisfactory results despite good prompting, specifically:
- Math problems with more than 3 steps
- Algorithm problems that GPT-4o solves incorrectly after multiple attempts
- Logical reasoning puzzles with complex constraint systems
- Debugging sessions where you have ruled out the obvious causes
Do not start with o1 for everything hoping it will perform better on simple tasks. The performance improvement on simple tasks is minimal and the cost and latency penalties are real.
Keep Reading
- GPT-4o vs Claude 3.5 Sonnet Comparison — comparing the top standard models
- LLM API Pricing Comparison 2026 — full cost breakdown across all models
- OpenAI API Guide — how to call o1 and o3 via the API
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