ChatGPT 4o vs o3-pro: differences, limits, performance, and costs of the main OpenAI models

GPT-4o and o3-pro are now at the core of ChatGPT’s advanced offerings, each designed for different functions and target audiences—companies, developers, and advanced users.

In recent months, OpenAI has moved toward creating models tailored for increasingly diverse and specialized needs. On one side is GPT-4o, conceived as an “omni-channel” multimodal model, meant to offer broad accessibility and functional richness for every user type. On the other side is the o3 family, culminating in o3-pro, aimed at those who need maximum stability, high performance in automated workflows, and full API control.

This segmentation is a response to the growing complexity of enterprise and developer demands: users who rely on AI every day now seek both user-friendly, multimodal interfaces and optimized “engines” for massive text-processing pipelines.

Summary table: availability and versions by subscription and API.

Choosing between GPT-4o and o3-pro depends on your subscription plan, usage needs, and required capabilities.

The availability of these two models varies significantly based on your ChatGPT subscription level and access method. GPT-4o is designed to be broadly available—from Free plans (text only) up to Team and Enterprise offerings—while o3-pro is reserved for professional solutions and companies working at scale, as shown in the table below.

Model	Free	Plus ($20/mo)	Team	Pro ($200/mo)	Enterprise/Edu	API
GPT-4o	Text only	Yes (full)	Yes	Yes	Yes	Yes
o3 / o3-pro	—	—	Yes	Yes	Yes (since June ’25)	Yes (paid)
Multimodality	No	Yes	Yes	Yes	Yes	Text only (audio/vision via separate endpoints)

This distribution explains why most private users rely almost exclusively on GPT-4o, while enterprises and developers prefer o3-pro for complex integrations and advanced process control.

Architecture and input/output: GPT-4o focuses on omnichannel, o3-pro on text efficiency.

GPT-4o handles multimodal input (text, images, audio), while o3-pro delivers speed and optimized capabilities for API workflows and text production.

One of the biggest differentiators between these two models is their foundational architecture: GPT-4o natively handles text, images, and audio, merging these channels into a single cognitive context. This makes it ideal for tasks like analyzing PDFs with tables and charts, describing images, real-time voice chat, and generating multimodal responses.

O3-pro, on the other hand, is a “machine” specialized for pure text processing: it doesn’t integrate visual or audio I/O but offers greater response speed and predictability, especially when used as a backend for applications or automated systems.

Feature	GPT-4o	o3-pro
Input	Text, images, audio	Text only
Output	Text, images, audio	Text only
Native multimodality	Yes (unified architecture)	No (text pipeline)
Image generation	Yes (integrated)	No (requires plugin)
Real-time audio	Yes (ChatGPT App, API)	No

For those who work daily with different types of data, GPT-4o is a decisive evolution. For those focused on throughput, automation, and stability for large volumes of text, o3-pro remains the preferred solution.

Context window, token limits, and file handling: GPT-4o expands the window, o3-pro ensures stable reliability.

Operational differences emerge in file handling, conversation length, and the ability to work with very large archives.

When managing long conversations, large files, or complex document archives, the context window and the maximum number of usable tokens per session become key factors. GPT-4o extends these capabilities up to 256,000 tokens, both in the ChatGPT interface and via API, enabling work on huge documents or maintaining a very long conversational memory. O3-pro offers a nominal 128,000-token context window (sometimes reduced to 64,000 on ChatGPT Plus during peak hours), still ensuring solid reliability—especially for API flows.

Model	Chat window (tokens)	API window (tokens)	File upload limit	Operational notes
GPT-4o	256,000	256,000	512 MB (80 files)	Files analyzed up to 2M tokens
o3-pro	128,000 (64k in Plus during peak)	128,000	512 MB (80 files)	More stable under 100k tokens

Both models support uploading large files and direct analysis via the Python sandbox, but the difference in context window can be crucial for large reports, summarization processes, or cross-analysis of structured data.

Benchmarks, performance, and use cases: GPT-4o excels in natural conversation, o3-pro leads in API throughput and cost efficiency.

Testing and benchmarks highlight OpenAI’s design choices: GPT-4o is built for advanced interaction, while o3-pro dominates in repetitive flows and programmatic integrations.

In practical comparisons and public benchmarks, unique characteristics emerge that make these two models suitable for very different use cases. GPT-4o has achieved the highest scores in question answering, reasoning, and multimodal generation, standing out for conversational naturalness, quality of explanations, and the ability to combine text, images, and voice. O3-pro, meanwhile, offers higher throughput in API jobs, lower latency (response times), stability in long sessions, and predictable behavior even in massive automation scenarios.

Metric	GPT-4o	o3-pro
MMLU (QA/Reasoning)	≈ 87.1	≈ 86.5
Average latency (API)	-35% vs o3	+50–80 ms
Adversarial robustness	Superior	Good
Executable code	Yes	Yes
Image performance	Yes (native)	No
Audio conversation	Yes (live)	No
Timeout/freeze	Low	More frequent
Effective context	256k tokens	128k nominal (sometimes 64k in Plus)

In daily business, this difference means GPT-4o is preferred by those needing brainstorming, support for mixed files, presentations, and automation of varied analyses, while o3-pro is chosen by developers, integrators, and companies focused on performance, consistency, and batch processing of pure text.

Costs and rates: GPT-4o is more affordable and flexible, o3-pro retains premium GPT-4-Turbo logic.

OpenAI’s new model lowers the entry threshold for high-end AI, while o3-pro remains the go-to for critical projects with larger budgets.

The cost structure also reflects the different roles of these models. GPT-4o has marked a turning point in OpenAI’s advanced model pricing: at equal quality, the per-1,000 token cost is half that of o3-pro, and access to multimodal features incurs no extra fees. O3-pro maintains the premium price tier inherited from GPT-4-Turbo, justified by higher API SLA and rate limits, and greater predictability in mission-critical environments.

Model	Input cost /1k tokens	Output cost /1k tokens	Images	Audio
GPT-4o	$0.01	$0.03	$0.008 / image	$0.006 / min
o3-pro	$0.02	$0.06	n/a	n/a

For those working extensively with AI, the price difference can translate into significant savings, while large enterprise clients find in o3-pro the solidity required for ongoing operations and large-scale projects.

Limits, stability, and operational notes: the choice depends on workflow, automation, and multimodal capabilities.

Each model has strengths and weaknesses depending on use: GPT-4o is for those seeking versatility and advanced analysis, o3-pro is for those needing consistency and throughput in heavily text-based pipelines.

Despite advancements in both architectures, practical limits and minor issues remain to be considered in everyday deployment. GPT-4o may encounter batch errors when exceeding certain image input limits, or audio throttling if a session surpasses 60 consecutive seconds. O3-pro, meanwhile, may see its context window reduced during peak hours (especially in Plus) and registers a slightly higher frequency of timeouts, particularly for long API calls.

So... In summary, GPT-4o adapts to hybrid workflows and creative conversations, while o3-pro remains the reference point for automation, performance, and environments demanding absolute predictability.

____________

FOLLOW US FOR MORE.

DATA STUDIOS

datastudios.org