Overall Performance: OpenAI's latest releases, o3 and o4-mini, are among the company's most powerful AI models to date, known as the "o-series" models. These models are specially trained to engage in longer reasoning before providing answers, thereby improving the accuracy of solving complex problems. They are the first to integrate images into the reasoning process, enabling them to "think with images" by combining visual information with textual reasoning.
OpenAI o3 and o4-mini Model Research Report
Overall Performance: OpenAI's latest releases, o3 and o4-mini, are among the company's most powerful AI models to date, known as the "o-series" models. These models are specially trained to engage in longer reasoning before providing answers, thereby improving the accuracy of solving complex problems. They are the first to integrate images into the reasoning process, enabling them to "think with images" by combining visual information with textual reasoning. Additionally, both models can autonomously utilize various tools to complete multi-step tasks, possessing a certain degree of agentic capability.
- Reasoning and Accuracy: The o3 model excels in complex tasks such as code comprehension, mathematical derivation, and scientific Q&A, setting new records in several benchmark tests. For instance, on platforms like Codeforces, SWE-Bench, and the multimodal reasoning benchmark MMMU, o3 has achieved new SOTA (state-of-the-art) levels. External expert evaluations indicate that o3 reduces major errors by 20% compared to the previous generation model, OpenAI o1, especially in programming, business consulting, and creative ideation. Early testers praised o3 for its rigorous thinking and ability to propose and evaluate new hypotheses, particularly in biology, mathematics, and engineering.
- High Performance in Small Models: Although the OpenAI o4-mini is a smaller model, it has been optimized to achieve an excellent performance-cost balance in reasoning tasks. It performs exceptionally well in mathematics, coding, and visual analysis tasks, even achieving first place in the 2024 and 2025 AIME math competition benchmarks. With Python tool assistance, o4-mini scored as high as 99.5% in the 2025 AIME competition. Compared to the previous generation small model o3-mini, o4-mini also surpasses in non-STEM tasks and data science fields. Overall, at the same cost, o4-mini offers a better cost-performance ratio than the previous generation o3-mini, demonstrating that small models can achieve high-quality reasoning.
- Speed and Latency: Due to the "think before answering" mechanism, o3 and o4-mini are slightly slower in response speed compared to traditional models but can still provide detailed answers within one minute. The o3 model typically uses longer reasoning chains to solve complex problems, which may require higher computational latency; in contrast, o4-mini is optimized for speed and low latency, providing faster responses while maintaining reasonable reasoning capabilities. Tests show that the small model o3-mini returns answers in typical coding tasks in about 27 seconds, while the Mixture-of-Experts architecture DeepSeek R1 takes 1 minute and 45 seconds. This demonstrates that OpenAI's dense architecture small models have faster single inference speeds. Therefore, in applications requiring high throughput and timely responses, o4-mini can offer lower latency and higher per-second inference throughput.
- Memory and Context: OpenAI's reasoning series models support large context windows, allowing the model to "remember" and process more information before answering. According to official and third-party data, models like o3/o4-mini have context lengths reaching 100k to 200k tokens (e.g., o3-mini provides about 200,000 tokens of context window, with a maximum output length of 100,000). This means users can provide long documents and conversation histories, and the model can still fully utilize these contexts for reasoning. In terms of memory efficiency, OpenAI chose the traditional dense Transformer architecture (non-Mixture-of-Experts), meaning every token computation involves all parameters of the model, ensuring consistency and reliability of results. Although this design results in higher computational load and memory usage during large-scale reasoning (compared to some models using expert mixture architectures to reduce parameters per computation), it offers more stable performance and faster response speeds. Overall, o3 is a high-computation, high-precision model, while o4-mini sacrifices some parameter scale for higher efficiency and usability.
2. Applicable Scenarios and Target Users
OpenAI o3: As a flagship reasoning model, o3 is suitable for scenarios requiring in-depth analysis and complex reasoning. It excels at solving multi-step complex problems, such as inquiries requiring cross-domain knowledge integration, challenging problems requiring reasoning and calculation, or open-ended questions with no obvious answers. Specifically, o3 performs outstandingly in the following scenarios:
- Professional Research and Business Analysis: o3 excels as a "thinking partner" in business decision-making, management consulting, and financial analysis, providing in-depth analysis and insights based on large amounts of data and information. For researchers, o3 can help form and evaluate new hypotheses, offering inspiration in topics requiring creative thinking, such as biology and engineering. For example, a user asked o3 to analyze energy consumption trends, and the model autonomously searched for public data online, wrote code to plot prediction graphs, and explained the underlying reasons, providing a complete solution for complex consulting problems.
- Advanced Programming and Mathematical Reasoning: o3 has top-notch capabilities in code generation, debugging, and complex algorithm derivation. It outperforms previous models in programming competition challenges (Codeforces) and software engineering tasks (SWE-Bench). Therefore, professional developers can use o3 to assist in solving difficult programming problems, optimizing algorithms, or conducting code reviews. In mathematics, o3 excels at solving advanced mathematical problems and derivation proofs (such as complex polynomial problems), making it suitable for research-level mathematical assistants or educational problem-solving analysis.
- Visual Information Analysis: Supporting multimodal capabilities, o3 can understand and deeply analyze images, charts, and other visual content. It can handle applications such as medical image interpretation, scientific chart analysis, and design sketch understanding. For example, when a user uploads a research poster or engineering drawing, o3 can comprehend the graphical and textual information and provide comprehensive analysis conclusions. This makes o3 a powerful tool for handling mixed graphical and textual information in professional fields (such as business reports and research paper charts).
In summary, o3's target users are primarily high-end professional users and developers, including researchers, data scientists, senior engineers, and teams with complex ChatGPT needs. These users are willing to pay a higher cost for the strongest reasoning capabilities to solve high-difficulty, high-value problems.
OpenAI o4-mini: As a small, efficient model, o4-mini is positioned for cost-sensitive scenarios that still require strong reasoning capabilities. It is suitable for a broader range of applications and user groups, providing higher call frequency and lower latency while ensuring a certain depth of reasoning. Typical applicable scenarios include:
- Code Assistance and Daily Development: o4-mini performs excellently in code generation and debugging, especially adept at quickly producing syntactically correct code snippets. Developers can integrate it into IDE plugins, command-line assistants, and other tools to achieve real-time coding suggestions, unit test generation, vulnerability scanning, and repair functions. For medium-difficulty programming tasks, o4-mini is sufficient to provide high-quality answers, and its low cost allows for high-frequency calls, making it an ideal programming assistant.
- STEM Education and Q&A: o4-mini has strong capabilities in science, technology, engineering, and mathematics, providing step-by-step reasoning and detailed explanations. For example, in math problem-solving applications, o4-mini can answer a large number of student math questions with low latency and provide clear step-by-step explanations; on online education platforms, it serves as an on-demand science tutor AI. Its low latency and cost make it capable of serving a large number of user inquiries.
- Data Analysis and Business Process Automation: For daily enterprise data processing and analysis problems, o4-mini is an efficient choice. It can write scripts, parse data files, and provide analysis results, helping to automate report generation, log analysis, and other tasks. Being more economical than o3, enterprises can deploy o4-mini on a large scale to handle routine repetitive analysis tasks or customer service Q&A, performing reliably in high concurrency scenarios. For complex queries requiring batch processing, o4-mini offers a balanced solution between quality and cost.
- General Consumers and Developer Testing: Since o4-mini is partially open to free users (ChatGPT Free users can choose the "Think" mode to invoke o4-mini when asking questions), general users can also experience its reasoning capabilities to some extent. Curious individual users can use o4-mini to solve complex problems in daily life (such as complex itinerary planning, financial calculations, etc.) without subscribing to premium services. This also makes o4-mini an entry-level advanced reasoning tool, expanding its target user base from developers to the general public.
Overall, o4-mini is aimed at scenarios requiring high throughput or low-cost applications, with target users including developers, educators, small and medium-sized enterprises, and ordinary users with advanced functional needs. It offers reasoning capabilities close to flagship models at a lower cost, achieving a balance between quality and efficiency.
3. Technical Architecture Highlights (Mixture of Experts, Parameter Count, Training Data, etc.)
OpenAI o3 and o4-mini have numerous innovations and improvements in their technical architecture, supporting their high-performance reasoning capabilities:
- Deep Reasoning and Chain-of-Thought: The most notable feature of the o-series models is their encouragement of the model to "think one step further" in architecture and training. Unlike traditional large models that generate answers in one go, o3/o4-mini simulate a step-by-step reasoning process internally before providing the final answer. This architectural improvement is partially achieved through reinforcement learning (RL) : OpenAI provides numerous examples of step-by-step problem-solving, teaching the model to reason before answering. This strategy makes the model more reliable in logical reasoning and mathematical calculations, reducing errors in complex reasoning.
- Large-Scale Reinforcement Learning Training: The OpenAI team emphasizes that they have increased the scale of reinforcement learning training in the development of o3, resulting in a performance leap. Compared to previous models, o3 has increased RL training compute by an order of magnitude and allows the model to think for more steps during reasoning. Experiments show that even under the same latency and the same computational cost, o3 can achieve higher performance than o1; and if reasoning time is further extended, o3's performance continues to improve. This indicates that large-scale RL training and allowing the model to "think longer" can still bring significant returns. This architectural design promotes the trend of expanding reasoning capabilities with computational investment.
- Autonomous Tool Usage (Agentic AI): In the model architecture, o3 and o4-mini integrate the ability to use all built-in tools of ChatGPT for the first time. This means that model outputs are not limited to pure text answers but can dynamically generate action commands, invoking tools like browsers, code execution environments, and image generators. OpenAI teaches the model when and how to use tools through reinforcement learning. Technically, this is similar to adding a decision module to the model structure: the model can output special markers like "[search...]" or "[call Python]" in the reasoning chain, prompting the system to execute the corresponding tool and feed the result back to the model for further processing. This architecture endows o3/o4-mini with a certain Agent attribute, allowing them to autonomously solve complex tasks through chained tool calls. For example, the model can make up to 600 tool calls in succession to tackle a difficult problem—this capability reflects architectural support for iterative decision-making and environmental interaction, a highlight not found in traditional static models.
- Multimodal Integration: Architecturally, o3/o4-mini have added visual input processing modules compared to previous GPT models, enabling them to encode image pixel information into the model's semantic space. In the reasoning chain, the model not only "sees" images but can also perform logical operations on images (such as rotation, zooming, etc.) as part of its thinking. OpenAI's implementation may draw from GPT-4's visual branch, adding an image encoder to the model, allowing text and image features to participate in attention calculations together. Through multimodal training data (such as text-image pairs, image-based questions, etc.), the model learns to integrate visual understanding into the reasoning process. This architectural highlight enables o3/o4-mini to achieve industry-leading performance in visual reasoning tasks—they can interpret handwritten notes, analyze charts, and even understand blurry or inverted images. For example, in an official demonstration, a researcher provided a research poster image for o3 to analyze, and the model was able to read and understand the image content, then autonomously decide to search for related information online, combining details in the image to provide conclusions. This reflects the seamless integration of multimodal architecture and tool usage, an important innovation of this series of models.
- Safety Alignment Mechanism: OpenAI introduces a new "Deliberative Alignment" strategy in the o-series models to enhance the model's understanding and adherence to safety guidelines. Specifically, the training includes steps where the model reads a series of human-written safety norms and then reasons and judges whether user requests might violate these norms. Before answering, the model examines its thought process and draft answers against built-in safety standards to check for policy violations. This architectural adjustment makes the model more resistant to being induced into undesirable behavior: because the model's "inner monologue" actively checks for potential issues. For example, in internal testing, o3-mini's unsafe response rate is about 1.19%, significantly lower than a certain open-source competitor DeepSeek R1's 11.98%. This safety enhancement is achieved through a combination of architecture and training highlights, making o3/o4-mini more suitable for high-security-demand scenarios.
- Parameter Scale and Architectural Trade-offs: OpenAI has not disclosed the specific parameter count of o3 and o4-mini. However, it is speculated that the small model o3-mini has reached about 200 billion parameters (dense full parameters involved in reasoning). As an upgrade, the large model o3's parameter count may further increase to hundreds of billions or even trillions, ensuring stronger knowledge reserves and reasoning depth. Notably, OpenAI's o-series chose a non-Mixture-of-Experts (MoE) architecture (i.e., Transformer full parameters involved in computation), which differs from some competitors using MoE to expand parameters. For example, the open-source model DeepSeek R1 has a total parameter count of 671 billion, but only a small portion of parameters (about 37 billion per token) are activated through 16 expert routes. In contrast, o3-mini calls all 200 billion parameters for each token. This design makes model output more consistent and stable, avoiding uncertainties that expert networks might bring, while also incurring high computational costs. OpenAI enhances model optimization and engineering techniques to run such a large dense model within acceptable latency. For example, in its ChatGPT implementation, o3-mini is divided into "low/medium/high" reasoning intensity modes, allowing developers to balance speed and depth of thought. Overall, o3 adopts a "large model + strong reinforcement learning" technical route, while o4-mini continues the dense architecture, achieving the level of larger previous-generation models through training optimization with relatively small parameters.
- Training Data and Knowledge Scope: Although official details are not disclosed, it can be inferred that o3/o4-mini combine massive general text corpus, code corpus, and multimodal data in training. In terms of text, it likely includes internet encyclopedias, book articles, etc., equipping it with knowledge in a wide range of fields. In terms of code, it may incorporate open-source code repositories, programming competition solutions, etc., enhancing its programming capabilities (o3 is described as "incredibly good at programming"). In terms of multimodal, the model's training set may include image description pairs, image Q&A, etc. (similar to the previous GPT-4 Vision's training strategy), teaching the model visual understanding. This series of models also undergoes extensive human feedback reinforcement learning (RLHF) and contrastive learning fine-tuning, making them better at following instructions and providing detailed steps. Additionally, to enable tool usage, OpenAI likely constructs specialized multimodal dialogue data containing search, calculation, and other operations for fine-tuning. Combining these training data and methods, the technical architecture highlights of o3/o4-mini lie in the organic integration of ultra-large-scale pre-training, multimodal integration, reinforcement learning decision-making, and safety alignment, creating general AI models that lead in both reasoning complexity and practical capabilities.
4. Main Advantages and Limitations of the Models
Main Advantages:
- Outstanding Reasoning and Problem-Solving Ability: o3 and o4-mini excel at handling complex problems requiring layered reasoning, significantly reducing reasoning errors. Compared to previous models, they reduce error rates in complex coding, mathematics, and scientific reasoning tasks by about 20%. Thanks to longer thinking processes, these models set new standards for answer correctness and rigor, capable of producing more organized and verifiable answers.
- Multi-Tool Integration Enhances Practicality: Both models can automatically use various tools such as web search, code execution, and image generation to complete tasks. This enables them to acquire real-time information, perform precise calculations, and output results like charts. Compared to pure language models that tend to guess answers based on training knowledge, o3/o4-mini can verify through tools, greatly reducing hallucinations and inaccurate information. For example, they can autonomously search the web for the latest data or call Python to analyze files, making answers more reliable and evidence-based.
- Visual Understanding and Generation Capability: As OpenAI's first truly multimodal reasoning models, o3/o4-mini can deeply analyze images and integrate image information into reasoning. In visual Q&A and image reasoning tasks, they achieve industry-leading performance. Users can input photos, handwritten notes, statistical charts, etc., and the models can understand their content and provide detailed answers. This capability expands the application boundaries of AI, enabling it to solve problems that text models previously couldn't directly address. Additionally, the models can achieve image-to-image generation (calling image generators) through tools, enriching the output format.
- Strong Interactivity and More Human-Like Dialogue: Optimized for more natural and conversational response styles, these models are adept at following instructions and providing contextually relevant answers. They can reference conversation history to personalize responses, ensuring smoother context continuity in long dialogues. For developers, the new models also support function calls and structured outputs, allowing them to provide results in specified formats like JSON, facilitating program integration. These improvements enhance the models' practical value as conversational assistants and orchestratable AI tools.
- Enhanced Security and Reliability: o3/o4-mini apply new safety alignment mechanisms, actively reviewing their reasoning processes to avoid inappropriate outputs. As a result, they are more resistant to harmful instructions and less prone to misuse. In internal evaluations, the probability of generating unsafe content has significantly decreased. Additionally, since reasoning steps occur behind the scenes, users are not exposed to intermediate thoughts, reducing the risk of sensitive information leakage. Overall, these models are more secure and reliable than previous generations, making them more suitable for deployment in enterprise and high-demand scenarios.
- High Cost-Effectiveness (Especially o4-mini): Compared to earlier top models like GPT-4, OpenAI's new reasoning models have improved cost efficiency. o4-mini costs less than one-tenth of the GPT-4 model per million tokens, yet offers reasoning capabilities far superior to GPT-3.5 (even outperforming some GPT-4 derivatives in math and code). Additionally, OpenAI has increased usage limits for subscription users, such as raising the daily request limit for Plus users using small models from 50 to 150. For applications requiring frequent calls, o4-mini provides an economical and efficient option, making advanced reasoning AI more accessible.
Model Limitations:
- High Computational Cost and Usage Threshold (Especially o3): Despite its powerful performance, o3's computational overhead and price are also very high. Its API call cost is $40 per million output tokens (about 5 times that of the regular GPT-4.1 model), consuming a large amount of GPU compute during reasoning. This means ordinary users cannot use o3 in large quantities, primarily targeting developers and enterprises with the ability to pay. Additionally, o3 is only available to Plus/Pro and other paid users in ChatGPT, not accessible to free users. For budget-constrained projects, o3's high cost and its closed-source nature (only callable through OpenAI cloud services, not deployable locally) are significant limitations.
- Higher Response Latency: Due to multi-round reasoning and tool calls, the o-series models often experience longer computational chains before providing final responses. This results in longer average response times compared to models with immediate output (in official demonstrations, o3-high mode's first answer chunk delay can reach tens of seconds). Although OpenAI optimizes speed through medium/low reasoning intensity modes and parallel batch processing, o3 still appears slow in real-time interactive applications. For example, compared to some lightweight models that respond within seconds, o3 often takes nearly a minute to process complex requests. This high latency is not ideal for real-time dialogue and scenarios requiring instant feedback. Therefore, applications need to balance reasoning depth and response speed.
- Still Prone to Errors and Limitations: Despite improved accuracy, these models are not infallible. In extremely complex or out-of-distribution problems, they may still reason incorrectly or produce incorrect answers. For example, in some professional field challenges or common-sense judgment problems, the models may provide seemingly reasonable but actually incorrect conclusions. This "hallucination" phenomenon, although alleviated, has not been completely eliminated. Additionally, the models may sometimes overuse tools or take roundabout strategies, outputting unnecessarily lengthy steps. While their multi-tool capabilities are strong, if external tool information is incorrect, the models may also be misled. Overall, critical decisions still require human verification, and the reliability of model outputs cannot be guaranteed 100%.
- Limited Context Consistency: Despite the large context window, the models' efficiency in utilizing ultra-long contexts is limited. In practical applications, if the input content is extremely complex, the models may struggle to maintain complete consistency of focus, potentially missing some details or producing inconsistent answers. Additionally, large contexts increase response time and cost, and may not always be fully utilized. For example, in 256k length benchmark tests, extending the context improved o3's performance by less than 1% and o4-mini's by about 3%. This indicates diminishing returns in extremely long text scenarios, where the models may not deeply digest every part of the information.
- Lack of User Customizability: OpenAI's o3/o4-mini are currently closed models that cannot be fine-tuned by users. Developers cannot modify their architecture or train them with specific domain data like open-source models, only using OpenAI's default behavior. This limits the possibility of fine-tuning optimization in some professional fields. Additionally, OpenAI's usage policies also restrict the models' use in certain high-risk applications, and the models' output is subject to preset safety and style constraints, which can be a limitation for users seeking complete freedom.
- Competitive Environment Pressure: With companies like Google and Anthropic launching their new generation of reasoning models (such as the free Gemini 2.5 Pro, Claude 3.7, etc.), OpenAI o3/o4-mini face challenges in performance and cost. Some competitors offer larger contexts (e.g., Google's Gemini reportedly supports 1 million tokens) or lower prices, even open-source for free. Although these external factors are not inherent model defects, they may influence user choices regarding o3/o4-mini. In comparative evaluations, OpenAI models like o3-mini rank among the top in comprehensive intelligence indices, but their prices are significantly higher than some competitors. Therefore, for users who highly value cost or openness, OpenAI's closed-source paid model may not have an advantage.
5. Pricing and API Access
ChatGPT Access: OpenAI has integrated o3 and o4-mini into the ChatGPT service. For subscription users, ChatGPT Plus, Team, and Pro users can select o3, o4-mini, and the high reasoning mode o4-mini-high from the model menu starting from the release date. These new models replace the previous o1, o3-mini, and o3-mini-high options. For free users, OpenAI offers limited experience—selecting the "Think" mode when entering prompts triggers an o4-mini reasoning, allowing non-paying users to try the small model's advanced reasoning capabilities. Enterprise and education users gain access about a week after release, while the higher-performance o3-pro model for Pro users is expected to launch in a few weeks. Overall, paid subscription is the main way to fully use o3/o4-mini, with Plus version offering unlimited use of these new models for $20 per month (but with daily request limits), and Pro and Team users enjoying higher quotas and faster responses. In the ChatGPT interface, these models also support all features such as plugin tools, networking, and file uploads, fully leveraging their comprehensive AI system capabilities.
API Access: Developers can use the OpenAI API to access o3 and o4-mini models (based on the Chat Completion interface). OpenAI has opened these models for calls in the Chat Completions API, Assistants API, and Batch Asynchronous API. Model names can be specified in API requests as "openai-o3" or "openai-o4-mini." Note that initially, these models may only be open for testing to developer accounts with higher API usage levels (API usage levels 3-5), gradually opening to more users. Through the API, developers can integrate o3/o4-mini into their applications, enabling dialogue Q&A, text generation, tool calls, and more. The o-series models fully support OpenAI's newly introduced function calling, JSON structured output, and multi-message roles capabilities—this means developers can more easily have the models output results in specific formats or act as intelligent agents with tools. Due to the ultra-long context length, API usage should control prompt size and reasonably utilize streaming to obtain intermediate results. OpenAI also provides an Agents SDK to simplify tool-based process orchestration. In terms of stability and monitoring, developers should refer to the official system card and updated preparedness framework to understand model behavior boundaries.
Pricing Strategy: OpenAI has set on-demand API pricing for o3 and o4-mini, proportional to model complexity. The table below lists the officially announced API call fees:
ModelInput Fee (per million tokens)Output Fee (per million tokens)OpenAI o3$10.00$40.00OpenAI o4-mini$1.10$4.40
The above fees are token-based: for example, calling o3 to generate 1000 tokens of response costs about 0.04.Forfrequentlarge−scalecalls,OpenAIoffersbulkinterfaceswith∗∗500.04.Forfrequentlarge−scalecalls,OpenAIoffersbulkinterfaceswith∗∗502/$8 per million tokens), but its reasoning ability is also weaker than the o-series models. Therefore, users can choose the most cost-effective solution based on application needs.
Call Method: When using the API, developers need to specify the model name and provide corresponding parameters in the request. For example, in the ChatCompletion interface, the request JSON includes model: "openai-o3" or "openai-o4-mini", along with a messages dialogue list, etc. When the model needs to use tools, the function calling feature can be utilized: first, describe the available tools (functions) in the messages, and the model will return a function_call object in the response indicating the use of a tool, after which the developer executes the tool and feeds the result back to the model as a new message. OpenAI's Cookbook documentation provides detailed guidelines on how to drive tool-enabled models. Note that the new models have ultra-long contexts, and the API request token limit has increased, but overly long inputs will increase costs and latency, so they should be reasonably trimmed. Overall, through the API, users can flexibly embed the powerful reasoning capabilities of o3 and o4-mini into various systems, but need to consider their higher costs and potential rate limits (defaulting to a certain quota per minute, depending on account conditions).
6. Comparative Analysis of o3 and o4-mini
Although OpenAI o3 and o4-mini both belong to the reasoning series models and share many core features, there are still significant differences in positioning and performance. Below is a comparison of the two from multiple aspects:
Comparison DimensionOpenAI o3 (Flagship Reasoning Model)OpenAI o4-mini (Small Efficient Model)Model Scale and ArchitectureLarge dense model, parameter count undisclosed (estimated far exceeds 200 billion); uses Dense Transformer architecture, with all parameters involved in reasoning.Small dense model, parameter count lower than o3 (hundreds of billions); also dense architecture, no MoE. Achieves high performance with small scale through optimized training.Reasoning AbilityExtremely strong reasoning depth, excels in complex, ambiguous problems. Best at cross-domain comprehensive analysis and creative thinking, with the lowest error rate. Sets records in competitions and professional benchmarks like Codeforces.Outstanding performance for its parameter scale, especially in structured tasks like math and coding. While not as strong as o3 overall, it achieves top scores in some benchmarks (e.g., AIME math). Suitable for scenarios requiring some reasoning but with cost constraints.Multimodal and Tool UsageFully Supported: Can understand complex images, charts, and autonomously decide to use browsers, Python, drawing, image generation, and all tools. Can solve difficult problems with long tool call chains (hundreds of steps).Fully Supported: Also has image understanding and various tool call capabilities, similar to o3. Can call web search, code execution, and other tools to complete multi-step tasks, but slightly less capable of sustained reasoning in extremely complex chains compared to o3.Speed and LatencyRelatively slow: Allows longer internal thinking time for ultimate reasoning, with typical response latency in the tens of seconds. Suitable for asynchronous or non-real-time scenarios.Relatively fast: Optimized for low-latency reasoning, with typical responses taking only a few seconds to a dozen seconds. Supports real-time interactive scenarios, with better high-concurrency processing.Context MemoryExtremely large context window (reported up to 200k tokens); excels at utilizing long dialogue/document information for problem-solving, maintaining stronger coherence in multi-turn dialogues.Similarly large context window (close to o3); can handle long content, but limited by model capacity, with slightly lower detail capture and reasoning depth in extremely long inputs compared to o3.Applicable ScenariosHigh-difficulty, high-value tasks. For example, research breakthroughs, business decision support, advanced programming debugging, complex consulting, etc. Targeting professional users, with relatively low usage frequency but high-quality requirements for each response.Daily batch tasks and interactions. Such as online education Q&A, large-scale code assistance, business data processing, general user complex Q&A, etc. Targeting a broader user base, allowing high-frequency calls, supporting high-traffic applications.Usage ThresholdOnly available to Plus, Team, Pro, and other paid users; API call costs are expensive, $40 per 1M tokens output. Call frequency is limited (Plus has daily quota limits).Available to Plus/Pro users, with some features open for free user trials; API costs are low ($4.4/1M output tokens), supporting higher concurrency and call volume.Main AdvantagesOne of the most intelligent general AI models with the strongest reasoning; least errors, providing in-depth and detailed answers, capable of handling problems other models cannot solve; excellent multi-tool collaboration ability, solving complex open-ended tasks.Fast, cost-effective, easy to deploy on a large scale; already excellent for most common tasks, with high cost-effectiveness; allows more frequent interactions, suitable for applications requiring instant feedback; also has comprehensive multimodal and tool capabilities.Main LimitationsHigh cost and computational expense, not suitable for large-scale public use; slow response, not ideal for scenarios requiring millisecond-level real-time reactions; only cloud-based, not open-source or customizable.Slightly less absolute capability, may struggle with extremely complex or innovative challenges; may not be as robust as o3 in scenarios with strict precision requirements; still requires cloud-based calls (though low-cost, still requires internet).
(Note: Some parameter estimates and performance evaluations in the table are based on current public information and test results, and specific data may change with optimization updates.)
Comparative Analysis: Overall, OpenAI o3 and o4-mini represent two orientations of performance and efficiency. o3 is a flagship model pursuing ultimate intelligence and reasoning depth, suitable for those "there's only one answer but the process is very complex" problems. It trades higher computational costs for superior capabilities, shining in scenarios requiring the highest accuracy and completeness. However, this also means o3 is more like a high-priced consulting expert, suitable for occasional critical problem-solving rather than all-day work.
In contrast, o4-mini is like a diligent and efficient practical assistant. Although it doesn't reach o3's ceiling, it is "sufficient and efficient," capable of batch processing a large number of daily tasks. For many applications, o4-mini's responses are already of quite high quality, and its speed and cost advantages allow it to be used more widely. Especially in services requiring real-time response or massive concurrency, o4-mini can provide satisfactory results with lower investment.
The launch of both models reflects OpenAI's product strategy: before the arrival of the truly next-generation GPT-5, use o3 to refresh the capability ceiling and o4-mini to distribute excellent performance to more users. For developers and enterprises, choosing which model depends on specific needs: if the problem is extremely complex, each solution is very important, and higher costs can be borne, then o3 is the best choice; but if AI needs to be deployed on a large scale to handle tasks, pursuing the best cost-effectiveness per unit, then o4-mini is undoubtedly more suitable.
In summary, OpenAI o3 and o4-mini each have their focus, with the former representing today's leading AI reasoning level and the latter making this level more accessible. Reasonably combining the use of both can achieve the best performance and efficiency combination in different business scenarios. In the future, with the emergence of unified models like GPT-5, this balance of performance and efficiency may be further optimized. But for now, the release of o3 and o4-mini consolidates OpenAI's leading position in both high-end reasoning models and practical deployment models, providing users with a clear choice gradient.
