OpenAI o1 vs GPT-4o: Reasoning vs Speed Compared
OpenAI o1 and GPT-4o solve two different problems. GPT-4o is the fast, multimodal model built for real-time chat, voice, and everyday tasks, while OpenAI o1 is a reasoning model that thinks step by step before answering hard math, coding, and science questions. This AI Comparison guide breaks down how the two OpenAI models differ across reasoning quality, speed, pricing, multimodal support, and real workloads so you can match the right model to each job.
The short version is simple. Pick GPT-4o for speed, low cost, and anything involving images or audio, and pick o1 when accuracy on a complex problem matters more than latency or price.
One caveat matters in 2026: OpenAI has since shipped o3, o4-mini, and the GPT-5 series, so o1 now sits at the older end of the reasoning line. The tradeoffs below still explain how OpenAI splits fast general models from slow reasoning models today.
OpenAI o1 vs GPT-4o: Quick Comparison
GPT-4o is the better default for speed, cost, and multimodal work, while OpenAI o1 is the better pick for complex reasoning tasks where a wrong answer is expensive. The table below summarizes the core differences between the two OpenAI models.
| Attribute | OpenAI o1 | GPT-4o |
|---|---|---|
| Model type | Reasoning model with hidden chain of thought | Omnimodal general-purpose model |
| First release | o1-preview September 2024, full o1 December 2024 | May 2024 |
| Accepted inputs | Text and images | Text, images, and audio |
| Generated outputs | Text | Text, images, and audio |
| Context window | 200,000 tokens | 128,000 tokens |
| Maximum output | 100,000 tokens | 16,384 tokens |
| Typical response time | Several seconds to over a minute | 1 to 3 seconds |
| API input price | $15 per million tokens | $2.50 per million tokens |
| API output price | $60 per million tokens | $10 per million tokens |
| Voice mode | No | Yes, powers Advanced Voice Mode |
| Strongest at | Multi-step reasoning, competition math, hard coding | Speed, conversation, multimodal tasks |
| Status in 2026 | Legacy, succeeded by o3 and GPT-5 | Legacy, succeeded by GPT-4.1 and GPT-5 |
Both models came from OpenAI, share the same safety stack, and were available inside ChatGPT and the API during their prime. The difference is how they spend compute: GPT-4o answers immediately, and o1 spends extra time reasoning before it replies. For a related comparison, see Claude vs ChatGPT.
What Is OpenAI o1?
OpenAI o1 is a reasoning model trained with reinforcement learning to produce a long internal chain of thought before it answers. OpenAI released o1-preview in September 2024 and the full o1 model in December 2024.
o1 spends “thinking” time generating hidden reasoning tokens that the user never sees but still pays for. This process lets it plan, check its own work, and backtrack, which raises accuracy on multi-step problems in math, science, and programming.
The December 2024 release added image input, function calling, structured outputs, and developer messages to o1. It still has no audio input, no voice mode, and slower responses than GPT-4o.
OpenAI also shipped an o1 pro mode for ChatGPT Pro subscribers that uses more compute for even harder problems. For most people, the standard o1 model was the reasoning option inside ChatGPT Plus.
What Is GPT-4o?
GPT-4o is OpenAI’s omnimodal model that handles text, images, and audio in a single network with near real-time speed. The “o” stands for “omni,” and OpenAI launched it in May 2024.
GPT-4o became the default ChatGPT model for both free and paid users, and it powers Advanced Voice Mode. It reads and generates images, accepts voice input, and holds fluent spoken conversations with low latency.
GPT-4o offers a 128,000-token context window, costs a fraction of older GPT-4 versions, and responds in a couple of seconds. That balance of price, speed, and capability made it the workhorse model for most ChatGPT and API traffic through 2024 and 2025.
GPT-4o is a strong generalist rather than a specialist. It writes, summarizes, translates, answers questions, and does light coding well, but it can slip on long multi-step logic problems. For a version-by-version look at how it improved, see our GPT-4o vs GPT-4 comparison and the GPT-4o vs GPT-4.1 guide.
Feature Comparison: o1 vs GPT-4o
GPT-4o leads on modality, speed, and tools, while o1 leads on structured reasoning and output length. The four differences below decide most model choices.
Reasoning approach
o1 generates a private chain of thought and can run for many seconds before answering, which suits proofs, algorithm design, and audits. GPT-4o answers in one pass, so it is faster but more likely to skip a step on a hard problem.
Multimodality
GPT-4o accepts text, images, and audio and returns text, images, and spoken audio. o1 accepts text and images only and returns text, so it cannot join a voice conversation or narrate a response.
Context and output length
o1 handles a 200,000-token context window and up to 100,000 output tokens, which helps with large codebases and long reports. GPT-4o handles 128,000 input tokens and 16,384 output tokens, which is enough for most chat and document tasks.
Tools and availability
GPT-4o reached free ChatGPT users, the API, and Advanced Voice Mode. o1 was gated to ChatGPT Plus and Pro plans and to paid API tiers, and at launch it lacked web browsing and file search that GPT-4o could use.
Performance Comparison: Benchmarks and Speed
OpenAI o1 posts far higher scores on reasoning-heavy benchmarks, while GPT-4o wins every test that measures response speed. OpenAI’s published evaluations show the gap clearly.
| Benchmark | OpenAI o1 | GPT-4o |
|---|---|---|
| AIME 2024 (competition math) | 83.3% | 13.4% |
| GPQA Diamond (PhD-level science) | 78.0% | 53.6% |
| MMLU (general knowledge) | 92.3% | 88.7% |
| MATH benchmark | 94.8% | 76.6% |
| Codeforces coding percentile | 89th | 11th |
The pattern is consistent. On problems that need one or two steps, the two models land close together, and on problems that need five or more steps, o1 pulls far ahead.
Speed runs the other way. GPT-4o typically replies in 1 to 3 seconds, while o1 often takes 10 to 60 seconds depending on how hard the question is. Independent testing by Vellum estimated o1 at roughly 30 times slower than GPT-4o on comparable prompts.
For everyday questions, that latency makes o1 feel sluggish. For a contract review or a tricky bug, the extra time is a fair trade for a correct answer.
Pricing: o1 vs GPT-4o
GPT-4o costs about one sixth of OpenAI o1 per token, and o1’s hidden reasoning tokens push the real gap even wider. API list prices tell the story.
| Plan or metric | OpenAI o1 | GPT-4o |
|---|---|---|
| API input price | $15.00 per million tokens | $2.50 per million tokens |
| API output price | $60.00 per million tokens | $10.00 per million tokens |
| Reasoning tokens billed | Yes, charged as output | None |
| ChatGPT access | Plus and Pro plans | Free, Plus, and Pro plans |
o1 charges 6 times more for input and 6 times more for output than GPT-4o. Because o1 also bills the reasoning tokens it generates before the visible answer, a single o1 response can cost several times what the token count suggests.
Inside ChatGPT, both models sat in the $20 per month Plus plan during their prime. Free users got GPT-4o with message limits, while o1 needed a paid plan, and o1 pro mode required the $200 per month Pro plan.
Pros and Cons
OpenAI o1 trades speed and price for reasoning accuracy, while GPT-4o trades peak reasoning for speed, cost, and multimodal range. The lists below cover the tradeoffs that matter in daily use.
OpenAI o1 strengths
o1 shows 4 advantages on hard problems:
- Highest accuracy on multi-step math, science, and logic tasks
- Large 200,000-token context and 100,000-token output ceiling
- Self-checks its work and catches its own errors more often
- Strong performance on competitive-programming and debugging tasks
OpenAI o1 limitations
o1 carries 4 clear drawbacks:
- Responses take 10 to 60 seconds, which breaks real-time use
- API cost runs about 6 times GPT-4o before reasoning tokens
- No audio input, no voice mode, and limited tool support at launch
- Verbose answers that can over-explain simple requests
GPT-4o strengths
GPT-4o offers 4 practical benefits:
- Near real-time responses suited to chat and voice
- Full multimodal support for text, images, and audio
- Roughly one sixth the token cost of o1
- Available on the free ChatGPT plan
GPT-4o limitations
GPT-4o has 3 notable weaknesses:
- Lower accuracy on long multi-step reasoning problems
- Smaller 16,384-token maximum output
- Can present a confident but flawed answer without flagging uncertainty
User Reviews and Community Feedback
Developers and writers report a clear split: o1 for occasional hard problems, GPT-4o for daily work. Feedback on the OpenAI developer forum, Reddit, and independent blogs points the same direction.
Engineers who tested o1 for routine coding found it too slow and too verbose for quick iteration, and several kept GPT-4o as their default editor model. The same engineers praised o1 for untangling complex bugs and architectural decisions that GPT-4o rushed through.
Writers and marketers generally prefer GPT-4o for tone and turnaround, and many say o1 produces stiff prose. A common complaint across both groups is the model picker itself, since choosing between o1, GPT-4o, and their mini variants adds friction. For a related comparison, see GPT-5 vs GPT-5 Mini.
Financial and legal users describe o1 as worth the wait for analysis they would otherwise do by hand. Their advice is to write a detailed prompt once and let o1 run rather than treating it like a chat partner.
Use Cases: When to Use o1 vs GPT-4o
Match the model to the task: GPT-4o for volume and speed, o1 for depth and correctness. These lists cover the most common jobs.
Choose GPT-4o for these 5 workloads:
- Customer support chat and voice assistants that need fast replies
- Content drafting, summaries, and translation
- Image understanding and image generation tasks
- High-volume API jobs where cost per call matters
- General questions, brainstorming, and light coding help
Choose OpenAI o1 for these 5 workloads:
- Competition-level math and quantitative modeling
- Debugging large or unfamiliar codebases
- Scientific reasoning and research analysis
- Legal, tax, and financial review that rewards careful logic
- Multi-step planning where each step depends on the last
Many teams use both models together. GPT-4o drafts, classifies, or routes a request quickly, and o1 handles the small share of cases that need verified reasoning.
Final Recommendation
Use GPT-4o as your default model and reach for OpenAI o1 only when a task genuinely needs step-by-step reasoning. The choice comes down to how much a wrong answer costs.
Choose GPT-4o if any of these 4 conditions apply:
- You need responses in a few seconds or in a voice conversation
- Your workload involves images or audio
- You run high request volume and watch token cost
- Your tasks are writing, chat, translation, or light coding
Choose OpenAI o1 if any of these 3 conditions apply:
- Your problem needs five or more dependent reasoning steps
- Accuracy on math, science, or code outweighs speed and price
- You can send a complete prompt and wait up to a minute for the answer
If you are picking a current OpenAI model rather than a historical one, start with our OpenAI Models Guide, since o3, o4-mini, and GPT-5 now cover the reasoning role o1 introduced.
Alternatives to o1 and GPT-4o
Newer reasoning and general models now beat both o1 and GPT-4o on most metrics. Consider these options before committing to either legacy model.
- OpenAI o3 and o4-mini deliver o1-style reasoning at lower cost and higher speed
- GPT-4.1 and GPT-5 replace GPT-4o as the fast general default
- Claude from Anthropic offers an extended-thinking mode that toggles reasoning on demand
- Gemini 2.5 Pro from Google pairs long context with strong reasoning
- DeepSeek R1 provides open-weight reasoning at a low price
For a head-to-head on the general-purpose side, read our Claude vs ChatGPT comparison, and check the GPT-4o vs GPT-4o Mini guide if budget is the main constraint.
Frequently Asked Questions
Short answers to the most common questions about OpenAI o1 vs GPT-4o are below.
Is OpenAI o1 better than GPT-4o?
o1 is better at multi-step reasoning, competition math, and hard coding, where OpenAI’s benchmarks show large gaps. GPT-4o is better at speed, cost, multimodal tasks, and everyday conversation. Neither model is better overall, since they target different jobs.
Is o1 worth the higher price?
o1 is worth it when a correct answer saves more time or money than the extra cost, such as legal review or complex debugging. For chat, drafting, and high-volume API calls, GPT-4o gives better value. The reasoning-token billing means o1 costs should be tested on your real prompts first.
Why is OpenAI o1 so slow?
o1 generates a long hidden chain of thought before it produces a visible answer, and that reasoning phase can run from 10 to 60 seconds. GPT-4o skips that phase and answers in one pass. The delay is the mechanism that makes o1 more accurate on hard problems.
Can OpenAI o1 handle images and voice?
o1 accepts image input and text input but returns text only. It has no audio input and no voice mode, so spoken conversations require GPT-4o and Advanced Voice Mode.
Is o1 available on the free ChatGPT plan?
o1 required a paid plan, either ChatGPT Plus or Pro, during its release period. GPT-4o was available on the free plan with message limits. o1 pro mode needed the $200 per month Pro plan.
What replaced OpenAI o1?
OpenAI released o3 and o4-mini in 2025 as faster, cheaper reasoning models, and the GPT-5 series folded reasoning and fast responses into one model. o1 is now a legacy model, and OpenAI has deprecated the o1-preview and o1-mini API snapshots.
Final Verdict
GPT-4o is the model to use for most work, and OpenAI o1 is the one to call in for problems that punish shallow thinking. GPT-4o wins on speed, price, and modality, and o1 wins on reasoning accuracy and output length.
If you are building today, skip both and use OpenAI’s current lineup, since o3, o4-mini, GPT-4.1, and GPT-5 improve on every axis this comparison measures. The o1 versus GPT-4o split still matters as the template OpenAI uses: a fast general model for volume, and a slow reasoning model for the hard 10 percent.
