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xFunnel TeamModels Comparison
| Company | Model | Source | Citations | Depth |
|---|---|---|---|---|
| OpenAI | ChatGPT (Unauth) | Pre‑trained | Only in "Search" mode | Summaries |
| ChatGPT (Free GPT‑4o) | Pre‑trained | Only in "Search" mode | Detailed, nuanced | |
| ChatGPT o1 | Pre‑trained | None by default | Thorough, accurate | |
| ChatGPT o1 Pro | Pre‑trained, no web | None (reasoning) | Very deep reasoning | |
| OpenAI API (GPT‑4o) | Model only | No auto | Capable; can be lengthy | |
| OpenAI API (GPT‑latest) | Model only | None | Fast, concise | |
| Google Gemini 2.0 Flash UI | SGE (RAG) or training data | Not Always (SGE) | Rich, large context | |
| Google Gemini 2.0 Flash API | Dev chooses (tools) | Possible custom | Same as UI | |
| Perplexity | Perplexity AI (UI) | Always search (RAG) | Yes (each statement) | Concise, factual |
| Perplexity AI (API – Sonar) | Real-time search (RAG) | Yes | Multi-step reasoning | |
| Anthropic | Claude 3.5 Sonnet (UI) | Pre‑trained only | None | Very detailed, large context |
| Claude 3.5 Sonnet (API) | Pre‑trained only | None | Same model, structured outputs | |
| Microsoft | Microsoft Copilot (Bing) | Bing search (RAG) | Yes (footnotes) | Moderate detail |
| DeepSeek | DeepSeek L1 (UI) | Pre‑trained only | None auto | Summaries, references |
| DeepSeek R1 (UI) | Hybrid search (RAG) | Some links/URLs | Very in-depth, iterative | |
| DeepSeek L1 (API) | No web | None | Similar to UI | |
| DeepSeek R1 (API) | Web search (RAG) | Partial references | Detailed chain-of-thought |
UI vs. API Realities
Although user-facing chat UIs seem to provide rich references, live web access, or advanced reasoning, the API behind them often delivers a more bare-bones experience. A chatbot may show footnotes, clickable sources, or large context windows in its interface—yet when developers call the same model programmatically, they may only get raw text without citations or any guarantee of real-time search.
Key Differences
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Citations and Live Data
- UI: Many chat UIs overlay search results or footnotes, giving the illusion of real-time knowledge.
- API: Typically provides only the model's raw text output. Developers must add their own retrieval or citation logic.
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Model Versions and Modes
- UI: Often gives simplified toggle options ("with browsing," "without browsing," "deep reasoning," etc.).
- API: You select a model endpoint, but it won't magically fetch web data or reformat for references unless you implement it.
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Depth of Reasoning
- UI: May appear to reason in steps (chain-of-thought) or offer extended memory.
- API: Usually requires you to structure prompts and maintain conversation state. You see only final answers, not intermediate reasoning.
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Speed and Limits
- UI: Platforms optimize performance and handle usage limits behind the scenes.
- API: You must handle rate limits, tokens, or usage fees on your own, and performance can vary based on your implementation.
In short, the user interface can mask complexities. If you rely on an API, you won't automatically get those same features; it's up to you to replicate or enhance them. This is important when building custom applications: your end users won't see the fancy UI features unless you explicitly implement them or incorporate external data sources yourself.
Conclusion
For developers and businesses aiming to leverage AI capabilities, understanding these UI vs. API discrepancies is crucial. The polished experience you see when chatting with a model on a web interface may not reflect the raw data or response style you'll receive directly from an API. By carefully configuring prompts, hooking into search APIs, or building your own citation mechanisms, you can recreate (and sometimes surpass) the functionality of the official UI—just don't assume you'll get it "out of the box."