AI Model Confusion

It’s becoming impossible to know which AI model to use for which task.

0
AI Model Confusion

It’s not just you. It’s not enough that there are a dozen good AI companies. What’s worse is that they all have a dozen different models with some non-standard names. It’s becoming impossible to know which AI model to use for which task.

2025 is going to be the year of model confusion. It feels like OpenAI is moving toward some sort of single model to rule them all, but is going at it by chomping away at the edges as it moves toward the center. It is getting harder and harder to know which model to use for which purpose. I’m hoping that the various AI companies will feel this and either create a standard, or vastly simplify things to a point where they make sense.

I asked ChatGPT DeepResearch to create a list of just the most recent OpenAI LLM models, their pros and cons and their use cases. The result was four pages long and the opening line is very telling:

The most widely used are GPT-3.5 and GPT-4, and newer variants like GPT-4 Turbo, GPT-4o, and GPT-4o Mini.

–OpenAI DeepResearch report

Comparison of OpenAI’s Latest LLM Models: GPT-3.5, GPT-4, and Newer Versions

Overview of Models

OpenAI has developed several generations of large language models (LLMs). The most widely used are GPT-3.5 and GPT-4, and newer variants like GPT-4 Turbo, GPT-4o, and GPT-4o Mini. Below is a high-level comparison of their key attributes:

ModelParameters (est.)Max Context WindowMultimodal InputRelative SpeedCost (API)*Release
GPT-3.5 (Turbo)~175 billion (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?)~4K tokens (some up to 16K) ([GPT-3.5 vs. GPT-4: Biggest differences to considerTechTarget](https://www.techtarget.com/searchenterpriseai/tip/GPT-35-vs-GPT-4-Biggest-differences-to-consider#:~:text=%2A%20Gpt,English%20language%20function%20calls))Text only ([GPT-3.5 vs. GPT-4: Biggest differences to considerTechTarget](https://www.techtarget.com/searchenterpriseai/tip/GPT-35-vs-GPT-4-Biggest-differences-to-consider#:~:text=%2A%20Multimodality.%20GPT,conversations%2C%20and%20document%20search%20and))Fast (small model) (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?)
GPT-4~1 trillion ([GPT-3.5 vs. GPT-4: Biggest differences to considerTechTarget](https://www.techtarget.com/searchenterpriseai/tip/GPT-35-vs-GPT-4-Biggest-differences-to-consider#:~:text=%2A%20Capabilities.%20GPT,more%20humanlike%2C%20seamless%20experience%20with))8K tokens (32K variant) (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?)Text (images via plugins) ([GPT-3.5 vs. GPT-4: Biggest differences to considerTechTarget](https://www.techtarget.com/searchenterpriseai/tip/GPT-35-vs-GPT-4-Biggest-differences-to-consider#:~:text=%2A%20Multimodality.%20GPT,conversations%2C%20and%20document%20search%20and))Slow (large model) ([GPT-3.5 vs. GPT-4: Biggest differences to consider
GPT-4 Turbo~1T (optimized)128K tokens (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?)Text & Vision variant (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?)Moderate (optimized)$10 / $30 per 1M tokens (in/out) ([GPT-3.5 vs. GPT-4: Biggest differences to considerTechTarget](https://www.techtarget.com/searchenterpriseai/tip/GPT-35-vs-GPT-4-Biggest-differences-to-consider#:~:text=In%20November%202023%2C%20OpenAI%20debuted,maximum%20of%204%2C096%20output%20tokens))
GPT-4o(undisclosed; optimized)128K tokens (similar)Text, Image, Audio ([GPT-4o vs. GPT-4: How do they compare?TechTarget](https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare#:~:text=GPT,blog%20post%20announcing%20the%20launch))Moderate (efficient) ([GPT-4o vs. GPT-4: How do they compare?TechTarget](https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare#:~:text=GPT,4))
GPT-4o Mini(smaller than 4o)128K tokensText & Image ([GPT-4o vs. GPT-4: How do they compare?TechTarget](https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare#:~:text=All%20users%20on%20ChatGPT%20Free%2C,clear%20timeline%20for%20that%20yet))Fast (small model) ([GPT-4o vs. GPT-4: How do they compare?TechTarget](https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare#:~:text=in%20its%20GPT%20series))

*Approximate API pricing for prompt (input) and completion (output) tokens. Lower is cheaper.

GPT-3.5 (ChatGPT Base Model)

GPT-3.5 is a refined version of GPT-3, introduced in late 2022. It powers the free ChatGPT and early ChatGPT Turbo APIs (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget). Despite a smaller size (~175B parameters) than GPT-4 (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget), GPT-3.5 is capable of fluent natural language generation and basic reasoning.

Pros:

Cons:

Ideal Use Cases:

  • Customer service chatbots & FAQ assistants: Fast, cost-efficient responses for common queries where absolute precision is not mission-critical.
  • Content drafting and brainstorming: Generating emails, social media posts, or blog ideas quickly. Humans can refine the output if needed (GPT-3.5 provides a quick first draft).
  • Language translation and summarization of short text: Converting text between languages or summarizing articles within its context limit. It performs these tasks fairly well for everyday content (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget).
  • Coding help for simple tasks: Useful for basic code snippets or debugging hints. It can generate code (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget), but for complex coding problems, GPT-4 is more reliable.

GPT-4 (Advanced Model)

GPT-4 is OpenAI’s flagship model released in March 2023. It’s much larger (≈1 trillion parameters) (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget) and was designed to be more reliable, creative, and capable than its predecessors (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget). GPT-4 powers the ChatGPT Plus service and is available via API for premium users. It set new standards for LLM performance, achieving human-level results on many academic and professional benchmarks (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget).

Pros:

Cons:

  • Slower response speed: GPT-4 is computationally heavy. Its responses typically have higher latency than GPT-3.5 due to the larger model size and more complex computations (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget). Users often notice that ChatGPT with GPT-4 “thinks” longer before producing output, especially for lengthy responses. This can impact real-time use.
  • High cost: Using GPT-4 via API is much more expensive than GPT-3.5 or other optimized models. For example, GPT-4 API calls cost about $0.03 per 1K tokens (input) and $0.06 per 1K output (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget) (roughly $30/$60 per million (GPT-4o vs. GPT-4: How do they compare? | TechTarget)) – dozens of times higher than GPT-3.5. This cost can add up quickly for large workloads, making GPT-4 best suited when its superior ability is truly needed.
  • Restricted access and throughput: Initially, GPT-4 API usage was rate-limited and it remains a premium feature (ChatGPT Plus or paid API). The higher cost and sometimes limited availability mean it may not be feasible for continuous high-volume tasks (where a cheaper model could suffice).
  • Diminishing returns on simple tasks: For straightforward tasks or short prompts, GPT-4’s extra power might be overkill. If a task doesn’t require complex reasoning or large context, GPT-3.5 or optimized models can achieve similar results faster and at far lower cost. In those cases, GPT-4’s use is hard to justify purely on ROI.
  • Knowledge cutoff and updates: GPT-4’s training data cutoff was initially late 2021 (similar to GPT-3.5). Later versions have been updated (some GPT-4 variants include data up to 2023) (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget), and it can use plugins or browsing for current information. Still, out-of-the-box it may not know very recent events, so it isn’t automatically “up-to-date” without internet tools.

Ideal Use Cases:

  • Complex problem solving and reasoning: GPT-4 excels at math word problems, logical reasoning puzzles, strategic planning, and multi-step analyses. It’s well-suited as a research assistant or to tackle hard questions where reasoning rigor matters.
  • Advanced coding and debugging: Developers use GPT-4 for difficult programming tasks – e.g. debugging tricky code, writing complex algorithms, or reviewing large codebases – because it understands context and programming logic better. It scored among top tiers in coding challenges and can handle longer code context, making it more reliable for non-trivial coding help.
  • Long-form content creation: When writing a detailed article, report, or story, GPT-4 can maintain coherence over long outputs. It’s ideal for drafting technical documents, creative fiction, or detailed essays. The larger context also means it can incorporate extensive reference material or earlier parts of a conversation when generating content.
  • High-stakes and analytical tasks: For use cases like legal document drafting, medical Q&A, or data analysis, where accuracy is paramount, GPT-4’s higher reliability is preferred (with human verification). It’s also useful for research – summarizing academic papers, extracting insights from data, or providing well-reasoned answers with supporting details (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget).
  • Multimodal queries: With vision input enabled, GPT-4 can describe images or charts, solve visual problems (like explaining a meme or graph), and assist in contexts that combine text and imagery (e.g. analyzing a schematic diagram alongside text).

GPT-4 Turbo (GPT-4 Turbo & GPT-4 Turbo with Vision)

GPT-4 Turbo is an enhanced version of GPT-4 introduced in late 2023. It offers major improvements in context length and cost. Specifically, GPT-4 Turbo supports a huge 128,000-token context window (about 300 pages of text in a single prompt) (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget) and comes at significantly reduced pricing. OpenAI also released a GPT-4 Turbo with Vision variant that can handle image inputs directly (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget). These “Turbo” models represent incremental upgrades to make GPT-4 more practical for large-scale applications (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget).

Pros:

  • Massive context capacity: With a 128K token window, GPT-4 Turbo can ingest or generate extremely large documents in one go (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget). This is ideal for tasks like analyzing lengthy contracts, books, or combining many documents into one conversation. It dramatically exceeds the 8K/32K limits of standard GPT-4.
  • Lower cost than standard GPT-4: GPT-4 Turbo was made much more affordable. Its pricing is roughly $0.01 per 1K input tokens and $0.03 per 1K output (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget) – about one-third the cost of original GPT-4 (which is $0.03/$0.06 per 1K). This 3× input and 2× output cost reduction (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget) makes using GPT-4-level capabilities more economical for larger workloads.
  • Up-to-date and improved: The Turbo model came with an updated knowledge cutoff (extended from 2021 to December 2023 for ChatGPT) (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?). This means it has more recent training data, potentially improving its factual accuracy on newer information. It also included minor performance refinements and bug fixes (“turbo” implies optimizations for efficiency and reliability) (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget).
  • Vision support: The GPT-4 Turbo with Vision variant can directly process images as input (without needing separate tools) (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget). This makes it easier to build applications that ask questions about images, perform OCR-related tasks, or generate text based on visual content, combining GPT-4’s reasoning with image understanding.
  • Maintains high performance: Importantly, GPT-4 Turbo still retains GPT-4’s strong capabilities in reasoning and creativity. It’s essentially the same core model, so quality of responses remains at the GPT-4 level. The improvements are mainly in efficiency and context size, with minimal trade-offs in answer quality.

Cons:

  • Still relatively expensive: While cheaper than original GPT-4, GPT-4 Turbo is still an order of magnitude costlier than GPT-3.5 or GPT-4o Mini. For instance, at $10 per million tokens input (GPT-3.5 vs. GPT-4: Biggest differences to consider | TechTarget), it’s 20× the price of GPT-4o Mini for the same volume. So for very simple tasks or huge scale deployment, Turbo may still be too costly compared to the smallest model.
  • Potentially slower with very long inputs: Handling 100K+ tokens can be computationally heavy. If you actually utilize the full context window, processing can be slow (in proportion to input size) ([ OpenAI’s API Pricing: Cost Breakdown for GPT-3.5, GPT-4 and GPT-4o | dida Insights

](https://dida.do/openai-s-api-pricing-cost-breakdown-for-gpt-3-5-gpt-4-and-gpt-4o#:~:text=A%20larger%20window%20size%20typically,more%20contextually%20relevant%20and%20coherent)). So while Turbo enables large inputs, using that capacity means more latency and compute cost per request. It requires careful prompt management to use efficiently.

  • Quality vs. GPT-4: GPT-4 Turbo’s quality is on par with GPT-4 for most purposes, but any subtle differences are not well-documented. OpenAI hasn’t disclosed detailed architectural changes (ChatGPT-4o vs GPT-4 vs GPT-3.5: What’s the Difference?). It’s possible that in corner cases, the original GPT-4 model (or the newest GPT-4o) might slightly outperform Turbo if Turbo was optimized for cost. However, for practical purposes these differences are minor.
  • Feature availability: GPT-4 Turbo was a new introduction – initially in preview – so not all libraries or tools supported the 128k context right away. There might be stricter rate limits due to the high context. Also, the Vision feature is only in the specific Turbo Vision model, which developers must explicitly use (it’s separate from the base Turbo model).

Ideal Use Cases:

  • Processing or summarizing large documents: Turbo shines when you need to feed very long texts. For example, summarizing entire research papers or even books in one prompt, analyzing lengthy transcripts, or comparing multiple documents side-by-side. It can handle contexts that were previously impossible without chunking.
  • Extended conversations and chatbots: For chat applications that require a very long memory (like multi-session conversations or threads with extensive history), the 128k context prevents older messages from “dropping out.” This is useful in customer service logs, RPG/storytelling bots that maintain lore over time, or any persistent chat scenario.
  • Batch processing and data analysis: Use GPT-4 Turbo to analyze large data dumps or logs in a single call. For instance, asking the model to extract insights from thousands of lines of logs or a big JSON file is feasible with the expanded window.
  • Cost-sensitive GPT-4 needs: Any scenario where you need GPT-4 level quality but want to trim cost a bit. GPT-4 Turbo, being cheaper, can be the default for GPT-4 level tasks if the absolute highest accuracy of GPT-4 isn’t required. It’s a good balance for applications like content generation or coding help in IDEs, where GPT-4 is great but using it constantly could be expensive – Turbo saves money while delivering similar results.
  • Vision-integrated tasks: Building a chatbot or assistant that users can both talk to and show images to (e.g. “What’s in this picture?” or “Read this diagram and explain it”). GPT-4 Turbo with Vision can handle that seamlessly, making it ideal for multimodal assistant apps on the web or mobile.

GPT-4o (Optimized “Omni” Model)

GPT-4o is a new model family launched in May 2024. The “o” stands for “omni” or “optimized”, reflecting two major aspects: it was built from the ground up for multimodality (text, images, and audio) and optimized for efficiency (GPT-4o vs. GPT-4: How do they compare? | TechTarget). GPT-4o is essentially a next-generation model that leverages GPT-4’s advancements but aims to be faster and more cost-efficient while handling more types of input. OpenAI trained GPT-4o end-to-end on text, vision, and audio data, so one neural network processes all modalities (GPT-4o vs. GPT-4: How do they compare? | TechTarget) (unlike GPT-4, which relied on separate systems for image or speech input).

Pros:

Cons:

  • Inconsistent real-world results: While benchmarks are impressive, user reports on GPT-4o vary. Some developers found GPT-4o sometimes underperforms GPT-4 on certain tasks. For example, anecdotes suggest GPT-4o can be weaker in coding assistance, classification, and complex reasoning compared to original GPT-4 (GPT-4o vs. GPT-4: How do they compare? | TechTarget). In coding tasks, a number of users felt GPT-4 still produced more accurate or direct solutions. These discrepancies suggest that GPT-4o’s training optimizations might have introduced trade-offs that affect specific use cases.
  • Verbosity: Testers observed that GPT-4o often gives more detailed (even verbose) answers than GPT-4 (GPT-4o vs. GPT-4: How do they compare? | TechTarget). It tends to elaborate at length. In many cases this can be a positive (more detail), but sometimes it goes overboard, providing unnecessarily long responses. Users who prefer concise answers might need to prompt it to be brief.
  • Performance not uniformly better: GPT-4o’s speed advantage isn’t always evident. In independent timing tests, GPT-4o was expected to be faster, but by January 2025 some prompts ran slower on GPT-4o than on GPT-4 (GPT-4o vs. GPT-4: How do they compare? | TechTarget). For straightforward queries, GPT-4 at times responded quicker (possibly due to ongoing model adjustments or differences in the ChatGPT service at the time). This means the touted 2× speed increase may apply primarily to multimodal or certain complex tasks, but not every use case. Real-world performance can depend on prompt types and system load, so GPT-4o’s speed edge is not guaranteed in every scenario.
  • New model stability: Being newer, GPT-4o might have less community trust and fewer established best practices than GPT-4. Organizations that have already integrated GPT-4 might be cautious to switch. In fact, if an existing workflow is finely tuned to GPT-4’s outputs, migrating to GPT-4o could require re-testing and adjusting prompts due to subtle differences. Some enterprises might stick with GPT-4 for critical systems where its long track record is valued over GPT-4o’s new features (GPT-4o vs. GPT-4: How do they compare? | TechTarget). Over time, this concern will fade, but it’s a short-term consideration.
  • Voice controversy (minor): Since GPT-4o introduced built-in voice capability (the model can output speech as “Sky”), there was a notable incident where the voice sounded like a famous actress, raising ethical concerns (GPT-4o vs. GPT-4: How do they compare? | TechTarget). OpenAI paused that particular voice. While this doesn’t affect text performance, it highlights that GPT-4o is pushing new boundaries (voice output) that come with unresolved issues. This is more of a side note on the model’s rollout.

Ideal Use Cases:

  • Multimodal applications: GPT-4o is the go-to model for apps that involve multiple data types. For example, a virtual assistant that a user can talk to (audio input) and show pictures to (image input), all in one conversation. GPT-4o can seamlessly handle a query like, “Here’s a photo of my garden, what kind of plant is this and how often should I water it?” using the image and then continuing the dialogue with text or voice answer.
  • Image and video analysis: Any task requiring interpreting visual content benefits from GPT-4o’s native vision understanding. This could be analyzing security camera footage (describing events in real-time), assisting the visually impaired by interpreting surroundings via a camera, or inspecting images in a workflow (like analyzing medical images or charts within the context of a broader text discussion).
  • Large-scale deployments with tight budgets: For companies that need high-quality language capabilities but find GPT-4’s cost prohibitive, GPT-4o offers a sweet spot. It delivers advanced performance at a fraction of the cost, making it suitable for things like an enterprise knowledge bot that combs through company data, or a customer support AI handling thousands of inquiries. The cost savings are substantial over GPT-4 for the same volume of text processed (GPT-4o vs. GPT-4: How do they compare? | TechTarget).
  • General-purpose chatbot with better quality: GPT-4o now powers the free ChatGPT service, replacing GPT-3.5 (GPT-4o vs. GPT-4: How do they compare? | TechTarget). This speaks to its utility for broad, general Q&A and conversation. It’s excellent for an AI assistant that needs to handle everything from casual small talk to moderately complex user questions. Essentially, it can serve as a superior “default model” for most use cases, given its balanced mix of capability and efficiency.
  • When both context and reasoning are needed: If you have tasks that involve very long context and require good reasoning, GPT-4o is a strong candidate. For example, analyzing a lengthy report and answering nuanced questions about it, or reading a long customer interaction history and then deciding the best course of action. It handles long context like Turbo, and reasoning nearly like GPT-4, which is a powerful combination for research assistants and analytical tools.

GPT-4o Mini (Lightweight Next-Gen Model)

GPT-4o Mini is a scaled-down version of GPT-4o introduced in July 2024 (GPT-4o vs. GPT-4: How do they compare? | TechTarget). It was explicitly designed to replace GPT-3.5 as OpenAI’s entry-level model, providing better performance than GPT-3.5 at even lower cost (GPT-4o vs. GPT-4: How do they compare? | TechTarget). GPT-4o Mini inherits the multimodal nature of GPT-4o (supporting text and images; audio input may be added later) (GPT-4o vs. GPT-4: How do they compare? | TechTarget), but in a smaller, more efficient package. It’s effectively the new default model for ChatGPT Free, Plus, and Team users as of its release (GPT-4o vs. GPT-4: How do they compare? | TechTarget).

Pros:

  • Extremely cost-efficient: GPT-4o Mini is the cheapest model in OpenAI’s lineup by far. Its pricing is roughly $0.15 per million input tokens and $0.60 per million output tokens (GPT-4o vs. GPT-4: How do they compare? | TechTarget) – orders of magnitude lower than GPT-4. This translates to about $0.00015 per 1K tokens (input). In fact, GPT-4o Mini is ~200× cheaper than GPT-4 for inputs (and ~100× for outputs) (Compare GPT-4o Mini vs. GPT-4). Such low costs enable widespread use and experimentation without worrying about budget.
  • Outperforms GPT-3.5: Despite being “mini”, its quality beats the older GPT-3.5 model. OpenAI stated GPT-4o Mini outperforms GPT-3.5 Turbo on various tasks (GPT-4o vs. GPT-4: How do they compare? | TechTarget). Partner companies testing it found it “significantly better than GPT-3.5” for tasks like extracting structured data from receipts and generating high-quality email replies using conversation history (GPT-4o mini: advancing cost-efficient intelligence | OpenAI). This means you get a notable quality boost over GPT-3.5, improving the user experience in many applications.
  • Good general performance: GPT-4o Mini, while not as powerful as full GPT-4, still carries many advancements of the GPT-4 family. It has decent reasoning ability, solid language understanding, and remains relatively coherent even on complex prompts. It’s more powerful than any model prior to GPT-4 in the majority of use cases (What is GPT-4o? OpenAI’s new multimodal AI model family – Zapier). This makes it a great default choice for everyday AI tasks – essentially delivering “near-GPT-4 skills at GPT-3.5 prices.”
  • Large context window: Like its big sibling, GPT-4o Mini supports a very large context (up to ~128K tokens) for input and a high output limit (Compare GPT-4o Mini vs. GPT-4) (Compare GPT-4o Mini vs. GPT-4). This is a huge jump from GPT-3.5’s context. Even though it’s a smaller model, it can ingest long conversations or documents, which the old GPT-3.5 could not. That enables more advanced interactions (for example, reading a lengthy email thread or document and then formulating a response) within a single call.
  • Fast and lightweight: Being smaller than GPT-4 or GPT-4o, the Mini model is optimized for speed. It can provide responses quickly, similar to GPT-3.5’s snappy replies, but with greater accuracy. This makes it suitable for real-time applications or running on modest hardware (where latency is critical). Essentially, it aims to retain the “fast & light” feel of GPT-3.5 while markedly improving quality.

Cons:

  • Lower ceiling than GPT-4/GPT-4o: As a scaled-down model, GPT-4o Mini doesn’t reach the absolute performance of the largest models on the most challenging tasks. Very complex reasoning, intricate creative writing, or domain-specific expertise might reveal its limitations. For example, GPT-4 might still outperform Mini on an advanced math proof or a highly complex coding challenge, simply because Mini likely has fewer parameters and thus less depth in handling edge cases.
  • Reduced power in niche tasks: If an application requires cutting-edge performance (like the highest score on a difficult benchmark, or solving tricky logic puzzles), GPT-4o Mini may not suffice. It’s more powerful than GPT-3.5, but it’s not GPT-4. So, in use cases like rigorous scientific Q&A or heavy-duty strategy simulation, you might observe the gap. Essentially, it trades off some peak performance to achieve efficiency.
  • Multimodal limits: At launch, GPT-4o Mini supports text and vision, but not audio yet (audio support is expected later) (GPT-4o vs. GPT-4: How do they compare? | TechTarget). So tasks involving speech recognition or audio input still require other models (like Whisper). Also, while it can process images, the complexity of what it can do with them might be somewhat less than GPT-4o, simply because of the model’s smaller capacity.
  • Less proven over time: Being a fresh model, it doesn’t have the long-term exposure that GPT-3.5 had. There may be unknown quirks or failure modes that will surface as it’s used by millions. However, since it’s built on the GPT-4o foundation (which underwent extensive safety evaluations) (GPT-4o mini: advancing cost-efficient intelligence | OpenAI) (GPT-4o mini: advancing cost-efficient intelligence | OpenAI), it likely inherits a lot of the robustness, and OpenAI has aligned it with the same safety mitigations (GPT-4o mini: advancing cost-efficient intelligence | OpenAI). This is more a caution that any new model might have surprises, though none glaring are known so far.

Ideal Use Cases:

  • High-volume chatbots and assistants: GPT-4o Mini is excellent for customer support bots, personal AI assistants, and other conversational agents that need to handle large numbers of queries cost-effectively. For example, an e-commerce support chatbot can use Mini to answer product questions, handle returns, etc., with better quality than GPT-3.5 – all while keeping API costs extremely low.
  • Business process automation: Tasks like reading invoices/receipts, forms, or emails and extracting data or drafting responses can be reliably handled by Mini. One proven case is extracting structured data from receipts (e.g. total, date, merchant) – Mini excels at this compared to GPT-3.5 (GPT-4o mini: advancing cost-efficient intelligence | OpenAI). Another is generating email replies given a thread – Mini produces high-quality, contextually aware responses (GPT-4o mini: advancing cost-efficient intelligence | OpenAI). These capabilities are useful for streamlining office work (email triage, report generation) at minimal cost.
  • Fine-tuning for specialized tasks: Because it’s cheap and reasonably good, GPT-4o Mini is a great candidate for fine-tuning (when OpenAI allows). Organizations can take Mini and fine-tune it on their proprietary data to get a model that performs very well in a niche domain – without the cost of fine-tuning a massive model. This could power things like an internal code assistant trained on a company’s codebase or a medical assistant tuned with healthcare data (leveraging Mini’s baseline capabilities).
  • Mobile and edge applications: Where computational resources are limited (like mobile devices or edge servers), a smaller model like Mini is preferable. While GPT-4o Mini would still likely be accessed via cloud API (it’s not small enough to run on a phone offline), its fast performance and low cost make it suitable for integration into consumer apps where responsiveness is key. For example, a language learning app that has an interactive chatbot could use Mini to provide instant feedback and conversation practice to users.
  • General content generation at scale: If you need to generate a lot of content (product descriptions, social media posts, simple articles), Mini offers a great balance of fluency and low cost. It can be used to mass-generate or analyze content where using GPT-4 for each piece would be cost-prohibitive. With Mini, one can afford to produce thousands of outputs, then maybe have a human or a larger model review a subset for quality control, thereby scaling content operations efficiently.

Each of these models has its niche, and often the best choice depends on the specific requirements of a task (accuracy vs. speed vs. cost). In summary: GPT-3.5 (and GPT-4o Mini now) are ideal for everyday applications needing fast and cheap responses. GPT-4 (and its Turbo variant) serve demanding tasks that need more intelligence, memory, or creativity – at a higher price. GPT-4o sits in between, offering a compelling blend of advanced features and efficiency, especially shining in multimodal use cases. By understanding these differences, one can select the most suitable model – from coding and content creation to customer support and research – optimizing for the desired balance of performance, cost, and capabilities (GPT-4o vs. GPT-4: How do they compare? | TechTarget) (GPT-4o vs. GPT-4: How do they compare? | TechTarget).

Leave a Reply 0