OpenAI o1 Mini vs GPT-4o: Which Model Fits Your Needs? [2025]

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The gap between o1 mini and GPT-4o isn’t just a difference in specs—it’s a fundamental choice about what you value in AI.

O1 mini doesn’t just perform well on mathematical tasks—it dominates them. This model scored an impressive 83% on International Mathematics Olympiad qualifying exams while GPT-4o solved only 13% of identical problems. This isn’t a slight edge—it’s a completely different league of reasoning capability.

Speed tells a different story. GPT-4o generates approximately 103 tokens per second compared to o1 mini’s 74 tokens. But raw speed isn’t everything. O1 mini excels in graduate-level reasoning, scoring 60 on the GPQA benchmark against GPT-4o’s 53.6. Its coding abilities are similarly stronger, achieving 92.4 on the Human Eval benchmark versus GPT-4o’s 90.2.

These performance gains don’t come cheap. O1 mini is priced at $15 per million input tokens and $60 per million output tokens—roughly 6 times more expensive than GPT-4o’s $2.50 for input and $10 for output tokens. This creates a clear decision point: pay premium prices for advanced reasoning or choose more affordable, faster responses.

We’ll help you understand both models across key benchmarks. Our goal isn’t to pick a winner but to help you determine which AI solution best matches your specific needs and budget constraints.

Performance Across Key Tasks: Reasoning, Language, and Classification

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O1 mini shines in specialized domains where deep reasoning matters most. The performance differences between these models become clear when we break down specific task categories.

Math Accuracy: 83% vs 13% on Olympiad Benchmarks

O1 mini doesn’t just solve math problems—it masters them. On International Mathematics Olympiad (IMO) qualifying exams, o1 mini achieved an 83% success rate, while GPT-4o managed only 13.4%. This isn’t a small gap—it’s a fundamental difference in capability.

On the American Invitational Mathematics Examination (AIME), o1 mini places among the top 500 US high school students. The model correctly answers about 11 out of 15 questions (70%), nearly matching the full o1 model’s performance (74.4%).

Reasoning Riddles: 60% vs 60% Accuracy

Both models score identically on reasoning riddles, each achieving 60% accuracy. This tells us something important: o1 mini’s advantage doesn’t extend to all reasoning tasks.

The difference lies in approach. O1 mini systematically explores possible solutions and thinks longer before responding. GPT-4o provides quicker answers but sometimes misses the depth that comes with more thorough analysis.

Classification Precision: 86% vs 73% vs 82%

GPT-4o leads with 86% precision, making it the better choice when you need to avoid false positives. O1 mini excels in recall, capturing 82% of true cases. For context, Claude 3.5 Sonnet shows balanced performance with a 77% F1 score.

O1 mini consistently outperforms GPT-4o on academic benchmarks that require deep thinking. On Graduate-level Physics Questions (GPQA), o1 mini scores 60% versus GPT-4o’s 46%. For coding tasks on HumanEval, o1 mini reaches 92.4% accuracy compared to GPT-4o’s 90.2%.

This pattern confirms what we’re seeing: o1 mini excels in specialized STEM areas, while GPT-4o maintains strong performance across broader language tasks.

Latency and Speed: How Fast Are These Models?

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Speed isn’t just a nice-to-have feature—it’s often the deciding factor for practical applications. The difference between waiting seconds versus minutes dramatically affects how these models fit into your workflow.

Response Time: GPT-4o vs o1 Mini (30x Faster)

The speed gap between these models isn’t small—it’s dramatic. GPT-4o responds almost immediately, while o1 mini needs approximately 30 times longer to process answers. This isn’t just a technical difference. It’s a fundamentally different approach to problem-solving.

O1 mini thinks longer before responding—often 2-3 minutes for complex queries. GPT-4o delivers results within seconds. This makes GPT-4o the clear choice when you need real-time interactions.

These differences stem from their core design philosophies. GPT-4o balances speed with quality, making it perfect for scenarios where immediate response matters more than exhaustive analysis. This approach works exceptionally well for customer service or data analysis applications where quick replies create immediate value.

Throughput: 143 Tokens/sec vs 80 Tokens/sec

Here’s where things get interesting. Once o1 mini finishes its initial "thinking" phase, it actually produces content faster. The o1 model generates approximately 143 tokens per second, beating GPT-4o’s 77-85 tokens per second.

This creates a unique performance profile: o1 mini takes much longer to start but then outpaces GPT-4o in content generation speed. As Vellum.ai notes: "While its output speed is significantly higher than other models, its latency, or time-to-think, is about 30x longer than GPT-4o".

For developers, this speed differential creates a clear choice. GPT-4o enables applications that need fast, real-time responses like customer support chatbots. O1 mini shines in scenarios where thoughtful problem-solving matters more than immediate reactions.

The decision comes down to your priorities: GPT-4o offers quick initial responses perfect for interactive applications, while o1 mini takes its time but delivers faster output once it starts producing content.

Cost Efficiency: Token Pricing and Budget Impact

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Money matters when selecting AI models. Your choice between o1 mini and GPT-4o doesn’t just affect technical performance—it directly impacts your budget and return on investment.

Input/Output Token Costs: A Clear Divide

The price difference between these models isn’t subtle. GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens. The full o1 model, meanwhile, commands $15.00 per million input tokens and $60.00 per million output tokens—six times more expensive than GPT-4o.

O1 mini offers a middle ground at $1.10 per million input tokens and $4.40 per million output tokens. This positions it between GPT-4o and the full o1 model. For comparison, GPT-4o mini provides an even more affordable option at just $0.15 per million input tokens and $0.60 per million output tokens.

This creates a straightforward pricing hierarchy:

When Does o1 Mini’s Cost Make Sense?

Not every project justifies premium pricing. O1 mini creates value primarily for specialized applications that need advanced STEM reasoning. Its performance on mathematical olympiad benchmarks makes it worth considering for complex problem-solving tasks.

OpenAI notes that o1 mini achieves "comparable performance on many useful reasoning tasks, while being significantly more cost efficient" than the full o1 model. It maintains strong performance on coding challenges, reaching 1650 Elo rating on Codeforces—nearly matching the full o1’s 1673.

O1 mini becomes the smart choice for:

• Educational platforms teaching high-level STEM concepts
• Research projects requiring sophisticated mathematical analysis
• Programming environments needing advanced algorithmic reasoning
• Applications where reasoning quality matters more than response time

Experts point out that "o1 Mini is ideal for applications prioritizing speed and cost-efficiency". At its core, o1 mini offers a balanced compromise—delivering much of o1’s specialized reasoning capabilities at approximately 80% lower cost.

Best Use Cases by Task Type

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Smart AI selection isn’t about finding the "best" model—it’s about matching the right tool to your specific needs. O1 mini and GPT-4o each shine in different scenarios, creating clear guidelines for when to use each.

Real-Time Chatbots: GPT-4o Advantage

GPT-4o dominates in speed-critical applications. With response times as quick as 232 milliseconds and averaging just 320 milliseconds, it creates smooth, natural conversations without the awkward pauses that frustrate users. This matters—one fintech company saw customer satisfaction jump 31% after cutting chatbot response time from 1.2 seconds to 190 milliseconds.

The economics work too. GPT-4o’s lower cost structure makes it practical for high-volume customer interactions. Its web search capabilities—something o1 mini lacks—further enhance its value for customer-facing tools. For businesses that need quick responses and affordable scaling, GPT-4o is the practical choice.

STEM and Logic Tasks: o1 Mini’s Strength

O1 mini excels where deep thinking matters more than speed. On the American Invitational Mathematics Examination (AIME), it achieves 70% accuracy—performance that places it among the top 500 U.S. high school students. Its coding abilities reach 1650 Elo on Codeforces, putting it in the 86th percentile of competitive programmers.

The pattern continues across scientific fields. O1 mini consistently outperforms GPT-4o on academic benchmarks like the Graduate-level Physics Questions Assessment (GPQA). Educational institutions, research teams, and STEM-focused applications gain enough value from these specialized capabilities to justify the higher cost.

Content Creation and Editing: GPT-4o Preferred

For content generation tasks, GPT-4o delivers better overall results. Human reviewers consistently prefer its outputs for general writing tasks, where it provides coherent, relevant content more efficiently than o1. Tasks like summarization, creative writing, and content editing simply don’t need the advanced reasoning capabilities that drive o1 mini’s premium pricing.

GPT-4o particularly shines for PowerPoint creation, social media content, and creative writing—all areas that benefit from its balanced capabilities without requiring deep reasoning. Its ability to work with multiple file formats through Projects makes it especially valuable for content creators who work across different media.

Comparison Table

OpenAI o1 Mini vs GPT-4o: Which Model Fits Your Needs?

_{Image Source: AI Generated}

The gap between o1 mini and GPT-4o isn’t just a difference in specs—it’s a fundamental choice about what you value in AI.

O1 mini doesn’t just perform well on mathematical tasks—it dominates them. This model scored an impressive 83% on International Mathematics Olympiad qualifying exams while GPT-4o solved only 13% of identical problems. This isn’t a slight edge—it’s a completely different league of reasoning capability.

Speed tells a different story. GPT-4o generates approximately 103 tokens per second compared to o1 mini’s 74 tokens. But raw speed isn’t everything. O1 mini excels in graduate-level reasoning, scoring 60 on the GPQA benchmark against GPT-4o’s 53.6. Its coding abilities are similarly stronger, achieving 92.4 on the Human Eval benchmark versus GPT-4o’s 90.2.

These performance gains don’t come cheap. O1 mini is priced at $15 per million input tokens and $60 per million output tokens—roughly 6 times more expensive than GPT-4o’s $2.50 for input and $10 for output tokens. This creates a clear decision point: pay premium prices for advanced reasoning or choose more affordable, faster responses.

We’ll help you understand both models across key benchmarks. Our goal isn’t to pick a winner but to help you determine which AI solution best matches your specific needs and budget constraints.

Performance Across Key Tasks: Reasoning, Language, and Classification

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_{Image Source: DEV Community}

O1 mini shines in specialized domains while GPT-4o offers balanced performance across a wider range of tasks. The differences become clear when we examine specific capabilities.

Math Accuracy: 83% vs 13% on Olympiad Benchmarks

O1 mini doesn’t just solve math problems—it masters them. On International Mathematics Olympiad qualifying exams, o1 mini achieved an 83% success rate while GPT-4o managed only 13.4%. This isn’t a minor gap—it’s a fundamental difference in reasoning ability.

On the American Invitational Mathematics Examination (AIME), o1 mini scores place it among the top 500 US high school students. The model averages 11 out of 15 questions correct (70%), nearly matching full o1 performance (74.4%).

Reasoning Riddles: 60% vs 60% Accuracy

Both models perform identically on reasoning riddles, each achieving 60% accuracy. This suggests that o1 mini’s mathematical advantage doesn’t extend to all reasoning tasks.

The difference lies in approach—o1 mini systematically explores solutions and thinks longer before responding. GPT-4o, optimized for efficiency, provides quicker but sometimes less thorough answers.

Classification Precision: 86% vs 73% vs 82%

GPT-4o leads in classification precision at 86%, making it ideal when correct positive predictions matter most. O1 mini excels in recall measurements, capturing 82% of true cases.

O1 mini consistently outperforms GPT-4o on academic benchmarks requiring deep reasoning. On Graduate-level Physics Questions Assessment (GPQA), o1 mini scores 60% versus GPT-4o’s 46%. Similarly, on HumanEval coding tasks, o1 mini reaches 92.4% accuracy compared to GPT-4o’s 90.2%.

The pattern is clear: o1 mini dominates specialized STEM tasks, while GPT-4o maintains competitive performance across broader language applications.

Latency and Speed: How Fast Are These Models?

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Speed isn’t just a technical specification—it’s a crucial factor that directly impacts real-world applications. O1 mini and GPT-4o present dramatically different approaches to processing time and response generation.

Response Time: GPT-4o vs o1 Mini (30x Faster)

The speed difference between these models isn’t subtle. GPT-4o responds significantly faster, with o1 mini requiring approximately 30 times longer to process answers. This delay comes from o1 mini’s chain-of-thought reasoning, which demands more computational resources and processing time.

For complex queries, o1 mini typically takes 2-3 minutes to generate responses, while GPT-4o delivers results within seconds. This stark contrast makes GPT-4o the obvious choice for real-time interactions.

The latency gap reflects fundamentally different design approaches. GPT-4o balances response times with thorough output, making it ideal for scenarios where moderate trade-offs between speed and depth work well. This makes GPT-4o perfect for customer service or real-time data analysis where quick replies matter.

Throughput: 143 Tokens/sec vs 80 Tokens/sec

Once o1 mini finishes its initial "thinking," it demonstrates superior throughput. The o1 model produces approximately 143 tokens per second, outpacing GPT-4o’s 77-85 tokens per second.

This creates an unusual performance profile: o1 mini has significantly longer thinking time followed by faster text generation. It’s like a student who takes longer to solve a problem but writes the answer down more quickly once they’ve figured it out.

For developers, this speed differential creates a clear choice: GPT-4o for fast, real-time text responses like customer support chatbots, or o1 mini for cases where thoughtful problem-solving matters more than immediate responses.

The trade-off is clear: GPT-4o offers substantially faster initial responses ideal for interactive applications, while o1 mini requires longer processing time but ultimately generates content at a higher rate once it begins producing output.

Cost Efficiency: Token Pricing and Budget Impact

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The price difference between these models isn’t just a footnote—it’s a major factor in your decision-making process. Understanding the true cost impact helps you align your technology choices with both capability needs and budget constraints.

Input/Output Token Costs: $2.50/$10 vs $15/$60

The official pricing structures reveal significant cost differences. GPT-4o is priced at $2.50 per million input tokens and $10.00 per million output tokens. The full o1 model, by contrast, costs $15.00 per million input tokens and $60.00 per million output tokens—six times more expensive than GPT-4o.

O1 mini positions itself as a more affordable alternative at $1.10 per million input tokens and $4.40 per million output tokens, placing it between GPT-4o and the full o1 model. For comparison, GPT-4o mini offers even greater savings at just $0.15 per million input tokens and $0.60 per million output tokens.

This creates a clear cost-performance spectrum:

When o1 Mini’s Cost is Justified

O1 mini’s higher price makes sense primarily for specialized applications requiring advanced STEM reasoning. Given its performance on mathematical olympiad benchmarks, o1 mini creates value for applications focused on complex problem-solving.

According to OpenAI, o1 mini achieves "comparable performance on many useful reasoning tasks, while being significantly more cost efficient" than the full o1 model. It maintains competitive performance on coding challenges, reaching an impressive 1650 Elo rating on Codeforces—nearly matching the full o1’s 1673.

O1 mini becomes the economical choice for:

• Educational platforms focusing on high-level STEM instruction
• Research applications requiring sophisticated mathematical analysis
• Programming environments needing advanced algorithmic reasoning
• Applications where the quality of reasoning outweighs response time requirements

O1 mini presents a balanced compromise—offering much of o1’s specialized reasoning capabilities at approximately 80% lower cost.

Best Use Cases by Task Type

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_{Image Source: Tactiq}

The choice between o1 mini and GPT-4o isn’t about which model is better—it’s about which model is better for your specific needs. Each offers distinct advantages for different applications.

Real-Time Chatbots: GPT-4o Advantage

GPT-4o excels in speed-sensitive applications, responding in as little as 232 milliseconds with an average of 320 milliseconds. This speed makes it perfect for live customer support or conversational AI requiring immediate feedback. One fintech startup reported a 31% boost in customer satisfaction after switching to a faster response model, reducing chatbot latency from 1.2 seconds to 190 milliseconds.

GPT-4o’s lower cost structure makes it economically viable for high-volume customer interactions. Its ability to search the web—a feature o1 mini lacks—enhances its value for customer-facing applications. For businesses prioritizing real-time query handling and affordable scaling, GPT-4o remains the practical choice.

STEM and Logic Tasks: o1 Mini’s Strength

O1 mini excels in specialized STEM reasoning tasks. On mathematics benchmarks, it achieves 70% accuracy on the American Invitational Mathematics Examination (AIME), placing it among the top 500 US high school students. Its coding capabilities are equally impressive, reaching 1650 Elo on Codeforces (86th percentile of competitive programmers).

O1 mini demonstrates superior performance in scientific reasoning, outperforming GPT-4o on academic benchmarks like Graduate-level Physics Questions Assessment (GPQA). Educational institutions, research organizations, and STEM-focused applications benefit most from o1 mini’s specialized capabilities, justifying its higher cost through superior reasoning outcomes.

Content Creation and Editing: GPT-4o Preferred

For content generation tasks, GPT-4o generally delivers better results. Human expert reviews consistently show preference for GPT-4o in general NLP tasks, where it provides coherent and relevant responses more efficiently than o1. Tasks like summarization, creative writing, and content editing typically don’t require the advanced reasoning capabilities that justify o1 mini’s premium pricing.

GPT-4o shines for PowerPoint presentation creation, social media content generation, and creative writing—tasks that benefit from its balanced capabilities without requiring deep reasoning. Its ability to integrate with multiple file formats through Projects makes it particularly valuable for content creators working across different media.

Comparison Table

Conclusion

Your choice between o1 mini and GPT-4o isn’t about finding the "best" model—it’s about finding the right fit for your specific needs. These models represent different approaches to AI, each with clear strengths and limitations.

O1 mini stands out in complex mathematical reasoning, scoring an impressive 83% on Mathematical Olympiad problems. GPT-4o delivers significantly faster responses for interactive applications. This fundamental difference shapes how each model fits into your workflow.

Speed matters. GPT-4o responds almost instantly, while o1 mini takes time to "think" before generating content. For customer-facing applications needing immediate responses, GPT-4o is the clear choice. For thorough problem-solving where time isn’t critical, o1 mini delivers superior results.

Cost efficiency creates another decision point. GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens. O1 mini charges $1.10 per million input tokens and $4.40 per million output tokens—less than the full o1 model but still a premium compared to GPT-4o mini. This means carefully evaluating whether specialized reasoning capabilities justify the additional expense.

The best choice depends on your primary use case. Content creation, customer support, and real-time interactions benefit from GPT-4o’s balanced capabilities and faster response times. Educational platforms, research applications, and specialized STEM environments typically extract more value from o1 mini’s superior reasoning abilities.

We help clients select AI models based on their specific operational requirements rather than pursuing the most advanced or least expensive option. The right choice isn’t about technical specs—it’s about how these capabilities align with your business goals.

FAQs

Q1. What are the key strengths of OpenAI o1 Mini?
OpenAI o1 Mini excels in complex mathematical reasoning and STEM-related tasks. It demonstrates impressive performance on advanced benchmarks like Mathematical Olympiad problems and graduate-level physics questions. This model is particularly well-suited for educational platforms, research applications, and programming environments that require sophisticated problem-solving capabilities.

Q2. How does GPT-4o compare to o1 Mini in terms of speed?
GPT-4o significantly outperforms o1 Mini in terms of response time. While GPT-4o can generate responses within seconds, o1 Mini typically requires 2-3 minutes for initial processing. However, once o1 Mini begins generating content, it demonstrates a higher throughput of about 143 tokens per second compared to GPT-4o’s 80 tokens per second.

Q3. Which model is more cost-effective for general use?
For general-purpose applications, GPT-4o is more cost-effective. It’s priced at $2.50 per million input tokens and $10.00 per million output tokens, whereas o1 Mini costs $1.10 per million input tokens and $4.40 per million output tokens. The choice depends on specific use cases and whether the advanced reasoning capabilities of o1 Mini justify its higher cost.

Q4. What types of tasks is GPT-4o best suited for?
GPT-4o is ideal for tasks requiring quick responses and general language understanding. It excels in real-time chatbots, customer support, content creation, and social media content generation. Its ability to integrate with multiple file formats and perform web searches also makes it valuable for diverse content creation tasks.

Q5. How do o1 Mini and GPT-4o compare in classification tasks?
In classification tasks, GPT-4o demonstrates higher precision with 86% accuracy, making it suitable for applications where correct positive predictions are crucial. O1 Mini, on the other hand, shows strength in recall measurements, capturing 82% of true cases. The choice between the two depends on whether precision or recall is more important for the specific classification task at hand.