Bench test · AI Coding
Continue vs LLaMA
Same rack, same rubric, four independent agents. Here's how they measure up — and which we'd pick.
| Continue | LLaMA | |
|---|---|---|
| consensus | score7.9/10 | score7.9/10 |
| agents won | 2 / 4 | 2 / 4 |
| from | Free | Free |
| free tier | yes | yes |
| category | AI Coding | AI Coding |
Agent panel — head to head
| Anthropic | 7.6 | 7.8 ▲ |
| OpenAI | 7.6 ▲ | 7.1 |
| Gemini | 8.2 | 8.8 ▲ |
| Grok | 8.2 ▲ | 7.8 |
Continue
- ✓Open-source and privacy-focused
- ✓Works offline with local models
- ✓Flexible integration with multiple AI providers
- —Requires manual setup and configuration
- —Performance depends on selected model quality
- —Smaller community compared to GitHub Copilot
AI code completion and generationSupport for multiple LLM providersLocal and cloud model optionsCode refactoring and documentationChat-based code assistanceCustomizable prompts and workflows
LLaMA
- ✓Runs on consumer-grade hardware with good performance
- ✓Fully open-source enabling community contributions
- ✓Competitive results compared to larger proprietary models
- —Requires technical expertise for setup and deployment
- —Less refined than some commercial alternatives
- —License restrictions on commercial use (original version)
Multiple model sizes (7B to 65B parameters)Open-source and freely availableOptimized for inference efficiencyStrong coding and reasoning capabilitiesSupport for multiple programming languagesFine-tuning friendly architecture
Free · free tier
Try Continue ▸Free · free tier
Try LLaMA ▸Verdict
Dead heat — both land at 7.9. Pick on price and fit.