Our methodology for sourcing, normalizing, and presenting AI model data
Comparing AI models is challenging because providers use different terminology, pricing structures, and capability definitions. Logisync AI standardizes this information to enable fair, apples-to-apples comparisons. This page explains our methodology, data sources, and known limitations.
We integrate directly with provider APIs (OpenAI, Anthropic, Google, etc.) to fetch real-time pricing, model availability, and rate limits.
Model specifications, context windows, and capabilities are extracted from official documentation pages using automated scrapers.
Benchmark scores are aggregated from established leaderboards and academic evaluations to provide objective performance comparisons.
GPU specifications are sourced from official manufacturer datasheets and verified against independent benchmarks.
Our GPU sizing calculator uses industry-standard formulas to estimate memory requirements:
This formula provides conservative estimates suitable for planning. Actual requirements may be lower with optimizations like FlashAttention or PagedAttention.
Benchmark scores can vary based on prompting strategies, evaluation versions, and sampling parameters. We use official reported scores when available.
Provider pricing can change without notice. While we update frequently, always verify current pricing on the official provider website before committing.
GPU memory requirements are estimates based on parameter counts. Actual usage varies by framework, batch size, and optimization techniques.
Benchmark scores don't capture all aspects of model quality. Real-world performance depends on your specific use case and data.
We continuously update our data and methodology. If you notice incorrect information or have suggestions for improvement, please let us know.
Last updated: February 2026