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ChatGPT-4o vs GPT-3.5: Full Comparison and Report


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The release of ChatGPT-4o marks a major turning point in the evolution of AI-powered conversational assistants.
For years, GPT-3.5 Turbo—often simply called “GPT-3.5”—set the industry standard for fast, affordable, and highly capable natural language models across a vast range of applications.
However, the arrival of ChatGPT-4o, OpenAI’s new “omni” model, introduces a dramatic leap not just in language understanding, but in reasoning, multimodal intelligence, and real-time performance. Unlike its predecessor, GPT-4o is designed to operate across multiple forms of input and output—text, images, audio, and, soon, even video—bridging the gap between written and spoken conversations in a seamless, unified way.

1. Performance

  • Speed: GPT-4o is dramatically faster. OpenAI reports GPT-4o responds to audio inputs in as little as 232 ms (average ~320 ms). By contrast, ChatGPT’s prior “Voice Mode” pipeline (using GPT-3.5 text model) averaged about 2.8 seconds latency. In text chat, GPT-4o matches GPT-4 Turbo’s throughput and is about 50% cheaper in API cost, implying faster processing.

  • Reasoning and Benchmarks: GPT-4o (and its smaller variant) substantially outperforms GPT-3.5 on reasoning tasks. For example, GPT-4o mini scored 82.0% on MMLU (a multi-domain reasoning test), far above GPT-3.5 Turbo’s score. OpenAI states GPT-4o “matches GPT-4 Turbo performance on text in English and code” and significantly improves on non-English text. Internal benchmarks show GPT-4o mini beats GPT-3.5 Turbo on all major tasks (textual reasoning, math, coding, and multimodal reasoning). Partners reported GPT-4o mini “significantly better than GPT-3.5 Turbo” at practical tasks (like data extraction and email generation).

  • Factual Accuracy / Knowledge: GPT-3.5’s training ended in early 2022, so it lacks knowledge of recent events. GPT-4o’s training cutoff is later (initially October 2023, updated to mid-2024) and it can fetch up-to-date information from the web during chat. In practice this means GPT-4o can answer questions about late-2023/2024 events that GPT-3.5 cannot. (Note: GPT-3.5 and GPT-4o both still sometimes hallucinate, but GPT-4o’s broader context and web access improve accuracy on current topics.)


2. Multimodal Capabilities

  • Modalities Supported: GPT-3.5 (the base model behind ChatGPT) was originally text-only. Recent ChatGPT versions can feed images through a separate GPT-3.5/4 vision module, but GPT-3.5 has no native audio or video understanding. GPT-4o (“o” for omni) is fully multimodal: it accepts any combination of text, images, audio, and video as input and produces text, image, or audio outputs. In other words, GPT-4o can hold voice conversations and watch videos end-to-end in the same model, whereas GPT-3.5 required separate speech recognition and generation systems.

  • Vision: Both models can analyze images in ChatGPT (via the “Vision” feature). OpenAI notes GPT-4o is “much better than any existing model at understanding and discussing images you share”. GPT-3.5’s vision capability (introduced Sept 2023) can describe photos, charts, etc., but GPT-4o advances this with more nuanced image reasoning. For example, GPT-4o can translate and discuss a photo of a foreign menu (learning about cuisine and recommending dishes). GPT-4o even plans to handle real-time video (e.g. explaining a live sports game) in future updates – something GPT-3.5 cannot do.

  • Audio/Voice: GPT-3.5 in ChatGPT used a pipeline: Whisper ASR → GPT-3.5 text → TTS voice. GPT-4o replaces this with a single end-to-end model. OpenAI highlights that GPT-4o was trained jointly on audio and text, so it can process tone, multiple speakers, background noise, and even produce singing or laughter. GPT-3.5 cannot natively “hear” or speak; it only handles transcribed text. GPT-4o’s native audio capability enables real-time voice chats (latency ~320 ms).

  • Other Modalities: GPT-3.5 has no video input and no audio output. GPT-4o can eventually output synthesized speech and handle live video frames (these features are under development). Both models support code and math via text; GPT-4o mini scores higher on math and coding benchmarks.


3. Pricing and Accessibility

  • ChatGPT Free vs Plus: Historically, ChatGPT Free used GPT-3.5 and ChatGPT Plus ($20/mo) gave access to GPT-4. After GPT-4o’s launch, OpenAI is making GPT-4o available to free users with limits. Free users get “GPT-4-level intelligence” with a weekly message cap; beyond that cap ChatGPT falls back to GPT-3.5. Plus, Team, and Enterprise users get higher GPT-4o usage limits (Plus up to 5× the free-user limit). The subscription cost for ChatGPT Plus remains ~$20/month, yielding faster responses and priority features (like earlier access to voice mode and file uploads). In short, GPT-4o is increasingly available even on free plans (with throttling), whereas GPT-3.5 was the unrestricted free-tier model before.

  • API Access: Both GPT-3.5 and GPT-4o are offered via OpenAI’s API (and Azure OpenAI). GPT-3.5 Turbo (text chat) is very low-cost (~$0.50 input / $1.50 output per million tokens for its 16k version). In comparison, GPT-4o mini (the smaller GPT-4o variant) costs about $0.15 input / $0.60 output per million tokens – roughly 60% cheaper than GPT-3.5 Turbo. The full GPT-4o (text) is higher-end (about $5 input / $20 output per million tokens). (For audio, GPT-4o is $40/$80 per million and mini is $10/$20.) GPT-3.5’s exact API pricing isn’t shown above, but the blog note implies GPT-3.5 Turbo is several times more expensive than GPT-4o mini. In all cases, GPT-4o mini offers a low-cost, high-performance option, while GPT-4o (full) targets premium applications.

Feature / Plan

ChatGPT Free

ChatGPT Plus/Team/Enterprise

API (1M tokens)

Default Model

GPT-4o (with usage limit) (reverts to GPT-3.5 after limit)

GPT-4o (higher usage cap)

GPT-3.5 Turbo / GPT-4o (text)

Monthly Cost

$0 (web app)

~$20/month (Plus); Team/Enterprise = custom pricing

Pay-as-you-go (token-based)

Speed (text)

Standard (limited threads)

Faster throughput, priority access

Depends on model (4o is faster)

Max Tokens / Message

~32k (GPT-4 limit)

~32k (or higher)

GPT-3.5: ~4k (some variants 16k); GPT-4o: 128k

Memory / Long Context

ChatGPT memory feature (across conversations)

Same (Plus can store more memories)

GPT-3.5: limited history; GPT-4o mini: 128k

Multimodal Support

Text, voice chat (Voice Mode), image & file upload

Same plus early voice/video features

GPT-3.5: text-only; GPT-4o: text+images+audio (via special endpoints)

Examples of Features

GPTs and Plugins, basic web search, standard chat

All above + advanced plugins, alpha voice/video, priority “memory” use

ChatGPT, Assistants API with function-calls

API Pricing (Text)

N/A

N/A

GPT-3.5 Turbo: ~$0.50 / $1.50 (approx)


GPT-4o mini: $0.15 / $0.60


GPT-4o: $5.00 / $20.00


4. Use Cases and Applications

  • General Text Generation: Both models excel at writing, summarizing, and conversing. GPT-3.5 Turbo is widely used for drafting emails, reports, brainstorming, and code snippets. GPT-4o, being more capable, delivers higher-quality, more coherent outputs on complex tasks (long documents, nuanced instructions) due to its stronger reasoning. For example, GPT-4o can produce creative content (stories, poetry) with better structure and fewer errors.

  • Programming and Math: GPT-3.5 Turbo can generate and explain code, but GPT-4o (and especially GPT-4o mini) is markedly stronger. In benchmarks, GPT-4o mini scored ~87% on coding problems (HumanEval) vs ~71–76% for competing models. This implies GPT-4o will produce more accurate code and math solutions. Use-case: pair-programming assistants or algorithm design – GPT-4o is preferable.

  • Data Analysis and Tools: GPT-4o’s support for function calling and long context lets it work with structured data more effectively. OpenAI notes GPT-4o mini has “strong performance in function calling” and can handle very long contexts. Practically, this means building chatbots that query APIs or databases is easier with GPT-4o. In contrast, GPT-3.5 can call functions too, but its shorter context and lower accuracy limit complex workflows.

  • Vision Tasks: GPT-4o is well-suited for any vision-related application. For instance, a customer could snap photos of faulty hardware and get troubleshooting advice, or use live video for assistance (“show me where to click next”). GPT-3.5’s image features work for basic tasks (image captioning, identifying objects), but GPT-4o’s improvements enable deeper image reasoning. Example: a field technician takes a picture of a chemical label and GPT-4o explains safety precautions (GPT-3.5 might do a simpler lookup).

  • Voice Interaction: GPT-4o opens up new voice-based use cases. It can hold natural spoken conversations, read aloud in expressive voices, and even sing or tell jokes with intonation. Use cases include virtual assistants that sound lifelike, language practice partners, or accessibility tools (like reading documents aloud in real time). GPT-3.5-based voice apps exist, but they rely on separate TTS engines and lack GPT-4o’s audio understanding.

  • Limitations:  GPT-3.5 is faster to run and cheaper, making it practical for high-volume or simpler tasks (e.g. bulk text generation at low cost). However, it occasionally hallucinates and struggles with very long or complex queries. GPT-4o, while more capable, still may produce confident-sounding errors and requires more computation. Both models are bounded by their training; GPT-4o’s cutoff (mid-2024) and GPT-3.5’s cutoff (2022) should be kept in mind. In summary, for straightforward chatbots or bulk content, GPT-3.5 can suffice; for demanding tasks involving multimodal input or deep reasoning, GPT-4o is the stronger choice.


5. Technical Architecture and Model Design

  • Architecture: OpenAI has not publicly released full parameter counts, but GPT-3.5 is widely believed to be on the order of 175 billion parameters (similar to GPT-3. GPT-4o’s size is undisclosed, but it likely uses a comparable or larger scale plus optimized components. Both are transformer-based models trained with supervised fine-tuning and RLHF. GPT-4o introduced a new shared tokenizer (improving non-English text handling). Importantly, GPT-4o is a single end-to-end model trained on text, images, and audio together. GPT-3.5 was trained only on text (though fine-tuned from a pre-GPT-4 architecture).

  • Context Window: GPT-3.5 Turbo historically accepted about 4,096 tokens of text context (long chat histories); variants can handle up to 16k tokens. By comparison, GPT-4o supports a 128,000-token context window. This enormous context length (enabled in both the full and mini models) means GPT-4o can process entire books or multi-hour dialogues in one conversation. It also supports up to 16k output tokens in a single response, far beyond GPT-3.5’s limit.

  • Training Data: GPT-3.5 was trained on internet text up to early 2022. GPT-4o’s training data goes later (up to Oct 2023, later updated to June 2024). Furthermore, GPT-4o can retrieve information via web search (the “Get responses from the web” feature) at generation time, whereas GPT-3.5 cannot. Both models inherit safety measures: GPT-4o builds on GPT-4’s alignment work, and GPT-4o mini applied an instruction hierarchy to resist prompt-jailbreaks. GPT-3.5 has basic RLHF alignment but is more prone to certain biases.

  • Memory: The chat interfaces include a memory feature (persisting user preferences and knowledge across sessions), but this is an application layer feature rather than the model itself. Architecturally, GPT-4o’s huge context window is effectively its “memory” of the conversation, whereas GPT-3.5 must rely on shorter histories.


6. API Availability and Integration Options

  • GPT-3.5 (Turbo) API: Widely available via OpenAI’s Chat Completions API (and Azure). Developers can send chat messages and receive responses. GPT-3.5 supports function calling, allowing it to output structured JSON or trigger external code. It is also available for fine-tuning in the “Fine-tune” API (certain variants) to specialize its behavior. GPT-3.5 Turbo is low-latency and is commonly used in production bots and apps.

  • GPT-4o API: The GPT-4o model family is available through OpenAI’s APIs in multiple ways. The Chat Completions API now includes GPT-4o (text) as a model option. There is a “Realtime” API tier for multimodal use: for text/chat, GPT-4o is $5/1M input and $20/1M output tokens; for audio transcription/translation it is $40/$80. The GPT-4o mini model is accessible via the Chat Completions API (as the new default “GPT-3.5” replacement) and the Assistants API. GPT-4o mini can also be fine-tuned soon. In practice, developers integrate GPT-4o the same way they use GPT-3.5: calling OpenAI’s endpoint with the chosen model name (e.g. gpt-4o-32k or gpt-4o-mini). GPT-4o’s API supports function calling and web browsing (via a special “browser” mode), enabling agent-like behaviors. For audio, OpenAI provides a gpt-4o-transcribe model for speech-to-text (higher quality than Whisper) and even text-to-speech synthesis using GPT-4o voice models. In summary, GPT-4o expands the API’s capabilities: any app that could use GPT-3.5 for chat can switch to GPT-4o for improved performance, and new APIs allow live audio/video integration.


Each model has its niche: GPT-3.5 Turbo remains a fast, cheap workhorse for text-only tasks, whereas ChatGPT’s new GPT-4o is a multimodal “omni” assistant excelling at rich, complex interactions.


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