How Accurate Is ChatGPT o1? The Reasoning Model Reviewed

AI Summary: OpenAI's o1 model represents a breakthrough in reasoning accuracy, achieving 83% on AIME compared to GPT-4o's 13% by using extended chain-of-thought reasoning before producing answers. This makes o1 dramatically more accurate than GPT-4o for hard math, science, and logic problems. The tradeoff is significantly slower response time and higher cost, making o1 most appropriate for the hardest problems where accuracy is paramount. Summary created using 99helpers AI Web Summarizer

The release of OpenAI's o1 model in September 2024 marked a genuine paradigm shift in how AI achieves accuracy. Rather than simply scaling model size or training data, o1 uses an extended internal reasoning process — often described as "thinking before answering" — to dramatically improve accuracy on problems that require multiple logical steps. For users asking how accurate is ChatGPT o1, the answer is: substantially more accurate than any previous OpenAI model on the hardest reasoning tasks.

The Chain-of-Thought Reasoning Breakthrough

o1's core innovation is its approach to reasoning. Before producing an answer, o1 generates an extended internal chain of thought — working through the problem, considering alternative approaches, checking its reasoning, and iterating before committing to a final answer. This process is not shown to the user directly (unlike some chain-of-thought prompting techniques) but produces substantially more accurate outputs on problems that benefit from deliberate multi-step reasoning.

The result is a model that "thinks slowly" in the tradition of System 2 thinking — deliberate, effortful reasoning — rather than the fast pattern-matching that characterizes standard language model responses. For problems that have a clear logical structure but require many sequential steps to solve, this approach produces dramatically better results.

Benchmark Performance: The Numbers

o1's benchmark performance on hard reasoning tasks was genuinely surprising to the AI research community:

AIME 2024 (mathematics competition): o1 achieved 83% accuracy, compared to GPT-4o's 13%
GPQA (science PhD-level questions): o1 scored 78%, surpassing human expert accuracy
Codeforces competitive programming: o1 reached ratings in the 89th percentile of human competitive programmers
International Mathematics Olympiad: o1 solved 6 out of 6 problems in one evaluation protocol

These numbers represent category-level improvements over previous models, not incremental gains. The jump from 13% to 83% on AIME is not an improvement — it's a fundamentally different capability tier.

Where o1 Accuracy Matters Most

o1's accuracy advantages are most pronounced in specific domains: competition mathematics, physics and chemistry problems, formal logical reasoning, complex coding challenges, and scientific analysis. These are tasks where the answer is definitively right or wrong and where arriving at the right answer requires sustained accurate reasoning across many steps.

For everyday tasks — writing, summarization, casual Q&A, content generation — o1's accuracy advantage over GPT-4o is much smaller or absent. The extended reasoning process is most valuable when the problem is hard enough to benefit from deliberate step-by-step analysis.

Legal and scientific reasoning are adjacent domains where o1 shows promise. Complex regulatory analysis, multi-step legal argument evaluation, and scientific hypothesis testing all potentially benefit from o1's reasoning accuracy. However, these applications are still emerging and require careful evaluation.

The Speed and Cost Tradeoff

o1 is significantly slower than GPT-4o. Where GPT-4o might respond in seconds, o1 may take a minute or more for hard problems because the extended reasoning process takes time. This is not a bug — the deliberation is what produces better answers — but it makes o1 impractical for use cases requiring rapid responses.

o1 is also more expensive per token than GPT-4o. For hard reasoning problems where accuracy is paramount, the cost premium is often justified. For everyday tasks, GPT-4o delivers comparable quality much faster and cheaper.

Verdict

o1 is significantly more accurate than GPT-4o for hard mathematical, scientific, and logical reasoning tasks. The speed and cost tradeoffs make it the right tool for accuracy-critical hard problems, not everyday AI use.

Trust Rating: o1 9.5/10 for hard math and reasoning tasks, 8/10 for general tasks (where GPT-4o is faster and cheaper)

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Frequently Asked Questions

Why is o1 so much more accurate at math than GPT-4o?

o1 uses an extended internal chain-of-thought reasoning process that allows it to work through mathematical problems step by step, check its work, and revise before producing an answer. GPT-4o generates answers more directly from patterns, which works well for common problems but fails on novel multi-step math problems that require careful sequential reasoning.

When should I use o1 instead of GPT-4o?

Use o1 when: working on complex mathematics, physics, or chemistry problems; doing formal logical analysis; working on hard algorithmic coding challenges; or any task where the problem is hard enough that GPT-4o is making errors and you need significantly better accuracy. For everyday writing, research, and analysis, GPT-4o is faster and cheaper with comparable quality.

Does o1 still hallucinate?

Yes, o1 can still hallucinate, though its extended reasoning process reduces hallucination frequency on problems where it can reason through to a verifiable answer. On factual recall tasks — specific dates, names, statistics — o1 has similar hallucination risks to GPT-4o because the reasoning process doesn't help when the required fact simply isn't in its training data.