GPT-4o vs o1: Speed vs Reasoning — Which OpenAI Model to Use?
GPT-4o vs o1 compared: speed, reasoning, AIME/GPQA benchmarks, pricing per Mtok, and when to use each OpenAI model.
Key Takeaways
- GPT-4o answers in under a second; o1 can take 10–60 seconds.
- o1 scores ~83% on AIME 2024 versus GPT-4o's ~13% — a massive reasoning gap.
- GPT-4o costs roughly 6x less per token than o1.
- GPT-4o is natively multimodal (audio + vision); o1 handles text and images only.
- Best practice: default to GPT-4o, escalate only hard reasoning to o1.
Quick Answer
GPT-4o is the better default for everyday work because it answers in under a second, handles text, vision, and audio in one model, and costs a fraction of o1. Choose o1 only when a task genuinely requires multi-step reasoning — competition math, hard algorithmic coding, scientific proofs, or logic puzzles — where its hidden chain-of-thought lifts accuracy dramatically (o1 scores ~83% on AIME 2024 versus GPT-4o's ~13%). The trade-off is latency and price: o1 can take 10–60 seconds per answer and costs roughly 6x more per token. Most production apps route the bulk of traffic to GPT-4o and escalate only the hardest queries to o1.
GPT-4o vs o1: Overview
High-volume chat, vision tasks, low-latency apps, and general assistants
Available in ChatGPT free tier with usage limits
~$2.50 per 1M input tokens, ~$10 per 1M output tokens (API)
GPT-4o vs o1: Feature Comparison
| Feature | GPT-4o | o1 |
|---|---|---|
| Median latency | Under 1 secondWinner | 10–60 seconds |
| AIME 2024 (math) | ~13% | ~83%Winner |
| GPQA Diamond (science) | ~53% | ~78%Winner |
| Input price per 1M tokens | ~$2.50Winner | ~$15 |
| Multimodal (audio/vision) | Full text, image, audioWinner | Text and image, no audio |
| Context window | 128K tokens | 200K tokensWinner |
Pros & Cons
GPT-4o
Pros
- Sub-second responses suit real-time chat and voice
- Natively multimodal: text, image, and audio in one model
- Roughly 6x cheaper per token than o1
- 128K context window covers most documents and codebases
- Strong general knowledge and writing quality
Cons
- Weak on competition-level math and multi-step logic
- No explicit chain-of-thought, so hard reasoning errors slip through
- Can hallucinate on niche factual queries
- Less reliable on long algorithmic coding problems
o1
Pros
- Dramatically higher accuracy on AIME, GPQA, and Codeforces
- Internal chain-of-thought reduces logical errors
- Excels at multi-step proofs and algorithm design
- Self-checks intermediate steps before answering
- Strong on PhD-level science questions (GPQA Diamond)
Cons
- High latency: 10–60 seconds per response
- Roughly 6x more expensive than GPT-4o
- Reasoning tokens are billed but hidden from you
- Overkill and slower for simple everyday prompts
Our Verdict: GPT-4o vs o1
GPT-4o and o1 solve different problems, so the right choice depends entirely on the task. GPT-4o wins on speed, cost, and multimodality, making it the correct default for the vast majority of production traffic. o1 wins decisively on reasoning-heavy benchmarks but pays for it in latency and price. The smartest architecture is a router that sends most requests to GPT-4o and escalates only genuinely hard reasoning queries to o1. Use GPT-4o if you need fast, cheap, multimodal responses for chat, vision, or voice; use o1 if your task hinges on competition math, scientific reasoning, or hard algorithmic coding.
GPT-4o vs o1 — FAQs
Is o1 always more accurate than GPT-4o?
No. o1 is far more accurate on reasoning-heavy tasks like competition math, scientific problems, and complex algorithmic coding. But on general knowledge, writing, summarization, and simple questions, GPT-4o is comparably accurate and much faster. For most everyday prompts the accuracy difference is negligible, so paying o1's latency and cost premium yields no benefit. Reserve o1 for problems that genuinely require multi-step thinking.
Why is o1 so much slower than GPT-4o?
o1 generates a long internal chain-of-thought before producing its visible answer. It spends "reasoning tokens" working through the problem step by step, self-correcting along the way. This deliberate process is what lifts its accuracy on hard tasks, but it also means a single answer can take anywhere from 10 to 60 seconds. GPT-4o produces answers directly without this hidden deliberation, so it responds in under a second.
Do I pay for o1's hidden reasoning tokens?
Yes. o1 bills you for the reasoning tokens it generates internally even though you never see them in the response. Combined with its higher base output price (~$60 per 1M tokens versus GPT-4o's ~$10), this makes o1 substantially more expensive in practice. Always estimate cost using both visible and reasoning token consumption, and cap reasoning effort where the API allows it.
Can GPT-4o handle audio and images like o1 cannot?
GPT-4o is natively multimodal across text, image, and audio in a single model, which is why it powers ChatGPT's real-time voice mode. o1 accepts text and images but has no audio capability. If your application needs voice interaction or rich multimodal input, GPT-4o is the only option of the two. For text-and-image reasoning tasks, both work, but o1 reasons more deeply.
Which model should I use for coding?
It depends on the coding task. For routine code generation, refactoring, and explaining snippets, GPT-4o is fast and capable enough. For hard algorithmic problems, competitive programming, or debugging subtle logic across many steps, o1 is significantly stronger thanks to its chain-of-thought. A common pattern is using GPT-4o for inline completions and routing difficult, ambiguous tickets to o1.
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