For coding agents
Prioritize tool calling, repository context handling, and retry behavior over raw context size.
Model comparison
LLM Context Window Comparison
Use context length as one signal, not the whole decision. This static comparison should be updated from official model docs and release notes.
Data source plan: official model documentation, pricing pages, provider release notes, and manual verification before each public update.
| Model family | Best early use | Context need | Watch out for |
|---|---|---|---|
| GPT-class frontier models | Coding, agents, complex workflows | Medium to high | Cost and changing model names |
| Claude-class long context models | Documents, analysis, long prompts | High | Latency on very long tasks |
| Gemini-class multimodal models | Long context, video, multimodal apps | High | Workflow-specific behavior testing |
| Open-weight models | Privacy, local workflows, cost control | Low to medium | Hosting, evals, quality variance |
For document apps
Long context helps, but retrieval, chunking, and citations still need careful design.
For support bots
Cost and reliability often matter more than frontier model capability.