Coding agent comparison
AI Code Assistants: Local vs Hosted Coding Agents
The real split is not autocomplete versus chat. It is whether the assistant can safely inspect a repo, run commands, edit files, verify the change, and explain exactly what it did.
| Factor | Local Assistant | Hosted Assistant |
|---|---|---|
| Privacy | Repo stays on your machine if the model is local. | Best models often require sending code/context to an API. |
| Cost | Hardware cost is fixed; electricity is low; quality may require tuning. | Token spend scales with usage, context length, and retries. |
| Latency | Fast for small local models, slower for hard reasoning. | Usually strong for reasoning, but network and rate limits matter. |
| Tool access | Excellent for local files, shell, builds, and private systems. | Strong when wrapped by an agent runtime with scoped tools and receipts. |
| Reliability | You own updates, drivers, disk, and model selection. | Provider handles serving, but outages and policy shifts become your risk. |
Same-Task Benchmark
Use the same repo task for every assistant. Otherwise you are comparing vibes, not coding systems.
- Clone a clean repo and run dependency install.
- Fix one failing test without changing public behavior.
- Add one small feature with existing style.
- Explain the diff and run verification.
- Record token cost, wall time, failures, and rollback steps.
MarketMai Take
The best coding-agent setup is hybrid: local repo authority, strict tool receipts, and hosted-model fallback when the task needs more reasoning than a local model can provide.
See the OpenClaw architecture