# Quoting Ted Sanders

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-10-28T23:23:21.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 130/100  
> **Primary Source:** [Simon Willison AI](https://simonwillison.net/2026/Oct/28/ted-sanders)  
> **Canonical Citation:** [https://spidits.com/timeline/quoting-ted-sanders](https://spidits.com/timeline/quoting-ted-sanders)

## Executive Summary
In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes.

## Why It Matters (Strategic Analysis)
Model version numbering functions as a key strategic tool for AI vendors to decouple incremental training updates from major architectural updates while managing API pricing tiers. Understanding the relationship between model size, token cost, and multidimensional task performance is critical for enterprise budget allocation and deployment design.

## Referenced Coverage & Sources
- **[Simon Willison AI](https://simonwillison.net/2026/Oct/28/ted-sanders)**: Quoting Ted Sanders — _In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes. Despite that complexity, much of the variation in..._

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*Synthesized by SPIDITS AI Market Intelligence Desk. Track live AI news, model releases, and funding: [https://spidits.com](https://spidits.com)*