Comparison of GPT-4o, GPT-4o with Scheduled Tasks, GPT-4.5, o1, o3-mini, and o3-mini-high

Performance and Efficiency (Speed, Memory, Energy)

GPT-4o is the original GPT-4 model with relatively fast responses, but the new GPT-4.5 is even faster and significantly more efficient in computation. According to tests, GPT-4.5 delivers 10× better processing efficiency than GPT-4o, meaning it can handle complex tasks faster and at a lower operating costso has lower response latency; in a comparative review, GPT-4.5 was rated as “faster” while GPT-4o was “fast” . The increcy of GPT-4.5 also results in better energy efficiency, as more work is done with less computing power per query. GPT-4o and GPT-4.5 can handle long conversations and large text inputs (thousands of tokens in context windows), making them well-suited for working with long code files or documents.

OpenAI o1 has a different performance pattern: it spends extra time and computing power “thinking” in multiple steps before responding. This means o1 is generally slower than the GPT-4 series models and more demanding in computation and memory . OpenAI itself notes ts significantly more computing power per response as it generates long chains of reasoning internally . In other words, o1 sacrifices speeor increased accuracy. To offer a faster/more efficient variant of o1, OpenAI also released o1-mini, which is ~80% cheaper and significantly faster than the o1-preview (early version of o1) . O1-mini can respond more quickly and at a loweres not have as broad general knowledge as the full o1 model .

The later OpenAI o3 generation follows a similar pattintroduced o3-mini (a smaller version of o3) focusing on precision and speed for technical tasks . O3-mini is optimized to be fast enough to be available even for free broadly released in ChatGPT, including the free tier) . It has three levels of "reasoning effort": low, medium, and high . In the free mods with medium effort, providing balanced speed and accuracy. Forbscribers, there is the o3-mini-high mode, where the model uses more computing power per query for higher quality . This high mode is slower and initially heavily rate-limited (e.g., first 50 requests per week, later 50 Plus users) due to its higher computational cost . O3-mini-high can thus be considered the "turbo mode" that prioritizes accuracy over response time. Even at the high sini is generally faster and lighter than full-sized o1/o3 models, but slightly slower than GPT-4o/4.5 when running at the highest precision.

Regarding memory management and context understanding, all these models can track conversation history reasonably well, but GPT-4o and GPT-4.5 have large context windows that allow them to "remember" long prompts. O1 and o3-mini use a unique strategy where they utilize an internal working memory to reason step-by-step about the answer . This chained reasoning helps them solve complex problems but also means more memory/tokens are consumed internally during genera on the other hand, relies more on its enhanced linguistic and pattern depth to provide intuitive answers without always needing to write out every reasoning step, which contributes to its speed and fluency . In summary, GPT-4.5 is by far the most performance-optimized (fast and energy-efficient) of the models, while o1 and o3-mini-high are theent per response (but use their extra computing power for better reasoning quality). GPT-4o falls in between—fast and powerful, but not as optimized as 4.5 or as deep-thinking as o1.

Capabilities and Use Cases

GPT-4o: This model is a general all-around AI with high capacity across many areas. GPT-4o was trained on an enormous amount of text and demonstrates strong general knowledge, language comprehension, and the ability to produce creative and complex texts. It is well-suited for diverse tasks such as answering knowledge-based questions, writing essays, generating code snippets, and analyzing text. However, it has only moderate factual accuracy and can hallucinate occasionally when handling difficult questions (hallucination rate ~61.8% in tests) . Until recently, GPT-4o was the most powerful model in ChatGPT and functions well as the default for most tasks—ranging from writing assistance and co basic programming advice. Its strengths lie primarily in creativity and language: it formulates text fluently and can adopt different styles. Its weaker points include heavy reasoning tasks such as complex mathematics or logic puzzles, where it lacks the deeper chain-of-thought capability of the o-models.

GPT-4o with Scheduled Tasks (Tasks): This is fundamentally the same GPT-4o model as above but with an additional feature in the ChatGPT interface that allows the AI to plan and execute tasks at a later time. The Tasks (scheduled activities) feature is new and in beta for Plus/Pro users—you can, for example, ask ChatGPT (with GPT-4o + Tasks) to send a news summary every morning at 8 AM, remind you of an activity, or report a stock price . In terms of capacity, the language model’s competence remains unchanged; GPT-4o with scheduling has the same language comprehension as GPT-4o. The difference lies case: it is best suited as a personal assistant that automatically performs tasks at designated times. The strength of this feature is that it can integrate into workflows (e.g., daily reports, reminders) without requiring the user to manually trigger it each time. This means ChatGPT can “work in the future” for the user . A limitation is that the Tasks feature is still in beta—only available for paying users and in GPT-4o mode —and that it is focused on time-scheduled tasks (it does not improve cognitive abilities or speed, for example). In summary, GPT-4o + Scheduled Tasks is best when you want to aurring tasks with AI assistance (e.g., regular code reviews, daily status reports), while other capabilities remain equivalent to standard GPT-4o

GPT-4.5:
GPT-4.5 is the latest generation GPT model and represents an upgrade of the GPT-4 series, with both improved performance and capabilities. It is described as OpenAI's “largest, most knowledgeable” model to date . Its strengths include:

• Better factual knowledge and accuracy: GPT-4.5 has been trained on a broader and updated dataset, giving it deeper world understanding and higher factual accuracy . It hallucinates significantly less than GPT-4o (~37% instead of ~62% in tests) , making it more reliable for knowledge-intensive topics.
• Higher emotional intelligence and conversational ability: The model produces warmer, more intuitive, and natural dialogues . It interprets subtle nuances in user instructions better and follows intentions more precisely. In human evaluations, GPT-4.5 outperformed GPT-4o in 57% of cases for general questions and ~63% for professionally complex questions —suggesting a more sophisticated general capability.
• Creativity and writing quality: GPT-4.5 excels in creative tasks. It is rated as “excellent” in creative writing, compared to “good” for GPT-4o and only “average” for o1/o3-mini . This makes GPT-4.5 ideal for content generation, idea formulation, marketing texts, and similar tasks where tone and style are important.

GPT-4.5 also includes all the new developer features (API support, function calling, "agentic planning," etc.), making it capable of integrating into complex workflows . An interesting feature is that it has been partially trained on data generated by smaller models (a form of distillation) and with parallel pre-training across multiple data centers, giving it an improved “world model” and pattern recognition . Use Cases: GPT-4.5 is best suited when answer quality is the highest priority—such as expert consulting, detailed reports, sensitive customer interactions—or when the task requires a combination of expertise and soft skills (it “collaborates effectively” with the user) . It is also strong for tasks requiring multimodal capabilities (e.g., image and file uploads, as well as the Canvas mode for coding) , though not yet for voice/video. A weakness of GPT-4.5 is that it is resource-intensive (larger model => higher cost); OpenAI notes that it is more expensive to run than GPT-4o and advises caution with costs in large-scale usage . In summary, GPT-4.5 is excellent for advanced conversations, knowledge queries, and demanding text tasks, where maximum accuracy and naturalness are desired—but simpler reasoning tasks can be just as well handled by the cheaper models.

OpenAI o1:
The o1 model introduces a new category within the GPT family, focused on improved reasoning. OpenAI described o1 as a complement to GPT-4o rather than a replacement —meaning o1 addresses tasks where GPT-4 might not be the strongest. O1 has been trained with new optimization methods and a specially tailored training dataset (narrower but deeper in certain domains) and with reinforcement learning to teach the model to reason step-by-step . O1's strength lies in complex problem-solving, logic, and computation-heavy domains. Before providing a final answer, o1 generates a long chain of thought steps (invisible to the user), breaking the problem into subproblems and reasoning through them . The result is that o1 handles significantly harder tasks in, for example, mathematics, science, and programming than GPT-4o. In a test of advanced math problems (AIME, mathematical olympiad-level), o1 solved 83% correctly, compared to only 13% for GPT-4o —a dramatic difference. Similarly, o1 performed at a PhD level in physics, chemistry, and biology in internal tests . In coding, o1 ranked in the 89th percentile on Codeforces programming competitions , indicating very high proficiency. These examples show that o1 excels when the problem is complex or requires multiple reasoning steps. Use cases where o1 shines include mathematical proofs, complex code debugging, scientific analysis, logic puzzles, and similar fields. O1 is also relatively robust against misleading questions—it follows instructions carefully and, due to its reasoning, is better at not violating given constraints (it can "think ahead" and realize if a certain answer would contradict safety guidelines) . Weaknesses of o1 include its limited creativity and general knowledge outside its core domains. Because its focus is on technical reasoning, o1 may provide shorter, more literal responses to open-ended questions. It has been ranked as low in emotional intelligence (compared to GPT-4.5) and only average in free-writing tasks. O1’s breadth of knowledge is good but not as extensive as GPT-4.5 for "world knowledge." Thus, for difficult technical problems, o1 is extremely capable.

OpenAI o3-mini:
O3 is the next generation of OpenAI's reasoning models (they skipped the name "o2"). The full o3 model is not yet widely available at the time of writing, but o3-mini was released as a scaled-down variant for the public in early 2025 . OpenAI describes o3-mini as a “specialized alternative” to o1 for “technical domains requiring precision and speed” . This suggests that o3-mini has been designed to be efficient in coding, logic, and STEM subjects while being fast enough to be used even by free users (unlike the heavier o1). O3-mini possesses advanced chained reasoning ability like o1 but in a smaller format. In ChatGPT, o3-mini is available in three modes: standard (medium, accessible to all users), as well as low and high for lower/higher computational effort . In practice, this is handled through two variants: standard o3-mini (which corresponds to medium) and o3-mini-high (high mode) for paying users. Behind the scenes, high mode allows the model to use more and longer reasoning steps before answering—meaning higher accuracy but more time per question . Capacity-wise, o3-mini already performs very strongly in complex tasks. OpenAI reported that o3 (full model) surpasses o1 in several areas: for example, o3 achieved ~72% on a difficult programming benchmark (SWE-Bench Verified, real GitHub issues), compared to ~49% for o1 . In Codeforces, o3 reached an Elo rating of 2727 (master level), compared to 1891 for o1 —a massive leap. Since o3-mini is a smaller variant, its results are slightly lower than full o3, but the direction is clear: the o3 generation pushes reasoning ability even further. O3-mini’s use cases resemble o1: advanced problem-solving in code, algorithms, mathematics, etc. But thanks to its optimizations, it is also suitable for faster Q&A in these areas—for example, a programmer on the free version can use o3-mini to get reasonably good help with debugging or math problems in real-time. O3-mini-high (for Plus/Pro users) is particularly useful if extra accuracy or depth is needed in technical answers—e.g., for difficult competitive programming problems or complex data analyses—where it can provide more correct solutions than the standard mode. One advantage of o3-mini is that OpenAI has made it more transparent in reasoning: updates were announced to better explain the model’s thought process (for evaluation) , which can give users insight into its solution method (at least partially). Weaknesses: o3-mini, like o1, is not designed for general creativity or casual conversation—outside technical domains, it may perform more modestly. Moreover, o3-mini-high has been limited in usage (due to computational cost), so for very long interactive sessions, GPT-4o/4.5 may be smoother.

Strengths and Weaknesses in Comparison

GPT-4o vs. GPT-4.5:

GPT-4.5 takes the strengths of GPT-4o and enhances them even further. A clear improvement is fact-checking and reliability—GPT-4.5 has fewer incorrect statements and a lower hallucination rate than GPT-4o . It also has broader knowledge due to updated training and a better ability to interpret subtle instructions, making its responses more relevant and accurate . In creative and social tasks, GPT-4.5 is stronger: it expresses itself in a more nuanced and human-like way and understands context/tone better. Where GPT-4o might sound formal or miss subtext, 4.5 delivers more sophisticated phrasing. This is evident in emotional intelligence, where GPT-4.5 is rated high, GPT-4o medium, and the o-models low . So for tasks where tone, style, and empathy matter (customer support, consulting, creative writing), GPT-4.5 is superior. GPT-4.5 is also faster than GPT-4o due to efficiency improvements , paradoxically making it both more capable and more responsive.

At the same time, there are some disadvantages of GPT-4.5 compared to GPT-4o: The model is larger and more expensive, requiring a higher subscription tier (Pro at launch). For simple questions, the difference from GPT-4o might be marginal, so GPT-4o can be “good enough” without consuming as many resources. Another weakness highlighted by some developers is that GPT-4.5 is not as specialized in deep reasoning as o1/o3. It tends to provide quick answers based on its extensive statistical pattern library, which means it sometimes misses the solution to truly tricky logical problems that require persistent step-by-step thinking. As one developer put it: “GPT-4.5 is not a model you can rely on for reasoning tasks... It's designed to be better at conversations, design, and writing” . So while GPT-4.5 can quickly arrive at an answer, it may struggle with a problem that needs a logical breakdown. GPT-4o may hallucinate more and is not as generally intelligent, but it remains a reliable workhorse alternative when 4.5's precision isn't necessary. OpenAI itself seems to be keeping GPT-4o active alongside 4.5 in ChatGPT, suggesting that 4o is sufficient for many tasks and complements 4.5 by being less resource-intensive.

GPT-4 (4o/4.5) vs. OpenAI o1/o3:

This is a tradeoff between general language proficiency and specialized reasoning. GPT-4 models (especially 4.5) have the advantage in general linguistic ability—they write better-structured text, are more creative and emotionally intelligent in tone, and have a vast knowledge of the world . O1 and o3-mini, on the other hand, excel in logical precision and problem-solving. They are explicitly trained to process every problem logically, making them far less likely to miss critical details in complex tasks. For example, the o-models dramatically outperform GPT-4 models in advanced mathematics and physics . Similarly, in coding, o1/o3 perform significantly better in competitive programming challenges (o3-mini reached elite-level Codeforces Elo ratings), while GPT-4 can sometimes make mistakes in highly challenging coding problems . So for reasoning-heavy tasks (proofs, algorithm design, complex logic), o1/o3 have a clear advantage in precision. They are also more consistent in multi-step reasoning—GPT-4 can sometimes lose track of longer reasoning chains or require user reformulation, whereas o1/o3 are built to maintain logical coherence internally.

On the other hand, o1/o3 models have clear weaknesses compared to GPT-4 models. One is response time and cost: the fact that o1 "thinks longer" leads to higher quality but takes more time and computational resources . In interactive conversations, o1 can feel sluggish, whereas GPT-4 responds quickly. O3-mini mitigates this somewhat by providing faster responses in medium mode, but for maximum quality (high mode), it is still slower than GPT-4. Another weakness of the o-models is limited creativity and communicative ability. They are rated low in emotional intelligence and only average in creative writing . This means their responses may be factually correct but short, dry, or less audience-friendly. GPT-4.5, for example, can deliver an engaging explanation of a topic, whereas o1 might respond more analytically and without "flair." For tasks requiring broad general knowledge, o1/o3 may also struggle if the information falls outside their specialized training—e.g., cultural, historical, or niche facts may be better covered by GPT-4 due to its massive training dataset. OpenAI itself noted that o1-mini (the smaller variant) lacked some "broad world knowledge" compared to full o1 , indicating that some information-dense areas were not as heavily prioritized in these models. Therefore, GPT-4 models and o-models complement each other: GPT-4 provides fluid, informative answers to most things, while o1/o3 provide deeply correct answers to the hardest problems—but are less conversational.

Coding Performance and Capabilities in Programming

All these models can generate code and assist with programming tasks, but their competency and style in coding vary somewhat:

• Supported Programming Languages: GPT-4o and GPT-4.5 have been trained on a massive amount of code from the internet (e.g., GitHub), making them highly proficient in popular languages such as Python, JavaScript/TypeScript, Java, C#, C/C++. They also handle web languages (HTML/CSS) and special cases (e.g., SQL, Bash) with surprisingly high accuracy. O1 and o3-mini are also trained on code (e.g., o1 was integrated into GitHub Copilot , indicating strong coding skills). Their focus on STEM domains makes them particularly strong in Python (common for data/AI) and C/C++ and Java (for algorithmic training). Less common languages may be attempted by all models, but GPT-4.5/GPT-4o have likely seen more examples in their general training. O-models may compensate with logic—if they haven't seen a language much, they can still infer syntax through analogy. Overall, Python is the language they all handle best (abundant training examples, and ChatGPT’s code interpreter is Python-based). JavaScript/TypeScript for web development is also well-represented. For low-level languages like C/C++ or Rust, GPT-4.5/GPT-4o can generate correct code for standard tasks, but if optimization for hardware or advanced pointer management is needed, they might make mistakes—similarly, o1/o3 can struggle in highly specialized scenarios. However, the o-models may be better at reasoning through what the code should do logically, which can help with language-agnostic debugging (see below).

• Complexity in Code Generation: When handling large or complex code problems, the models approach the task differently. GPT-4o/4.5 can generate relatively large codebases (they can write long files or multiple function definitions in a single response). GPT-4.5 also supports a large context window, allowing it to keep the entire specification in memory and generate coherent code for multiple modules. It follows user specifications closely and writes code that often looks correct. O1/o3, on the other hand, approach complex code by implicitly breaking down the problem. They may be better at planning architecture—for example, reasoning (in the background) about which components are needed before implementing them one by one. In competitions like Codeforces, where problems require multi-step reasoning to find the right algorithm, the o-models perform exceptionally well—o3 reached an Elo rating of 2727 (equivalent to a high-performing human competitive programmer) . This suggests that for algorithmically complex tasks (e.g., finding an efficient solution to a new problem), o1/o3 excel in problem-solving over GPT-4.5. GPT-4.5, however, has the advantage of a broader knowledge base; it might recognize that "this problem resembles the classic X problem" and reuse a known pattern, whereas o1 would derive it from first principles.

Debugging Capabilities

Debugging is an area where AI models can be very useful, as they can analyze lines of code and identify errors or suggest improvements. GPT-4o and GPT-4.5 are both very skilled at explaining what a given code snippet does and pointing out possible bugs. GPT-4.5 has been explicitly highlighted for its programming support—including debugging errors and suggesting code improvements . Its strength in understanding human instructions means that if a user says, "The code crashes on line X, what’s wrong?", GPT-4.5 can provide a relevant answer quickly. It also has a large training database to compare against (“this error type is similar to…”), which often helps.

OpenAI o1/o3 take debugging to the next level when dealing with truly difficult bugs. Because they can simulate reasoning around the code, they can internally test different hypotheses. For example, o1 may be better at following logic step by step through a program and identifying exactly where a variable gets an incorrect value. In a benchmark test (SWE-bench) that involved solving real GitHub issues, the o-models showed their strength: o3 solved ~72% of the bugs/tasks, vs. ~49% for o1 (GPT-4’s result on this test is not specified there, but GPT-4 likely scores around o1's level or slightly below). This suggests that o3 can detect and fix bugs at a level that surpasses what GPT-4o previously managed.

For small tasks (syntax errors, simple bugs), you may not notice much difference—all models can say, "You forgot a semicolon" or "Change this variable type." But for complex errors (logical errors requiring an understanding of the entire program flow or edge cases), the chain reasoning of o-models gives them an advantage. A practical approach could be:

1. Use GPT-4.5 to first generate a solution and understand error messages.
2. Have o3-mini-high review the code for deeper logical flaws.
   O3-mini-high can leverage its extra reasoning steps to detect subtle errors that GPT-4.5 might have missed.

A limitation of o1/o3 in debugging is that they tend to give concise answers unless otherwise requested—GPT-4.5 might provide a more pedagogical explanation, which can be valuable for understanding.

Ability to Optimize Code

When it comes to writing performance-optimized code or improving the efficiency of existing code, the models take different approaches:

• GPT-4.5, with its vast knowledge base, recognizes many common optimization patterns and "best practices." For example, it can suggest replacing a triple-nested loop with a more efficient algorithm if it's a well-known problem (such as swapping an O(n²) solution for Quicksort).
• Its primary strength is in recognizing common patterns. If an optimization involves a known approach (e.g., bitwise tricks in C++), GPT-4.5 has likely seen it and can immediately mention it, saving time.
• O1/o3, on the other hand, can genuinely calculate how to make something more efficient through reasoning. If you ask o1 to optimize an algorithm, it can analyze which part is the bottleneck and suggest a different approach.
• In competitive programming, o3 demonstrated its optimization capabilities by solving difficult coding challenges with high performance constraints .
• If an optimization requires an entirely new approach, o1/o3 may outperform GPT-4.5, as they don’t just rely on past training but instead break down the problem and solve it logically.

For example, assume you have a complex function and ask for optimization:

• GPT-4.5 may suggest micro-improvements (e.g., caching a value, loop unrolling).
• O1 may realize a completely different algorithm is needed.

If platform-specific tricks are involved (e.g., CPU cache optimization, writing vectorized assembly), it depends on the training data. GPT-4.5, with its extensive "world model," may have picked up niche optimization techniques from forums and documentation. O-models are not specifically trained for things like assembly, but they can still reason through it based on general logic.

Best Model Choices for Different Coding Fields

Web Development:

Web development involves both coding and creativity (e.g., UI/UX design, writing content), using HTML, CSS, JavaScript/TypeScript, React, Next.js, etc.

• GPT-4.5 is the best choice, as it excels in both code generation and text-based tasks (such as writing user-facing copy).
• It understands modern web frameworks and can generate well-structured HTML/CSS.
• GPT-4o is also good for web development and can generate full components and scripts. Compared to 4.5, GPT-4o's output may need more refinement, but it's a solid option for Plus users without Pro access.
• OpenAI o1/o3 are useful if there’s a complex backend logic problem (e.g., optimizing database queries, performance tuning).

Data Science & Analysis:

Data science requires both coding (Python, pandas, numpy, scikit-learn) and statistical reasoning.

• GPT-4.5 is ideal for both writing Python scripts and explaining insights from data.
• OpenAI o1 is better for complex mathematical reasoning, such as statistical theorem proofing or debugging a machine learning model.
• O3-mini-high is likely even stronger than o1 for verifying calculations.
• GPT-4o is a great alternative for Plus users, but double-check statistics-heavy outputs for hallucinations.

AI & Machine Learning Development:

Developing AI models involves both theoretical understanding and coding implementation.

• GPT-4.5 is the best model for writing ML code in PyTorch/TensorFlow.
• It explains concepts clearly (e.g., backpropagation, transformer architecture).
• OpenAI o1/o3 are better for deep reasoning (e.g., debugging a novel AI algorithm).

Performance-Critical & Low-Level Programming:

Low-level programming (e.g., embedded systems, real-time computing, C/C++ optimization) prioritizes efficiency and correctness over everything else.

• OpenAI o1 and o3-mini-high are the best choices when absolute performance matters.
• They reason about memory management, cache efficiency, and algorithmic complexity better than GPT-4 models.
• GPT-4.5 is excellent for code structure but may not always generate the most optimized solution unless prompted explicitly.
• GPT-4o is solid but requires manual fine-tuning for performance-critical tasks.

Final Recommendations for Coding

• For most programming tasks: GPT-4.5 is the best overall choice due to its high coding proficiency, broad language knowledge, and ability to explain code clearly.
• For competitive programming & algorithm-heavy coding: O3-mini-high is likely the best option (superior in reasoning and complex problem-solving).
• For debugging: GPT-4.5 is excellent for general debugging, while o1/o3 are better for hard-to-find logic errors.
• For performance optimization: O1 and O3-mini-high are best for deep analysis, but GPT-4.5 is better for known optimization tricks.
• For low-level programming: O1/o3 are superior for optimizing and debugging C/C++ code, while GPT-4.5 is better for general implementation and structuring.
• For web development: GPT-4.5 is best for both frontend/backend tasks, but GPT-4o is a great free-tier alternative.
• For data science & AI development: GPT-4.5 for general use, o1/o3 for mathematical proofs and deep debugging.

Overall Summary

• GPT-4.5 is the best general-purpose model—great at coding, writing, and broad tasks.
• O1 and O3-mini-high are the best models for complex reasoning—ideal for mathematics, algorithms, debugging, and optimizations.
• GPT-4o is a reliable mid-tier choice—not as strong as 4.5 but a great Plus-tier model for coding and web dev.
• O3-mini (free) is great for logical tasks but not as creative as GPT-4 models.
• Scheduled Tasks (GPT-4o with scheduling) is useful for automation but does not improve model intelligence.

Concise Summary of GPT-4o, GPT-4o with Scheduled Tasks, GPT-4.5, o1, o3-mini, and o3-mini-high

General Comparison

Best Models for Coding & Programming

Key Takeaways

• GPT-4.5 is the best all-around model for coding, AI/ML, writing, and general knowledge.
• GPT-4o is the best Plus-tier option, balancing speed, knowledge, and creativity.
• o1 & o3-mini-high are best for deep logic, debugging, and competitive coding.
• o3-mini (free) is the best free model for technical tasks but lacks creativity.
• GPT-4o + Tasks is useful for automation but doesn’t enhance coding abilities.