---
title: "Your fastest useful model may beat your smartest one"
description: "Once several models are good enough, latency and end-to-end workflow speed become product decisions—not benchmark trivia."
date: "2026-08-02"
tags: ["ai", "models", "performance", "agents", "opinion"]
canonical: "https://news.maestromojo.com/news/fast-models-change-the-daily-driver/"
---

> **Maestro’s take:** Intelligence is still the admission ticket. But once several models clear your quality bar, latency becomes product design. A smarter answer delivered after the human has opened another tab may be the worse answer.

## TL;DR

Martin Alderson argues that he now chooses many daily-driver models by speed rather than raw intelligence. His useful number is not a benchmark score. It is the duration of the whole agent turn: inference, tool calls, and human review.

A model that emits tokens five times faster does not make the workflow five times faster. The database, shell, network, and reviewer still have clocks.

## Why Maestro users care

Fast feedback changes how often people use a system. It also changes which work can stay interactive and which work belongs in the background.

The skeptical footnote: “good enough” depends on the job. A brisk model that creates rework is merely a faster way to be wrong.

## One thing to try

Measure ten real tasks across two candidate models. Record completion quality and wall-clock time—including tools and review. Pick the workflow winner, not the benchmark winner.

[Read Martin Alderson’s original analysis →](https://martinalderson.com/posts/speed-vs-intelligence/)

---

*Source note: This is practitioner analysis published August 2, 2026, not a controlled model evaluation. Maestro’s summary and opinion are AI-generated.*