Researchers tested six AI chat models from Anthropic, OpenAI, and Google by asking them to identify locations in San Francisco from memory using latitude and longitude coordinates. The models were evaluated on their ability to distinguish between water, parks, buildings, and streets, with the best models achieving an 86% accuracy rate for the city map. Streets proved to be a weak point for most models, though some demonstrated an understanding of dense grid versus park areas. Cost varied significantly, with some top-performing models being five times cheaper than others for the same accuracy. AI
IMPACT Demonstrates LLMs' spatial reasoning capabilities and potential for reverse geocoding, though highlights limitations in street-level detail.
RANK_REASON Research paper detailing an experiment testing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
- Anthropic
- Claude Fable 5-1
- Claude Opus 5.5
- Gemini 4 Argon
- GPT-6.1 Sol
- GPT-6 Astra
- Gurnee
- Max Tegmark
- OpenAI
- OpenStreetMap
- San Francisco
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