Choosing an AI Model: OpenAI vs Anthropic vs Local LLMs
One of the biggest advantages of a model-agnostic AI assistant like OpenClaw is that you are not married to a single provider. You bring your own API key and switch models per task. This guide compares the main options — OpenAI, Anthropic, Google Gemini, and local LLMs — so you can pick the right engine for your self-hosted assistant.
Why model choice matters
Every model makes a different trade between reasoning quality, context length, speed, cost, and privacy. Locking yourself into one provider means inheriting all of its weaknesses. A bring-your-own-key, model-agnostic assistant lets you route heavy reasoning to one model, long-document work to another, and private tasks to a local model — without changing tools.
OpenAI (GPT-4o and friends)
OpenAI's models are a strong default: excellent general reasoning, reliable tool use, fast responses, and broad ecosystem support. For a self-hosted assistant handling mixed everyday tasks — drafting, summarizing, coding help, automation — GPT-4o is a safe, capable choice. You pay OpenAI directly for tokens with your own API key.
Anthropic (Claude)
Anthropic's Claude models are known for careful, well-structured reasoning, long context windows, and a strong safety posture. They excel at long documents, nuanced writing, and workflows where you want the model to follow instructions precisely. Claude is an excellent primary model for an assistant that does a lot of reading and writing.
Google Gemini
Gemini's headline strength is very large context, which suits assistants that must reason over big documents, transcripts, or codebases in a single pass. It is a good option when your workload is dominated by long-context tasks.
Local LLMs via Ollama
Running a local model with Ollama keeps every token on your own instance — nothing is sent to an external API, which is the strongest privacy guarantee available. The trade-off is capability: local models are smaller than hosted frontier models and need more compute. For privacy-critical tasks, or to avoid per-token API costs, a local LLM is compelling; many users pair a local model for sensitive work with a hosted model for heavy reasoning.
Quick comparison
| Option | Best for | Privacy | Cost model |
|---|---|---|---|
| OpenAI GPT-4o | General-purpose default | Data to OpenAI API | Pay per token (your key) |
| Anthropic Claude | Long docs, careful writing | Data to Anthropic API | Pay per token (your key) |
| Google Gemini | Very long context | Data to Google API | Pay per token (your key) |
| Local LLM (Ollama) | Maximum privacy, no API cost | Stays on your instance | Just your compute |
The point: you don't have to choose just one
Because OpenClaw is model-agnostic, the real answer is "use the right model for each job." Start with a capable hosted model, add a local LLM for private tasks, and change your mind whenever you like. Ready to try it? Follow the self-host OpenClaw guide or deploy from the homepage.