Overview
ChatGPT is OpenAI's consumer assistant, powered by the GPT-4o and GPT-4o-mini family (and GPT-5-preview where enabled). It's the most-used AI product on the internet, the model most workflow guides assume, and — thanks to its Custom GPTs, memory, and tool ecosystem — the fastest way to prototype an AI feature end-to-end.
Capabilities
- Multimodal reasoning across text, images, audio, and screen shares.
- Custom GPTs with actions, file search, and code interpreter.
- Long-lived conversational memory across sessions.
- Native tool use: web browsing, Python code execution, image generation.
- Voice mode with low-latency spoken conversation.
Where it shines
- Broadest ecosystem — every automation platform, IDE plugin, and no-code tool integrates with the OpenAI API first.
- Best-in-class function/tool calling reliability for production agents.
- Fastest iteration on new features; new model releases usually ship here first.
- Custom GPTs offer a genuine no-code path from prompt to shareable assistant.
Where it struggles
- Long-context accuracy still trails Claude for 200K+ token retrieval tasks.
- Aggressive content filters can be noisy for security research and pen-testing workflows.
- Memory features can leak context between projects if you don't segment carefully.
Best for
- Building your first AI agent or Custom GPT.
- Any workflow that already depends on the OpenAI API.
- General-purpose research, summarization, and drafting.
- Voice-driven assistants and phone-based agents.
Pricing notes
GPT-4o-mini is the price/quality workhorse for most workflows; reserve GPT-4o (or GPT-5-preview) for reasoning-heavy tasks and treat that split as a config, not a re-architecture.
Tutorials & patterns that work
Build your first Custom GPT
Start in the GPT builder with a one-paragraph description of the job. Add file knowledge (max 20 files of ~2MB each), enable the tools you actually need (avoid enabling all three — it slows planning), and add one or two Actions calling your own API for anything the base model can't do. Test with adversarial prompts before publishing.
Reliable function calling in the API
Set tool_choice to 'required' when you know a tool must fire. Use JSON schema with strict mode enabled — this eliminates 90% of malformed argument errors. Log every tool call and its outcome to a structured store; you'll need it to debug the long tail of edge cases.
Cost control at scale
Route classification and simple extraction to gpt-4o-mini; escalate to gpt-4o only when a downstream confidence check flags the result. This tiered pattern typically cuts monthly spend 60–80% versus running everything on the flagship model.
Voice-first prototypes
The Realtime API lets you build a phone-quality voice agent in a day. Keep system prompts tight — voice sessions penalize long preambles more than text does. Design for interruption; users will talk over the model constantly.