Two models are competing for the title of best local coding AI in 2026: Qwen3-Coder and Llama 4 Scout. Both are available on Ollama, both run on consumer hardware, and both outperform models from a year ago that required much more memory. This guide compares them directly so you can pick the right one for your setup.
The Contenders
Qwen3-Coder is Alibaba’s dedicated coding model. It uses a mixture-of-experts architecture with 3B active parameters (80B total), meaning it punches well above its active parameter count in coding tasks. It is specifically trained and optimised for code generation, debugging, and technical reasoning.
Llama 4 Scout is Meta’s general-purpose MoE model with 17B active parameters (109B total) and native multimodal support. It is a strong all-rounder with competitive coding performance — not a dedicated coding model, but capable enough to compete.
Hardware Requirements
| Model | Active Params | VRAM Needed | Best For |
|---|---|---|---|
| Qwen3-Coder | 3B | 6–8GB | Laptops, budget GPUs |
| Llama 4 Scout | 17B | 20–24GB | RTX 4090, Mac Studio 32GB |
This hardware gap is significant. Qwen3-Coder runs on an RTX 4060 laptop. Llama 4 Scout needs an RTX 4090 or equivalent. If you are on a mid-range GPU, Qwen3-Coder is the practical choice.
Pulling the Models
# Qwen3-Coder
ollama pull qwen3-coder
# Llama 4 Scout
ollama pull llama4Coding Performance Comparison
Both models perform well on standard coding benchmarks, but in different contexts:
- Qwen3-Coder — exceptional for a 3B active model. Outperforms models with 3-4x more active parameters on HumanEval and SWE-Bench. Excels at code completion, function generation, and debugging single files.
- Llama 4 Scout — stronger for complex multi-file reasoning, architectural decisions, and tasks that benefit from its larger context understanding. The 10M token context window is transformative for large codebases.
Speed Comparison
Qwen3-Coder generates tokens significantly faster than Llama 4 Scout due to its smaller active parameter count. On an RTX 4090:
- Qwen3-Coder: 60–80 tokens/second
- Llama 4 Scout: 20–35 tokens/second
For interactive coding in VS Code or a chat interface, faster response times matter. Qwen3-Coder feels snappier for quick completions.
Which Is Better for Your Use Case?
- Choose Qwen3-Coder if: You have a mid-range GPU (8–16GB VRAM), you need fast token generation for interactive coding, or you primarily do single-file code generation and debugging
- Choose Llama 4 Scout if: You have 24GB+ VRAM, you work with large codebases across multiple files, you need multimodal support (passing screenshots of UI bugs), or you want a capable all-rounder beyond just coding
Using with VS Code
Both models work with the Continue extension for VS Code. In your Continue config (~/.continue/config.json):
{
"models": [
{
"title": "Qwen3-Coder",
"provider": "ollama",
"model": "qwen3-coder"
},
{
"title": "Llama 4 Scout",
"provider": "ollama",
"model": "llama4"
}
]
}The Verdict
For most developers, Qwen3-Coder is the better practical choice in 2026. It runs on hardware most people actually own, generates code faster, and its coding performance is remarkable for its active parameter count. Llama 4 Scout is the better model if raw capability and context size matter more than hardware requirements — but it demands high-end hardware to run.
If you have an RTX 4090 or Mac Studio with 32GB, try both and see which fits your workflow. If you have anything less, Qwen3-Coder is the clear recommendation.
