---
date: "2025-10-25T00:00:00Z"
categories:
  - linkedin
  - coding
description: I built a GitHub profile app using various coding agents to compare performance. I found that quality varies significantly more than cost or speed, suggesting users should prioritize model quality over minor price or latency differences.
keywords: [coding agents, llm evaluation, benchmarking, web development, github api, model comparison]
---

I asked multiple coding agents and models to build the same app:

> Create a single-page web app at `index`.`html` that beautifully renders a GitHub user profile and activity comprehensively. Pick the ID in the URL ?`id`=..., default to ?`id`=`torvalds`.

... and compared their quality, cost, and speed.

My observations:

**Quality variance is the highest**. Some models / agents produce great visuals, some average, some fail completely.

**Cost and time variance are lower** among the successful models. About 2X variance in each.

This is unlike non-code usage, where quality varies _less_ than cost.

My takeaway: **Pick the best model** / **agent**. Don't worry about speed and cost - the variance is lower.

Results: https://sanand0.github.io/llmevals/coding-agents/

![](https://files.s-anand.net/images/2025-10-25-coding-agent-comparison-linkedin.jpg)

[LinkedIn](https://www.linkedin.com/posts/sanand0_i-asked-multiple-coding-agents-and-models-activity-7383420784389787648-moGP)