Cloud vs. Local LLM Agents for Generating Lecture Slides with an Agent Skill

Three cloud models (Claude Opus 5.5, Sonnet 5.5 and Haiku 5.5) and one local model (qwen3.8 27B through Ollama on a Mac Studio, M4 Max, 64 GB) built the same 20–30 slide lecture deck from the same prompt, with the present-in-html Agent Skill in Claude Code. Opus and Sonnet scored 9/10 on both subjects, and Sonnet took about half Opus's time at a quarter to a third of its cost; Haiku scored 7/10 at about $0.30. The local dense model did not finish within 120 minutes.

A preliminary study. Each model ran once per task, so these are early indications, not firm conclusions. More tests are needed.

The Study, in Short

Each model got one course module and one fixed prompt: turn it into a 20–30 slide deck for a 50-minute lecture, with speaker notes on every slide and a quiz at the end. There were two subjects, Wireless 101 and STATS 101. The skill's checker and a blind LLM judge scored every finished deck.

Generated Decks: the Main Comparison

One run per model per subject. Each deck is one HTML file and opens in a new tab.

Wireless 101

STATS 101

Generated Decks: the Plan Experiment

A separate experiment, not compared with the runs above. Opus wrote one plan for STATS 101, and three local models built a deck from it.

Source, Data and Scripts

The prompts, the harness, the judge and the scripts are on GitHub: present-in-html/evals/model-comparison. The numbers behind the figures are here too: study-data.json and tables.md.

The study ran in Claude Code with my personal present-in-html plugin, not Apple's.