Last post we stood up Ollama on the RTX 5090, pulled a stack of models, and wired them into our coding workflow. The whole time there was an obvious question hanging over it: are local models actually good enough?

Not good enough in the abstract benchmarks-on-a-leaderboard sense. Good enough for the thing we’re journaling: vibe coding. Specifically, can a model running on consumer hardware in my homelab produce code that's as correct, as fast, and as complete as what comes back from Anthropic's cloud?

We built a benchmark to find out.

The Setup

Six models, one prompt, no second chances.

Cloud (Anthropic API):

Sonnet 4.6 ( claude-sonnet-4-20250514 )

Opus 4.6 ( claude-opus-4-20250514 )

Local (Ollama on RTX 5090, 32 GB VRAM):

Codestral 22B ( codestral:22b )

DeepSeek R1 14B ( deepseek-r1:14b )

Devstral ( devstral:latest )

Qwen 3.5B MoE ( qwen3.5:35b-a3b )

The prompt was intentionally straightforward: build a Python CLI todo app with SQLite persistence, CRUD commands (add, list, complete, delete),…