All ChatGPT models in 2025: complete report on GPT-4o, o3, o4-mini, 4.1 and their real capabilities on web and app

ChatGPT-5 is now rolling out and taking the place of previous models. Check our article here.

Here we share a complete overview of all currently supported ChatGPT models in the official ChatGPT web interface and mobile apps (iOS/Android) as of mid-2025 and beyond.

Deprecated models like the original GPT‑3.5 and GPT‑4 (legacy) are excluded. Each model section lists its official name, availability tier, context length, file upload/vision capabilities, memory support, key strengths, limitations, and best use cases.

GPT-4o (Omni General Model)

• Availability Tier: Included for Plus, Pro, Team, Enterprise, and Education plans. GPT‑4o replaced the legacy GPT‑4 as the default ChatGPT model for paid users in April 2025. (Free users generally do not have GPT‑4o access, aside from limited trials or usage quotas.)

• Token Context Limit: Up to 128,000 tokens (128k) context window. In practice, GPT‑4o can generate up to ~16k tokens in a single response. This large context allows very long conversations or document inputs without losing earlier context.

• File Upload Capability: Yes. GPT‑4o supports all advanced ChatGPT tools (formerly “Code Interpreter”), including file uploads and analysis. Users can attach files (PDF, DOCX, CSV, images, etc.) up to 512 MB each, with a max of 20 files per chat or project. Text/document files are additionally capped at ~2 million tokens per file (no limit on spreadsheet rows). Free users are limited to a few file uploads per day, while Plus users can upload up to 80 files per 3 hours (and higher limits for Pro/Team).

• Vision and Image Support: Yes. As an “omni” multimodal model, GPT‑4o accepts images in prompts and can understand/describe them. It can also generate images via the built-in DALL·E tool when asked (inserting images in chat) and supports voice input/output (speech recognition and spoken replies) for voice conversations. In sum, GPT‑4o can handle text, images, and audio modalities natively.

• Memory Support: Persistent memory is supported. Paid users can enable ChatGPT Memory (custom instructions + cross-session recall), so GPT‑4o will remember user preferences and recent conversations to personalize responses. It also has a long context to refer back within the same chat. Free-tier users have only basic short-term memory (within each session) and limited personalization.

• Key Strengths: GPT‑4o is a well-rounded, fast, and highly intelligent general-purpose model. It excels at a broad range of tasks including creative writing, summarization, translation, Q&A, and casual conversations. It has a large knowledge base and produces more natural, contextually aware answers than earlier GPT-4 versions. It handles images (e.g. analyzing a photo or chart) and audio queries, making it very versatile. GPT‑4o is also optimized for speed and cost-effectiveness – it’s faster and cheaper to use than the older GPT-4 models, while maintaining comparable quality. It generally provides reliable, fluent, and well-explained responses across a wide range of topics.

• Known Limitations or Tradeoffs: GPT‑4o does not perform step-by-step logical reasoning as deeply as the specialized “o-series” reasoning models (like o3). While it’s very capable on typical reasoning and coding tasks, it can make mistakes in complex math proofs or intricate multi-step problems that require meticulous logic – areas where the slower o-series might be more accurate. Its answers, although usually correct, may sometimes omit showing full reasoning or simplify tricky problems to maintain speed. Additionally, GPT‑4o’s responses are capped at around 4k tokens of output due to safety limits, so extremely long direct answers might get cut off or summarized. (The 128k context is primarily for reading large inputs, not for producing a novel-length single reply.) Overall, GPT‑4o favors a balance of speed and quality over exhaustive reasoning, and it may still hallucinate on obscure topics or when pushed beyond its knowledge cutoff (which was around late 2023).

• Best Suited Use Cases: GPT‑4o is the default choice for most users and everyday tasks. It’s ideal for content creation (articles, stories, emails), general Q&A, brainstorming, language translation, and non-specialist coding assistance. Its multimodal abilities make it great for analyzing images (e.g. explaining a diagram or chart) and handling spoken queries or voice chats. In summary, whenever you need a quick, accurate, and context-aware response across mixed media, GPT‑4o is usually the best pick. For example, you might use GPT‑4o to draft a blog post, get an explanation of a photo, debug a short code snippet, or converse about a news article – all in one model. It provides strong performance on both creative tasks and analytical tasks, so long as the problem doesn’t require the deep, methodical reasoning of the specialized o-series models.

GPT-4.5 (Large Knowledge Model – Research Preview)

• Availability Tier: Offered as a Research Preview model for Pro plan users starting in Feb 2025. Later in spring 2025 it became accessible to Plus, Team, Enterprise, and Edu users as well. (GPT-4.5 was never available to free tier.) Note: As of August 2025, OpenAI has begun phasing out GPT-4.5 – it remains available in ChatGPT for some Pro users, but it was deprecated in the API by mid-July 2025 in favor of GPT-4.1.

• Token Context Limit: 128,000 tokens (128k) context window, similar to GPT‑4o. GPT-4.5 can take in very large text inputs (hundreds of pages) and maintain context. (Knowledge cutoff for GPT-4.5 was updated to around October 2024, giving it more up-to-date information than GPT-4o.)

• File Upload Capability: Yes. GPT-4.5 supports all the same ChatGPT tools and file-handling features as GPT-4o. You can upload and analyze documents, images, spreadsheets, etc., within the same size limits (512 MB/file, 2M tokens text) and file count limits mentioned above. It can also write and execute Python code on data. Essentially, GPT-4.5 can do everything GPT-4o can with regard to files and tool usage, since it is integrated in the ChatGPT interface with full tool access.

• Vision and Image Support: Yes. GPT-4.5 accepts image inputs and can analyze or discuss them just like GPT-4o. It can also generate images through the DALL·E tool and even produce simple SVG graphics or diagrams in-line. It has access to the web (Browsing) for up-to-date info as well. However, GPT-4.5 does not output audio or video – it’s focused on text and image outputs (it relies on GPT-4o for voice conversations if needed).

• Memory Support: Yes. Since it’s available to paid plans, GPT-4.5 can leverage the persistent memory/custom instructions feature to remember user preferences and context across sessions. It also uses the standard ChatGPT conversation memory (or “temporary chat”) within its 128k context. There are no unique memory limitations noted for GPT-4.5 beyond those of other paid models.

• Key Strengths: GPT-4.5 is OpenAI’s largest model to date in the GPT series, with an emphasis on an “innately smarter” knowledge model rather than explicit step-by-step reasoning. It has an even broader knowledge base than GPT-4o (trained on more data), often producing very informed and detailed answers on a wide range of topics. Its responses tend to be articulate, creative, and nuanced, making it excellent for tasks like creative writing, complex problem solving, and giving well-rounded explanations. GPT-4.5 was noted for having a higher “EQ” – it can respond with more empathy, humor, and natural conversational tone than previous models. It also has improved pattern recognition and factual accuracy, meaning it is generally less prone to hallucination and can provide more reliable information off the bat. In summary, GPT-4.5 is great at general-purpose tasks: it can brainstorm ideas, draft high-quality content, translate and summarize text, help with coding, and so on, often with fewer mistakes and a more human-like style. It supports all multimodal and tool features, so it’s just as versatile as GPT-4o in handling images or files.

• Known Limitations or Tradeoffs: Notably, GPT-4.5 does not employ the new chain-of-thought “reasoning” paradigm that the o-series uses. This means that while it’s very smart and knowledgeable, it does not explicitly think through problems step-by-step internally, which can be a drawback for highly complex logical or mathematical questions. In those domains, GPT-4.5 might give an answer quickly but without showing or performing a rigorous reasoning chain, potentially making subtle mistakes that a reasoning-focused model might catch. It is also resource-intensive and relatively slow compared to smaller models – some users find GPT-4.5 responses slower due to its sheer size. Because of its computational cost, usage of GPT-4.5 (especially on Pro accounts) has been rate-limited (e.g. some Pro users only got ~10 messages per week with 4.5) and OpenAI decided to retire it from the API to free up capacity. Another limitation is that GPT-4.5, unlike GPT-4o, cannot output audio or video despite being multimodal in input. It will not speak with you in voice or generate audio clips; its multimodality is mainly text and vision. Finally, since GPT-4.5 was a research preview, it may have occasional quirks and wasn’t fine-tuned as extensively for long-term chat consistency. As of August 2025, most of GPT-4.5’s advantages have been merged into newer models (GPT-4.1 and GPT-4o improvements), making GPT-4.5 somewhat transitional.

• Best Suited Use Cases: GPT-4.5 shines in scenarios where you want a highly knowledgeable and creative AI partner – for example, drafting long-form content, solving broad knowledge questions, or providing insightful analysis on open-ended problems. It’s excellent for writing assistance (stories, essays, marketing copy) given its nuanced style and reduced hallucinations. It’s also very capable at programming help and debugging, offering detailed code suggestions and explaining them (though GPT-4.1 might be even better specifically for coding). If you have a complex practical problem or planning task, GPT-4.5 can draw on its large knowledge and produce a thoughtful answer (e.g. helping plan a project, analyze a business scenario, or give advice on a complex issue). However, because GPT-4.5 is being sunset and requires a higher-tier plan, many of its use cases can now be served by GPT-4.1 or GPT-4o3-pro. In summary, use GPT-4.5 when you need the richest general knowledge and writing quality from ChatGPT and have access to it – it effectively serves as a bridge between the fast GPT-4o and the advanced reasoning models.

GPT-4.1 (Coding & Instruction Specialist, Long-Context GPT Model)

• Availability Tier: Available to all Plus, Pro, Team users (introduced in May 2025). Enterprise/Edu plans also have access (rolled out shortly after Plus). GPT-4.1 appears under the “More models” menu in ChatGPT. (It is an additional model – GPT-4.1 did not replace GPT-4o; rather it complements it for specific tasks.) All paid users have the same message rate limits for GPT-4.1 as they do for GPT-4o. (Free users do not have GPT-4.1.)

• Token Context Limit: Supports an extremely large 1,000,000-token context window (1 million tokens) – a huge jump from the 128k of GPT-4o. This million-token context is a theoretical limit (~800k words of text), enabling GPT-4.1 to ingest massive amounts of code or text (for example, entire codebases or lengthy documents) without needing chunking. In practice, the ChatGPT UI may cap the usable context lower (to maintain performance), but the model is designed for dramatically expanded context. Notably, OpenAI trained GPT-4.1 to use the full long context reliably – it can pick out relevant details even deep into a long input and ignore irrelevant distractors. The max output length for GPT-4.1 is also increased (it can output up to ~32k tokens in a response when allowed), which is double GPT-4o’s output cap.

• File Upload Capability: Yes. GPT-4.1 supports all ChatGPT file tools (functionally the same as GPT-4o in this regard). You can upload code files, documents, data sets, etc., and GPT-4.1 will analyze or modify them. Given its specialization, GPT-4.1 is especially adept at working with large code files or multiple files at once. The file size and count limits remain (512 MB per file, up to 20 files per project for Plus users, higher for Pro) – but GPT-4.1’s million-token context means it can actually process very large files or many files collectively without losing context. It also supports browsing the web and running Python code as needed, just like other advanced models.

• Vision and Image Support: Yes (input only). GPT-4.1 is a multimodal model in terms of input – it can accept and reason about images (you can show it a diagram, screenshot, or photo and ask questions). However, GPT-4.1 outputs text only. It does not generate images or audio/voice on its own. So, while it can interpret a picture you upload (e.g. reading a graph or identifying a pattern in an image) similar to GPT-4o, it won’t produce spoken answers (no voice output) and it doesn’t call the DALL·E tool for image creation. (For image generation or voice replies, you’d use GPT-4o or GPT-4.5). Aside from that, GPT-4.1 fully supports vision input and will describe or analyze images in detail.

• Memory Support: Yes. GPT-4.1 can utilize ChatGPT’s persistent memory feature (custom instructions and cross-chat recall) for Plus/Pro users. There’s no model-specific memory limitation; it can reference prior chats via the memory system and of course remember an enormous amount within a single conversation thanks to the 1M token window. As with other models, free users have no persistent memory to leverage here.

• Key Strengths: GPT-4.1 is a specialized GPT-4-series model excelling in coding, structured tasks, and precise instruction-following. It was introduced as a response to developer demand, and it surpasses GPT-4o in various coding-related tasks. Key strengths include:

  • Programming and Debugging: GPT-4.1 is tuned to be an exceptional coding assistant. It can generate correct, well-structured code with fewer errors, and it handles things like following diff/patch instructions very reliably. In benchmarks, GPT-4.1 solved significantly more software engineering problems end-to-end than GPT-4o (e.g. 54.6% vs 33.2% on a code repository challenge). It’s also adept at reading and editing large codebases, thanks to the long context. Developers report GPT-4.1 produces more useful and concise code reviews and suggestions compared to other models.

  • Precise Instruction Following: GPT-4.1 was trained to follow user instructions and formatting requirements much more rigorously. It excels at tasks that require adhering to a specific format (JSON, XML, etc.), following multi-step instructions in order, and handling “don’t do X” constraints. Users find that GPT-4.1 is less likely to go off-script or ignore instructions, which means less time fixing its output format.

  • Speed and Efficiency: Despite its power, GPT-4.1 operates with roughly the same latency as GPT-4o for most queries. OpenAI managed to make it quite efficient given its capabilities, so Plus users can get better performance without slowdowns. It’s also cheaper in the API (and presumably less message-restricted in ChatGPT) than the giant GPT-4.5, making it a practical choice for daily use.

  • Long-Context Comprehension: With the 1M token window, GPT-4.1 can ingest and synthesize very large texts or sets of documents. It’s skilled at long-context comprehension – e.g., analyzing entire books or extensive logs and drawing conclusions without missing relevant details. This makes it valuable for tasks like reviewing lengthy legal documents or consolidating information from multiple sources at once.

  • Reliability: GPT-4.1 generally hallucinates less than older GPT models on factual queries (it was trained with feedback to improve correctness). And although GPT-4.5 slightly edged it out in certain creative writing aspects, GPT-4.1 strikes a strong balance of intelligence and reliability, enough that OpenAI decided it could replace GPT-4.5 going forward.

• Known Limitations or Tradeoffs: Because GPT-4.1 is optimized for coding and instruction clarity, it may be less “creative” or empathetic in open-ended conversations compared to GPT-4.5. Users have noted that GPT-4.5 sometimes gave more nuanced or emotionally aware responses (e.g. for writing fiction or discussing feelings), whereas GPT-4.1 can be a bit more straightforward and focused on correctness. GPT-4.1 is also not explicitly a reasoning-chain model – it doesn’t internally do the multi-step thought simulation that the o-series does – so for extremely complex logical puzzles or math proofs, OpenAI’s o3 model can still outperform it in accuracy. Essentially, GPT-4.1’s reasoning is improved from GPT-4o, but it’s not as slow and methodical as o3, which means on the hardest problems it might still make logical missteps. Another limitation: as mentioned, GPT-4.1 cannot generate images or audio. If you ask it to draw a picture or speak in a voice, it won’t – those features are reserved for the other models. Finally, while GPT-4.1 supports 1M token context, ChatGPT’s interface may not yet allow full exploitation of that (there might be practical limits in the UI such as 128k or 256k tokens per message to avoid timeouts). So, there’s a difference between the model’s theoretical capacity and what you can use in one go interactively. In any case, handling extremely long inputs can be slow and potentially expensive, so it’s to be used judiciously.

• Best Suited Use Cases: GPT-4.1 is best when you need a smart, detail-oriented assistant for coding or technical tasks. Great use cases include: writing and debugging code (it’s particularly good for web development tasks, following specs to create code, and fixing bugs in code snippets); working with large texts or datasets (since it can take in huge contexts, you can feed it an entire log file or multiple documents and ask for analysis); complex multi-step instructions (if you need the model to output in a specific format or follow a procedural script, GPT-4.1 will stick to the requirements better than others). It’s also an excellent alternative when GPT-4o or GPT-4.5 feels too “loose” in following instructions – GPT-4.1 will be more literal and exact, which is valuable for things like structured outputs (JSON/XML), mathematical calculations, or API function calling (it was designed with the new function calling capabilities in mind). For everyday use, many Plus users might choose GPT-4.1 as a faster, more cost-effective “power tool” when they specifically need accuracy in coding or formatting. On the other hand, for casual creative writing or simple conversation, GPT-4o might suffice. In short: use GPT-4.1 for coding, data, and any task where correctness and context-length are paramount, or as a robust general model if you prefer its style. It’s a powerhouse for engineers, data analysts, and power users who leverage ChatGPT in more technical workflows.

GPT-4.1 mini (Efficient Small Model – Successor to GPT-4o mini)

• Availability Tier: Plus, Pro, Team, Enterprise, Edu – available to all paid users (added May 14, 2025). GPT-4.1 mini replaced the older GPT-4o mini in the model picker for paid plans. Free users indirectly benefit: GPT-4.1 mini serves as the fallback model for free tier users once they exhaust any limited GPT-4o access. (In essence, the free tier’s default model is now this GPT-4.1 mini, since GPT-3.5 is deprecated. Free users might start on GPT-4o for a few messages if allowed, but then drop to 4.1 mini for continued chatting.)

• Token Context Limit: 1,000,000 tokens (same 1M context as the full GPT-4.1). Despite being a “mini” model in terms of size, GPT-4.1 mini maintains the huge context window, enabling long conversations or large file analysis comparable to its larger counterpart. This is a major improvement over the previous GPT-4o mini, which had 128k context – now even the small model can handle up to ~8 novels worth of text input.

• File Upload Capability: Yes. GPT-4.1 mini has access to the full toolset (web browsing, code execution, file uploads, etc.) just like other models. In fact, one of its upgrades over the older mini is broader tool use – it can utilize advanced tools (vision, Python, etc.) that were previously limited to big models. So you can upload multiple files and have GPT-4.1 mini analyze or compare them. The same file limits (512 MB, 20 files, etc.) apply. Because GPT-4.1 mini is lighter on compute, Plus and Team users are allowed higher message volumes with it (e.g. Plus users: ~300 messages/day on mini vs ~100 on mini-high or heavy models), making it great for high-throughput file analysis tasks.

• Vision and Image Support: Yes. GPT-4.1 mini can accept and analyze images as input, and it supports the DALL·E image generation tool as well. This is an upgrade from older small models – for instance, GPT-4o mini did not natively handle vision (calls were internally rerouted to GPT-4o). Now GPT-4.1 mini directly understands images (e.g. you can ask it to describe a photo or solve a problem from a diagram). It can also generate images through the integrated DALL·E plugin. Additionally, GPT-4.1 mini supports voice input on mobile and can follow voice commands, though like the full GPT-4.1, it outputs text (or uses the TTS voice of the app for spoken replies). Essentially, GPT-4.1 mini is a fully multimodal small model.

• Memory Support: Yes (persistent). On paid accounts, GPT-4.1 mini uses the shared Memory feature – it will remember custom instructions and can incorporate prior chat history across sessions (if enabled). Within a conversation, it has the same enormous memory (1M tokens) to draw on. For free users, this model is what they interact with by default, but free users only have ephemeral session memory (with some recent-history referencing rolled out as a new feature).

• Key Strengths: GPT-4.1 mini is a fast, capable, and highly efficient model that delivers much of the power of GPT-4.1 at a lower cost and latency. Its major strengths include:

  • Speed and Throughput: As a smaller model, 4.1 mini responds quickly – noticeably faster than the big GPT-4o or o-series models – making it ideal for interactive chatting or rapid-fire Q&A. OpenAI also allows significantly more requests with 4.1 mini (less restrictive rate limits), which is useful for heavy users or batch tasks.

  • Strong General Performance: Despite its size, 4.1 mini’s training inherits many improvements from GPT-4.1. It matches or exceeds GPT-4o’s performance on many benchmarks. For example, it saw marked improvements in instruction following, coding ability, and reasoning compared to the older GPT-4o mini. External tests found it often as good as GPT-4o in answering questions correctly, while being faster.

  • Large Context + Tools: Unusual for a “small” model, it handles the same 1M-token context and the full range of tools. This means 4.1 mini can tackle tasks like analyzing a lengthy document or using browsing+Python on data, which previously required big models. It’s an excellent all-purpose model for high-volume tasks – for instance, scanning through many documents quickly, or handling lots of user queries in a support chatbot scenario.

  • Cost-Effective Reasoning: While not as intelligent as o3, GPT-4.1 mini does have improved reasoning algorithms (it “thinks” more effectively than the older mini). It gives solid performance on math, coding, and logical questions for a fraction of the compute cost, making it a good choice for those who need reasoning but have budget or speed constraints. In fact, experts rated its output quality as more useful and well-founded than its predecessors, thanks in part to it being able to cite sources with the browsing tool.

• Known Limitations or Tradeoffs: As a lighter model, GPT-4.1 mini is not quite as powerful as the full GPT-4.1 or GPT-4o. On very complex tasks (especially those requiring deep reasoning or extensive creativity), it may occasionally lag behind the flagship models. It tries to balance reasoning ability with speed, so there’s a small tradeoff: for example, its answers might be slightly less in-depth or less nuanced on difficult prompts compared to GPT-4o or GPT-4.5. Also, while it can handle a 1M token context, pushing a smaller model to attend to that much information might yield less accurate results than a larger model doing the same – it’s an impressive capability but not magic. Users have to be mindful that feeding huge context to the mini model can still overwhelm its attention somewhat (e.g., it might miss a detail buried in 800k tokens, whereas the full model might catch it more often). Another limitation: like GPT-4.1, the mini variant does not generate audio or do voice output on its own – it will stick to text answers (the voice feature on mobile is handled by the app’s TTS). In summary, GPT-4.1 mini trades a bit of raw strength for speed and efficiency. It’s more than sufficient for most everyday tasks, but for the absolute toughest problems or highest fidelity creative work, one of the larger models might still have an edge.

• Best Suited Use Cases: GPT-4.1 mini is becoming the go-to model for many daily ChatGPT needs, thanks to its speed and competence. Ideal use cases include: casual Q&A and conversation (it’s fast and smart enough to handle almost any general question clearly), light coding tasks (like writing simple scripts, explaining code, or debugging small issues – it’s nearly as good as big 4.1 for many programming queries, and quicker), data analysis on moderate data (you can upload a few files or a spreadsheet and get rapid insights), and high-volume workloads (such as customer support bots or brainstorming sessions where you need lots of back-and-forth). For free users, GPT-4.1 mini essentially is ChatGPT now – answering everything from homework help to writing prompts once the limited GPT-4o uses are exhausted. For Plus/Pro users, you’d choose GPT-4.1 mini when you want faster responses but still with GPT-4-level quality. For instance, if you’re iterating on content drafts or doing quick analyses of texts, the mini will be very efficient. It’s also well-suited for building AI assistants via the API or custom GPTs, where cost and speed matter – you get strong performance without breaking the bank. Overall, GPT-4.1 mini is best for everyday use and scalable tasks, whereas you might switch to a larger model only when you need that extra bit of reasoning muscle.

OpenAI o3 (Advanced Reasoning Model – “Thinker”)

• Availability Tier: Accessible to Plus, Pro, Team subscribers as of April 2025 (appears as “OpenAI o3” in the model picker). Enterprise and Education plan users received access about a week later. Free users do not have direct access to o3, given its high compute cost and strict usage caps. (On free tier, the “Think” mode uses the smaller o4-mini model instead.)

• Token Context Limit: 200,000 tokens (200k) combined context. This means o3 can process extremely lengthy inputs (roughly 150k words) while keeping relevant details in mind. Max output is around 100k tokens in a single response (though in practice UIs limit how much it will actually print out). The 200k window is larger than GPT-4o’s 128k, enabling o3 to handle very detailed multi-part problems or large data without dropping context.

• File Upload Capability: Yes. O3 has full tool usage in ChatGPT – it can search the web, run Python code, analyze uploaded files, interpret images, and even generate images via DALL·E. This was a first for OpenAI’s reasoning models: unlike older ones, o3 is agentic with tools – it is trained to decide when to use the browser, when to use the file analysis, when to use Python, etc., to solve a complex query. As a user, you can upload files (within the standard size limits) and o3 will deeply analyze them or combine them with its reasoning. O3’s high context means it can take in very large files or multiple files at once and reason across them. Keep in mind that o3 usage is heavily rate-limited – for instance, Enterprise users get ~100 messages per week on o3 (Plus/Pro users have similarly tight caps), so you might reserve file analyses for important cases.

• Vision and Image Support: Yes. OpenAI o3 is a multimodal reasoning model – it can understand images and perform complex visual reasoning. It excels at analyzing charts, graphs, diagrams, and photographs, extracting insights from them. In fact, vision is one of o3’s strengths; OpenAI noted it performs especially well on tasks like interpreting scientific charts or solving visual problems. O3 will take an image input and break down what it shows, often in a step-by-step manner. It also supports image output generation (via the same DALL·E tool) within ChatGPT. For example, o3 can determine that a query would benefit from creating an image and use the tool to do so if asked. (In evaluations, o3 was able to decide to generate an image or use browsing when appropriate, as part of its agentic toolkit.)

• Memory Support: Yes. O3 supports the persistent Memory feature in ChatGPT for users who have it enabled. Moreover, o3 is explicitly designed to utilize conversation context effectively – it will reference earlier messages (even across long dialogues) to avoid repeating mistakes and to personalize answers. The model was noted to “reference memory and past conversations to make responses more personalized and relevant” more naturally than previous models. One current quirk: upon initial launch, temporary chats (the feature to start an incognito session that isn’t saved) were disabled for o3 due to a technical issue. This implies that using o3 will always log the conversation to history for now. But aside from that, memory works normally.

• Key Strengths: OpenAI o3 is the most powerful reasoning model in ChatGPT’s lineup. It is designed to “think longer” and tackle complex, multi-step problems that require deep analysis. Key strengths include:

  • Rigorous Logical & Mathematical Reasoning: O3 is exceptional at solving complex math problems, logical puzzles, and reasoning-intensive tasks. It uses a chain-of-thought approach internally, essentially working through problems step by step like a human tutor might. This makes it more accurate on tricky questions in domains like algebra, calculus, formal logic, etc. External evaluations found o3 makes ~20% fewer major errors than its predecessor (o1) on difficult real-world tasks. It shines in areas like programming (algorithmic challenges, debugging tricky code), scientific reasoning, and engineering problems, often outperforming GPT-4o on these types of logic-heavy queries.

  • Tool-augmented Intelligence: O3 was the first model that can agentically use every tool within ChatGPT. It doesn’t just rely on its internal knowledge; it will actively search the web for latest information, run Python code to do calculations or data analysis, load files you give it to extract facts, etc., on its own during a single answer. This leads to very thorough and up-to-date responses. For instance, if you ask a complex data science question, o3 might read an attached CSV with Python, produce some statistics, and then give a step-by-step interpretation. This capability to combine tools in one reasoning chain is a game-changer for solving multifaceted problems.

  • Multimodal Reasoning: O3 is particularly strong at tasks that involve visual data or multi-modal inputs. Early testers noted its analytical rigor in interpreting images, charts, or diagrams in fields like biology and engineering. It can, for example, examine a graph from a research paper and provide detailed observations and conclusions. No other model is as adept at this level of visual reasoning integrated with text.

  • Accuracy and Reliability: When you absolutely need a correct and well-justified answer, o3 is the model to turn to. It was consistently rated higher in clarity, comprehensiveness, and factual accuracy compared to GPT-4o in expert reviews. It tends to explain its reasoning in a very transparent way (it “teaches like a human” by showing steps, as one commentator put it). This makes its answers easier to trust for high-stakes queries.

  • Complex Problem Solving: O3 is ideal for multi-faceted queries whose answers aren’t immediately obvious. It can break a complex question into sub-problems, solve each part (using tools if needed), and synthesize a solution. This agent-like behavior means it’s a step toward an AI that can independently execute tasks on your behalf rather than just respond passively.

• Known Limitations or Tradeoffs: The main tradeoff with o3 is speed. O3 is deliberately designed to take longer per query – it “thinks” more steps internally before responding. As a result, responses can be significantly slower than GPT-4o or GPT-4.1, sometimes on the order of tens of seconds to a minute for a complex answer (depending on how many tools it uses). OpenAI recommends using o3 for challenging questions where reliability matters more than speed. If you need a quick answer, o3 might feel sluggish. There are also strict usage limits due to the computational cost: Plus users might only get a limited number of o3 messages per day or week (and Pro/Enterprise have defined quotas). This means you have to “spend” your o3 queries wisely on the toughest problems. Another limitation is that while o3 is generally conversational, it may come off as more “formal” or dry in style compared to GPT-4o. It focuses on analysis over chit-chat, so it’s less suited for casual creative writing or roleplay. OpenAI is working to make the o-series more conversational, but the focus is clearly on task-solving ability. One more note: at launch, certain features were not supported in o3 – for instance, Canvas mode (the scratchpad for rendering code or markdown) was initially not enabled for o3, and it also might not engage in voice conversations as smoothly as GPT-4o (though it can interpret audio input, the “Advanced Voice” output might default to GPT-4o’s voice model). These are minor, temporary issues as the platform integration catches up. In summary, the tradeoff is speed and availability: you get the most advanced reasoning, but you must be patient and mindful of usage quotas.

• Best Suited Use Cases: OpenAI o3 should be your choice when you have a difficult problem that requires meticulous reasoning or analysis. Ideal use cases:

  • Complex Math and Science Questions: If you’re tackling a hard math problem (say an Olympiad question or engineering calculation) or analyzing a scientific scenario, o3 will carefully work through the steps and likely arrive at a correct solution with justification.

  • Advanced Coding and Debugging: For debugging an especially tricky piece of code or solving a programming challenge that stumps GPT-4o, o3’s step-by-step approach can find the bug or figure out the algorithm more reliably. It’s like having a senior engineer who systematically tests and reasons about the code.

  • Data Analysis and Research Synthesis: With its tool use, o3 is superb for data-heavy queries. For example, you can upload a dataset and ask o3 to explore it – it will run Python analyses and give you insights. Or you can ask it to do a literature search on a topic – it will browse sources, maybe fetch some references, and then provide a synthesized answer with citations.

  • Consulting and Planning: O3 excels in domains like business strategy, legal analysis, or academic research where a question might not have a single obvious answer and requires considering multiple angles. Early users noted it was great for generating and critiquing novel hypotheses in fields like biology and finance. It’s the model you’d use to get a thorough, well-reasoned report or plan.

  • When Accuracy is Paramount: If you have a query where getting the right answer is critical (e.g., checking a complex calculation, verifying a logical argument, or ensuring compliance with rules), o3’s reliable reasoning reduces the risk of errors. It’s the closest thing to an “expert mode” in ChatGPT.

  In summary, use OpenAI o3 for “hard mode” problems – it’s slower, but you’ll get an answer that’s been thought through from all sides. For straightforward or time-sensitive queries, one of the GPT series models may be more convenient, but for the toughest nuts to crack, o3 is unmatched in capability.

OpenAI o3-pro (Ultra-Reliable Reasoning Model)

• Availability Tier: Exclusive to ChatGPT Pro and Team plans as of June 10, 2025. (Enterprise/Edu gained access a week later.) It replaced the older o1-pro model. Plus subscribers do not have o3-pro by default – it’s a perk of higher tiers (Pro is the premium personal plan above Plus). Essentially, o3-pro is a special model for users who need the absolute best reasoning quality and have a Pro/Team subscription.

• Token Context Limit: 200k tokens (same as standard o3, since o3-pro is a variant of the o3 model). It doesn’t increase context length; instead, it changes how the model allocates its “reasoning effort.”

• File Upload Capability: Yes. O3-pro inherits full tool access from o3, meaning it can also browse, use Python, handle file uploads, and analyze images just like o3. If anything, o3-pro will be even more inclined to use tools when appropriate, because it’s optimized to leave no stone unturned in answering. Important differences: at launch, certain tools were temporarily not supported in o3-pro – notably, image generation (DALL·E) was disabled in o3-pro, and the Canvas feature was not available. So, while o3-pro can analyze images you give it, it cannot create new images itself (you’d need to use GPT-4o or o3 for generation). Similarly, if you try to open the scratchpad Canvas with o3-pro, it won’t function. These limitations may be lifted later as they integrate features, but as of Aug 2025 they are known restrictions.

• Vision and Image Support: Yes (analysis only). Like o3, the o3-pro model can accept and analyze visual inputs — it will meticulously describe images or diagrams and reason about visual information. This is extremely useful for, say, interpreting a complex scientific figure or debugging a circuit diagram with maximum accuracy. However, as noted, o3-pro currently does not support generating images. If you ask o3-pro to “create an image,” it will likely refuse or produce a textual description instead of invoking DALL·E.

• Memory Support: Yes (with a caveat). O3-pro supports persistent memory and will use conversation history or custom instructions just like other models. One caveat: temporary chats are disabled for o3-pro at the moment. That means you cannot use o3-pro in an ephemeral/incognito mode; any chat with o3-pro will be saved and can leverage memory (this was done to resolve a technical issue). Otherwise, o3-pro will remember prior context in the conversation (up to 200k tokens) and use it. If anything, o3-pro tends to use the provided context even more exhaustively, as it’s tuned for thoroughness.

• Key Strengths: O3-pro is essentially OpenAI o3 with an extra gear for careful reasoning. It’s the most powerful model available to the public as of mid-2025 in terms of raw problem-solving ability. Its key strength is extreme reliability and depth of explanation:

  • Enhanced “Thinking Time”: O3-pro is configured to think even more steps and for a longer duration before finalizing an answer. This means it explores more possible solution paths internally, catches more edge cases, and produces answers that are more fully reasoned and checked. For example, on a difficult math word problem, o3-pro might internally try multiple solution approaches or double-check its calculations, whereas o3 might stop after one approach. This leads to answers that come with very clear, human-like explanations of why the answer is correct. In comparative tests, users found that “only one teaches like a human” – referring to o3-pro’s ability to explain its reasoning superiorly.

  • Maximum Accuracy in Key Domains: O3-pro particularly excels in domains like science, math, engineering, coding, education, and writing help. Reviewers consistently preferred o3-pro’s output over o3’s in every tested category. For instance, in coding, o3-pro might not only fix a bug but also explain subtle aspects of the code’s logic in detail. In educational questions, it provides very comprehensive yet clear explanations, making it an excellent tutor model.

  • Clarity and Instruction-Following: Another noted strength – o3-pro’s answers are even more clear, comprehensive, and on-point in following instructions. It tends not to skip any part of a multi-part question; it addresses each part methodically. If you give it a complicated instruction with multiple constraints, o3-pro will carefully adhere to all of them. This makes it extremely reliable for tasks where strict compliance is needed.

  • Tool Utilization: O3-pro uses the toolset in a very effective manner. Since it’s not as constrained by speed, it will, for example, perform multiple web searches if needed and cross-verify information. It’s willing to run longer Python analyses on provided data. Essentially it leverages ChatGPT’s tools to maximize the quality of the answer, even if it takes a bit longer.

  • Use of Chain-of-Thought: O3-pro really leans into its chain-of-thought training. Users often observe that it will produce a more detailed reasoning process in its final answer (when appropriate) than the standard o3. It “shows its work” like a teacher who double-checks everything.

• Known Limitations or Tradeoffs: Speed is the biggest tradeoff. O3-pro is slower than even o3 – because it is literally taking more time to think. It’s not unusual for o3-pro to take a minute or two to respond for a complex query, whereas GPT-4o might answer in 10 seconds. This is by design (to maximize reliability), but it means you wouldn’t use o3-pro for quick casual questions. Another limitation is accessibility: o3-pro is available only on the highest tiers and has very stringent usage caps. Pro and Team users get it, but even they might have a relatively low weekly message allowance for o3-pro (for example, if Pro had near-unlimited GPT-4o, it might still limit o3-pro to, say, 50 messages a week – hypothetical numbers). Enterprise may allow more usage but still limited. So o3-pro is a scarce resource. Additionally, at launch it lacks a couple of features (no image generation, no Canvas), which slightly limits its multimodal output capabilities – though it can still describe images or write code, it just can’t render images or use the canvas for drawing. Also, some users report that o3-pro can be “overkill” for simple tasks – it might give extremely long-winded answers or delve into more detail than needed (since it’s trying to be extra thorough). This is a minor downside; you can usually instruct it to be brief. Finally, one must note that even o3-pro is not infallible – while it greatly reduces hallucinations and errors, it’s not 100% accurate. Users should still verify critical outputs (especially since o3-pro often deals with high-stakes queries, you’ll want to double-check its results).

• Best Suited Use Cases: O3-pro is the model you turn to for the most challenging and critical queries where you absolutely need the best answer possible and are willing to wait for it. Ideal scenarios:

  • High-Stakes Problem Solving: e.g. verifying a complex engineering calculation, solving an unfamiliar but critical programming bug, or performing a detailed legal analysis. O3-pro will give you the highest confidence answer in such cases.

  • In-Depth Explanations & Tutoring: If you are learning a difficult concept (say quantum physics or advanced mathematics) and want an AI tutor, o3-pro is unparalleled. It will patiently explain step by step and cover all nuances, much like an expert teacher. It’s great for educators or students who need perfectly explained solutions.

  • Scientific Research Assistance: For researchers asking ChatGPT to analyze experimental data or cross-check logic in a proof, o3-pro’s methodical approach is ideal. It’s less likely to make a careless mistake, which is crucial in research contexts. It can also generate hypotheses or critique methodologies with a very keen eye.

  • Complex Multi-Step Workflows: If you have a task that involves many stages (for example: “search for these 5 topics, gather data, analyze each with code, then compare and write a report”), o3-pro is more likely to execute this correctly using the tools and produce a coherent final result. It’s like having a highly diligent project assistant.

  • Areas Requiring Precision: Fields like finance (calculating risk models), medicine (analyzing symptoms with medical literature via browsing), or law (interpreting a contract’s clauses) – these often benefit from o3-pro’s extra caution and detail to avoid any misinterpretation.

  
  

  In essence, use o3-pro when quality matters more than speed. It’s overkill for a simple chat about the weather, but for a thesis-level question or a mission-critical coding issue, it’s worth every second of waiting. Many Pro users save o3-pro for “important” questions and use GPT-4o or 4.1 for routine ones. By deploying o3-pro on the toughest problems, you get the closest approximation to an expert human consultant that ChatGPT currently offers.

OpenAI o4-mini (Next-Gen Fast Reasoning Model)

• Availability Tier: Included for Plus, Pro, Team, Enterprise, Edu users since April 2025. It appears in the model picker as “OpenAI o4-mini” alongside o3. Also available via API with appropriate developer tier. By late April 2025, free users also got access to o4-mini in a limited form – in the ChatGPT UI, free users could select a “Think” mode for tougher queries, which uses o4-mini behind the scenes. Over time, o4-mini fully replaced the older o3-mini as the default reasoning-capable model for free usage as well. So in summary: all users have some access to o4-mini (with free being restricted by quotas), while paid users can use it freely (Plus/Team: ~300 messages/day, Pro: higher).

• Token Context Limit: 200,000 tokens (200k) context window, same as o3. This is a huge context for a “mini” model, enabling o4-mini to handle long or multiple documents in one go. Its max output is similarly around 100k tokens, though typically it won’t produce nearly that much unless asked for an extremely exhaustive answer.

• File Upload Capability: Yes. Like o3, o4-mini has full support for ChatGPT’s advanced tools. In fact, a key improvement of o4-mini over the previous generation is complete tool parity with larger models. It can perform web searches, run Python code, accept vision inputs, generate images with DALL·E, and handle file uploads – none of these are gated out. For example, you can upload a PDF and o4-mini can analyze it, or ask it to create an image and it will do so. All the file size limits (512 MB, etc.) and counts (20 files per chat for Plus) apply equally. One advantage of o4-mini’s efficiency is that usage limits are higher – Plus users can send more daily messages with o4-mini than with o3 (e.g., 300/day vs 100/day), making it suitable for batch processing multiple files or queries in a day.

• Vision and Image Support: Yes. O4-mini is a multimodal model – it can understand images that you upload (just like o3, it can describe and analyze them), and it also supports vision outputs (via the image generation tool). This was highlighted by OpenAI as a major feature: unlike its predecessor o3-mini, o4-mini can handle vision input and output. It leverages the same improvements from GPT-4o, meaning it can do tasks like reading the content of a chart or interpreting a meme. It’s also good at visual tasks in STEM fields (e.g. solving a visual math puzzle). Additionally, o4-mini can work with the voice mode in the mobile app – it can listen to voice queries and respond (using the app’s TTS) since it supports the multimodal pipeline.

• Memory Support: Yes (persistent). O4-mini utilizes the ChatGPT memory feature across sessions for Plus/Pro accounts. Moreover, because it’s built on the o-series paradigm, it has some improvements in how it references conversation history. External evaluators noticed that both o3 and o4-mini provided more personalized and memory-consistent answers due to improved training that references past dialogue better. Free users using o4-mini (via “Think” mode or as default once rolled out) do not have cross-session memory, but within a conversation o4-mini will remember up to 200k tokens of history.

• Key Strengths: OpenAI o4-mini is a smaller, faster reasoning model that aims to deliver much of o3’s benefits at a lower cost and with higher throughput. Its strengths include:

  • Balanced Reasoning + Speed: O4-mini attempts to blend strong reasoning abilities with improved speed and efficiency. It achieves remarkably good performance on tasks like math and coding for its size – in fact, it outperformed the older o3-mini on a variety of STEM benchmarks and even on some non-STEM tasks. At the same time, it’s lighter-weight, allowing more rapid responses and more concurrent usage. This makes it a great “fast thinking” model – it provides step-by-step logical analysis but without the significant delay of o3.

  • High Throughput / Volume Use: Thanks to its efficiency, OpenAI significantly raised the usage limits for o4-mini. It’s described as a strong option for high-volume, high-throughput scenarios where you need reasoning on many queries. For instance, if a support center wants an AI to handle lots of customer questions that require some reasoning, o4-mini can handle a larger number of interactions per day than o3. Similarly in the API, o4-mini’s token pricing and rate limits are lower, enabling cheaper deployment at scale.

  • Solid Performance on Benchmarks: O4-mini was reported to set a new state-of-the-art on certain benchmarks for its category. For example, it achieved top scores on the AIME (American Invitational Math Exam) 2024 and 2025 problems (when allowed to use tools). It basically means that for many academic or logical tasks, o4-mini does extremely well, rivaling much larger models in accuracy. It also inherited algorithmic improvements from o3, so it shows better results in programming and quantitative tasks compared to o3-mini.

  • Improved General Abilities: Unlike previous mini models that sometimes struggled with following complicated instructions or handling open-ended tasks, o4-mini made gains in instruction following and versatility. External testers noted it gives more useful and verifiable responses than its predecessors, partly due to training improvements and inclusion of browsing for source citation. So it’s not only good at math/code – it’s also quite capable for things like writing and data analysis now.

  • Tool Utilization: O4-mini, despite being smaller, effectively uses tools similar to o3. One example given: on the AIME 2025 math exam, o4-mini got 99.5% of questions right when allowed to use the Python tool to do calculations. This shows that o4-mini “knows what it doesn’t know” and will smartly use the calculator or other tools to compensate, leading to very high accuracy results.

• Known Limitations or Tradeoffs: Being a smaller model than o3, o4-mini is not quite as absolutely rigorous on the most complex problems. For example, on extremely difficult, novel problems (especially those requiring extended chain-of-thought with less available data), o4-mini might occasionally make an error or oversimplify where o3 would persist longer. In OpenAI’s internal evals, o3 still has the edge in some “high difficulty” domains – o4-mini is designed to cover most reasoning needs, but a few extremely complex or edge-case tasks might still favor o3’s brute-force thinking. Essentially, o3 may outperform o4-mini on highly complex tasks, whereas o4-mini can be faster for simpler reasoning or code generation. Another limitation: because o4-mini is fast, it usually uses a bit shallower reasoning by default (though you can invoke a “high effort” mode – see o4-mini-high below). If not instructed otherwise, o4-mini might give a slightly less detailed explanation than o3 would, in the interest of speed. If you need that detail, you might ask it to “explain step by step,” which it can do, just a tad less naturally than o3. Also, as with any model, hallucinations can occur – though improved, o4-mini can still confidently make up info if pushed outside its knowledge. It’s recommended to use the browsing tool if you suspect it needs factual updates. Finally, while usage limits are higher, they’re not unlimited; heavy users on Plus might still bump into the daily cap of 300 messages on o4-mini (which resets daily), and free users have a much smaller pool (replenishing every 5 hours).

• Best Suited Use Cases: O4-mini is a great choice for everyday logical tasks where you want a good balance of accuracy and speed. Use cases include:

  • Step-by-Step Explanations on the Fly: If you have a question that needs some reasoning – like “Explain how to solve this physics problem” or “What is the argument of this philosophy text?” – o4-mini will give a structured answer quicker than o3, making it useful for interactive learning or homework help in real-time.

  • Coding and Scripting: For writing code or small-scale debugging, o4-mini is usually sufficient and much faster. It can handle writing functions, explaining code, or suggesting improvements, with less waiting. Only for extremely complex code bases might you need a bigger model.

  • Data Analysis and Summarization: You can upload a couple of datasets or documents and ask o4-mini to analyze or compare them. It will use Python and other tools as needed, and because of its lighter footprint, you can iterate quickly (ask follow-ups, refine the analysis) without hitting slowdowns or low message limits. For example, parsing a 50MB CSV for insights – o4-mini can do that fairly well.

  • High-volume Q&A or Assistant Tasks: If you’re deploying ChatGPT as an assistant that handles many queries (for a team or business), o4-mini is ideal due to its higher throughput. Each query might involve some reasoning, but nothing o4-mini can’t handle, and it will keep up better with large numbers of requests. Customer support bots, FAQ assistants, or tutoring systems could leverage o4-mini to serve many users concurrently.

  • When o3 is Overkill: In cases where o3 could solve it but you don’t need that level of scrutiny – e.g. a moderately difficult puzzle or a business analysis that doesn’t require absolute perfection – o4-mini will likely give you a correct and well-structured answer in a fraction of the time. It’s a good default for reasoning if you’re not sure you need the heavy o3.

  In short, OpenAI o4-mini is best for “everyday reasoning” – it’s powerful enough for most analytical tasks and much more agile. Think of it as the workhorse model for reasoning tasks that demand quality but also efficiency. Only reach for o3 if you notice o4-mini struggling or if the question is extraordinarily complex.

OpenAI o4-mini-high (High Effort Mode of o4-mini)

• Availability Tier: Same as o4-mini – available to Plus, Pro, Team, Enterprise/Edu users. In the ChatGPT UI, o4-mini-high appears as a separate option (often listed as a variant of o4-mini). Free users do not have the “high” mode (free can only use the standard o4-mini via the Think toggle). Plus/Team users are limited to fewer daily messages on o4-mini-high (e.g. ~100/day on Plus) because each high-effort response uses more compute. Pro users have higher limits.

• Token Context Limit: 200k tokens, same as standard o4-mini (no change in context size). O4-mini-high doesn’t increase how much text it can handle, it increases how much reasoning it applies.

• File Upload Capability: Yes. O4-mini-high is the same model as o4-mini, just running with a higher “reasoning effort” setting. It has identical tool and file capabilities. You can upload files, use images, etc., just as with o4-mini.

• Vision and Image Support: Yes. It retains all multimodal features of o4-mini (image understanding and generation).

• Memory Support: Yes. Memory works the same as o4-mini (persistent for paid accounts, full conversation recall).

• Key Strengths: O4-mini-high essentially tells o4-mini to “take its time and be thorough.” The model will produce more detailed reasoning chains and consider more possibilities before answering. Strengths include:

  • Improved Accuracy on Reasoning Tasks: By giving the model more computation per query, o4-mini-high can catch mistakes that the normal mode might miss. It’s closer to o3’s carefulness, albeit using the smaller brain of o4-mini. Users might notice fewer logic gaps or arithmetic errors when using high mode for tough questions.

  • More Detailed Explanations: O4-mini-high tends to output lengthier, more step-by-step answers (because it’s effectively configured to not rush). For educational or analytical queries, this means you get a richer explanation or proof.

  • Retains Speed Advantage (vs o3): While slower than regular o4-mini, it’s still generally faster than using o3 for the same task. It provides a middle ground where you get better reasoning without the full slowness of o3.

• Known Limitations or Tradeoffs: The tradeoff for high-effort mode is longer response time and higher message cost. On Plus accounts, each o4-mini-high message is counted more heavily (hence the lower daily cap of ~100). So you’d only use it when needed. Another limitation: because it’s the same underlying model, it doesn’t magically gain knowledge or capabilities beyond o4-mini’s scope. For extremely complex tasks that genuinely need the bigger model’s capacity, o4-mini-high might still fall short. (Think of it like overclocking a smaller engine – you get a bit more power, but it won’t become a V8.) There were also some reports from early users of higher hallucination rate in certain cases when using high mode with very large contexts. This might be due to the model trying too hard to fill in gaps. It’s something to watch out for: always verify critical answers. Additionally, using high mode for every query is inefficient – it might give unnecessary depth for simple questions.

• Best Suited Use Cases: O4-mini-high is best when you have a problem slightly beyond the normal o4-mini’s comfort zone, but you want to avoid the heavy cost of o3. For example:

  • Difficult Homework or Puzzles: If o4-mini gave an answer but you’re not fully confident, run the question again on o4-mini-high. It will double-check and possibly correct or elaborate on the answer.

  • Detailed Code Explanations: When you want a very thorough line-by-line explanation of code or a complex algorithm, high mode will ensure no step is skipped.

  • Medium-Complexity Analysis: For tasks that are complex but not exceedingly so (maybe analyzing a moderately complicated dataset or reasoning through a multi-paragraph logical argument), o4-mini-high provides that extra layer of assurance. It’s useful for analysts or students who have moderately complex questions regularly – they can use high mode for those without always resorting to o3’s limited weekly allotment.

  • “Almost o3” Scenarios: Essentially, if you feel a question might require o3’s level of reasoning but you’re not sure, trying o4-mini-high first is a good strategy. It often will handle the task, and you save your o3 budget. Only if high mode also struggles would you escalate to o3.

  
  
  
  

• In sum, OpenAI o4-mini-high is a dialed-up version of the fast reasoning model, ideal for cases where you need just a bit more confidence and detail. It bridges the gap between the quick thinking of o4-mini and the deep thinking of o3. You get most of the benefit of the latter on many tasks, while still benefiting from the efficiency of the former. It’s a great option to have for Plus and Pro users to maximize accuracy without always spending an o3 query.

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