ChatGPT 4o vs. 4.1 vs. o3: features, context, pricing, and practical differences
- Graziano Stefanelli
- 4 days ago
- 5 min read
OpenAI now offers three distinct AI models, among others, to suit different needs and workflows.
GPT-4o, GPT-4.1, and o3/o3-pro each bring unique architectures, feature sets, and strengths to the market, serving everyone from everyday users to enterprise developers.
OpenAI’s rapid release schedule has led to an AI ecosystem where model choice isn’t just about power, but about fit for workflow. The three current “flagship” models—GPT-4o (Omni), GPT-4.1, and the o3/o3-pro line—are all optimized for different scenarios: GPT-4o prioritizes speed and multimodal interaction; GPT-4.1 delivers long-context reasoning at a lower cost; and o3-pro is designed for robust agentic workflows and complex tool use in production settings.Understanding where each model excels is now essential for teams, developers, and companies designing products or internal processes around generative AI.
Release timeline and current availability reveal distinct audiences.
These models are offered in different places: GPT-4o is everywhere, o3/o3-pro is reserved for pro and enterprise, and GPT-4.1 is mostly API-only for now.
Model | Public launch | ChatGPT tiers | API | Notes |
GPT-4o | May 2024 | Free (text-only), Plus, Team, Enterprise | Yes | Default model for most users; multimodal (text, vision, audio) in paid tiers |
GPT-4.1 | April 2025 | Not in ChatGPT UI (API only) | Yes | Comes in full, mini, and nano variants |
o3 / o3-pro | April 2025 (o3), June 2025 (o3-pro) | Team, Pro, Enterprise/Edu | Yes | Reserved for high-throughput, reasoning-heavy, agentic work |
OpenAI thus segments the market: GPT-4o for broad access, GPT-4.1 for developers needing very long context, and o3/o3-pro for specialized workflows and enterprise automation.
Architectures and I/O channels distinguish their technical DNA.
Each model’s design reflects its mission: GPT-4o leads on real-time multimodality, GPT-4.1 on pure token context, and o3-pro on tool orchestration and robust text processing.
Capability | GPT-4o | GPT-4.1 | o3 / o3-pro |
Text | ✔ | ✔ | ✔ |
Images (vision) | ✔ (native) | ✔ (API) | ✔ (charts/graphics strong) |
Audio (TTS/STT) | ✔ (real-time) | — | — |
ChatGPT built-in tools | Web, Python, File, Vision | Not in UI | Web, Python, File, Vision—full agentic orchestration |
Design focus | Fast, versatile, multimodal | Long-context, efficient | Tool-chaining, deep reasoning, production stability |
For use cases involving images, live voice, or document understanding, GPT-4o is the clear leader; if your work demands pure text with extreme context (whole books, videos), GPT-4.1 is unmatched; o3-pro is ideal when you need agents that chain multiple tools or handle heavy reasoning over complex data.
Context window and file handling: capacity matters for practical workloads.
How much text a model can “remember” and reason about is crucial, especially for research, coding, and automation.
Model | Max context (tokens) | Typical output cap | File upload in ChatGPT | Practical remarks |
GPT-4o | 128 k | 16 k | 512 MB · 80 files | Works smoothly with mixed files; hard 128 k token cap |
GPT-4.1 | 1 M | 128 k | N/A (API) | Handles entire books, giant PDFs, and long videos; best for long-context evals |
o3 / o3-pro | 200 k | 100 k | 512 MB · 80 files | Stable under 100 k in UI; excels at multi-step reasoning and file tool use |
For massive research tasks, GPT-4.1 is in a league of its own; o3-pro handles large contexts well for agentic or workflow-based applications; GPT-4o remains the best generalist for everyday, multi-format document chat.
Benchmark tests and practical performance show unique strengths.
Recent public benchmarks and developer feedback highlight the concrete differences between models.
Metric / task | GPT-4o | GPT-4.1 | o3-pro |
MMLU (knowledge & reasoning) | 85–88 | ≈ 90 | 86–87 |
SWE-bench (code fixes) | 33 % | 54 % | 49 % |
Latency (API, first-token) | 0.7 s avg | 1.5 s @128 k ctx; 5 s for nano | 0.5 s (caching, o3-pro) |
Adversarial robustness | Good | Very good | Good-plus |
Long-video Q&A (Video-MME, no subs) | 65 % | 72 % | 68 % |
GPT-4.1 outperforms on long-context reasoning, advanced coding, and robustness, but is slower in API calls; o3-pro leads in latency and multi-tool chains; GPT-4o is highly competitive on general benchmarks and remains the speed leader for casual chat.
API pricing models impact cost and scaling for developers and businesses.
GPT-4.1 offers the lowest rates for long-context work; o3/o3-pro targets power users and enterprise with premium pricing and higher agentic quotas.
Model | Input | Output | Cached input | Multimodal extras |
GPT-4o | $5/M tokens | $20/M | $2.50/M | Vision & audio tokens included |
GPT-4.1 | $2/M | $8/M | $0.50/M | Long context included; vision included |
o3 | $2/M | $8/M | $0.50/M | Vision included; high agent/tool quota |
o3-pro | $20/M | $80/M | — | Reserved for Pro/Enterprise, high-SLA |
For startups and researchers working with huge datasets or long documents, GPT-4.1 is the best value; for high-frequency, production-grade use, o3-pro is reserved for those who need maximum reliability and agentic capability; GPT-4o remains the best for mainstream multimodal chat at a fair cost.
Security, privacy, and compliance are core features across all models.
All models share OpenAI’s certified infrastructure, but offer distinct data-retention options and legal policies relevant to enterprise and regulated sectors.
Aspect | GPT-4o | GPT-4.1 (API) | o3 / o3-pro |
Independent audits | SOC 2 Type 2, ISO 27001 | Same | Same |
Default data retention | 30 days* (UI), 48 h for files | 30 days* (API) | Same |
Enterprise “no-log” mode | Optional | Opt-out | Opt-out, granular controls |
In-model memory | Opt-in (Plus/Team) | Not applicable | Opt-in (Enterprise granularity) |
*OpenAI API data is typically deleted within 30 days unless required by law.
Rate limits, quotas, and stability affect both speed and reliability.
Message and request caps can change by tier and time; stability is crucial for production workloads.
Metric | GPT-4o | GPT-4.1 | o3 / o3-pro |
ChatGPT Plus cap | 40 msg/3h (free), 300 msg/3h (Plus) | n/a | 200 msg/3h (o3); o3-pro not in Plus |
Team cap | 3,000 msg/3h shared | — | 2,000 msg/3h shared |
API default limits | 50k TPM, 5k RPM | 50k TPM, 2.5k RPM | 60k TPM/6k RPM (o3); 80k/8k (o3-pro) |
Observed context drops | Rare; holds 128k UI | 1M in API, capped to 32k in ChatGPT | UI can drop to 64k at peak hours |
Timeout rates | Low | Moderate at 1M ctx | Slightly higher for agentic multi-tool chains |
For mission-critical apps, o3-pro and GPT-4.1 (API) are preferred for their higher quotas and more predictable context; GPT-4o is most reliable in the mainstream chat interface.
Tooling, agent ecosystem, and developer experience guide workflow design.
GPT-4o and o3-pro both offer modern built-in tools and agentic features in ChatGPT; GPT-4.1 is “bring your own tools” via API.
Capability | GPT-4o | GPT-4.1 | o3 / o3-pro |
Built-in tools (ChatGPT) | Web, Python, Deep Research, File-analysis, Vision | — (API only; devs build tools) | Same as 4o; higher agent tool-call quota for Pro |
Agent mode | Plus/Team/Pro, 40–400 tasks/mo | Not available | Yes (Pro: 400 tasks/mo) |
Function calling / JSON | Yes (stable) | Yes (API/streaming) | Yes (multi-step chains optimized) |
SDK and ecosystem | Python, JS, LangChain, LlamaIndex | Same | Same; more enterprise samples |
For autonomous agents, advanced search, and complex retrieval pipelines, o3-pro offers more agentic power and quota; GPT-4o provides the richest built-in media and user-friendly stack; GPT-4.1 leaves orchestration to the developer.
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