Continue.dev config.json Deep Dive: Multiple Models, Slash Commands, and Context Providers
Last updated: 2026-06-15
Most Continue.dev users set it up once, pick a model, and stop there. That's leaving serious capability on the table. The real power is in config.json — where you can run a large model for architectural questions, a blazing-fast small model for autocomplete, pull in documentation on demand, and build slash commands that encode your entire team's standards.
This guide covers the config in full. No cloud required.
Where Your config.json Lives
```
~/.continue/config.json # global (all projects)
```
Per-project configs are additive — they extend, not replace, your global config. That means your personal model profiles always load, and project-specific context providers layer on top.
Open it directly or via Continue → gear icon → Open config.json in VS Code.
Multiple Model Profiles: The Right Model for the Right Job
The models array is where you define every LLM Continue can reach. You switch between them with Cmd/Ctrl + ' or the model selector dropdown.
```json
{
"models": [
{
"title": "Llama 3.3 70B — Deep Work",
"provider": "ollama",
"model": "llama3.3:70b",
"apiBase": "http://localhost:11434"
},
{
"title": "Qwen 2.5 Coder 7B — Fast Chat",
"provider": "ollama",
"model": "qwen2.5-coder:7b",
"apiBase": "http://localhost:11434"
},
{
"title": "Gemma 3 27B — Long Context",
"provider": "ollama",
"model": "gemma3:27b",
"apiBase": "http://localhost:11434",
"contextLength": 131072
}
]
}
```
Strategy for local hardware:
| Task | Model Size | Why |
|---|---|---|
| Architectural review, refactors | 70B | Quality matters more than speed |
| Quick explanations, small edits | 7B–14B | Fast enough to feel instant |
| Long file analysis, big diffs | 27B+ with large context | Don't truncate the input |
If you have an Apple Silicon Mac, Ollama runs these efficiently on unified memory — a 70B Q4 model fits in 40GB RAM. For NVIDIA setups, VRAM is the constraint: a 24GB card handles 34B Q4 comfortably.
> Hardware recommendation: If you're bottle-necking on local inference speed, RunPod lets you spin up dedicated GPU instances by the hour — useful for one-off heavy tasks without committing to cloud subscriptions permanently.
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