---
type: concept
title: Open Models for Web Use
description: Self-hostable, open-weight models that are strong at tool use, API calling, and internet-assisted tasks.
tags:
  - open-models
  - tool-use
  - function-calling
  - llm
  - unverified
verified: 2026-08-28
timestamp: 2026-08-11
resource: https://qwenlm.github.io/blog/qwen3/
---

# Open Models for Web Use

The model is the brain behind the agent. For web research the model must call tools, follow instructions, and sometimes understand a page image. This page ranks the best self-hostable open-weight options.

> **Your setup:** Pi already serves local models through llama.cpp. See [Llama.cpp Server](../tools/llama-cpp/index.md) and [Pi Models Configuration](../pi/models.md). You can run any of the picks below on those hosts.

## The Front-Runner: Qwen3 (Alibaba, Apache 2.0)

Qwen3 is the current state of the art for open function calling and tool use. It has a thinking / non-thinking hybrid mode and strong tool-calling. Release date: April 2025.

| Model | Type | Active params | Context | Q4 VRAM |
|-------|------|---------------|---------|---------|
| Qwen3-8B | Dense | 8B | 128K | ~5 GB |
| Qwen3-14B | Dense | 14B | 128K | ~9 GB |
| Qwen3-32B | Dense | 32B | 128K | ~18 GB |
| Qwen3-30B-A3B | MoE | 3B | 128K | ~15 GB |

The 8B and 14B models run on one consumer GPU. The 30B-A3B MoE runs on a single A100-class card or equivalent.

## Best Vision Model: Qwen3-VL (Apache 2.0)

Many web tasks need a model that can read a page image, not just text.
**Qwen3-VL** supersedes Qwen2.5-VL as the strongest open-weights vision family
(4B/8B dense through 30B-A3B/235B-A22B MoE): text, chart, icon, and layout
reading in screenshots, plus computer-use agent support. Small dense sizes
ship Apache 2.0; a quantized 4B/8B runs on a single consumer GPU. On this
fleet, vision duty is carried by the mmproj vision towers on the endpoints
(dragoon, zealot) — see [System hosts](/system/index.md).

Llama 4 Scout is the larger multimodal option. Verify the claims before you trust them:

- "10 million token context" — true, but **Scout only**, not Maverick.
- "Fits on a single H100" — only true with INT4 quantization (about 48 GB).
- "Beats GPT-4o" — Meta's own release benchmarks, not independent runs.

## Choose by Hardware

```mermaid
graph TD
    A[Consumer GPU 16-24GB] --> B[Qwen3-8B / 14B]
    A --> C[Qwen3-VL-4B / 8B]
    D[Single server GPU 80GB] --> E[Qwen3-30B-A3B]
    D --> F[Llama 4 Scout INT4]
    G[Multi-GPU cluster] --> H[Qwen3-235B-A22B]
```

## Serving Options

- **llama.cpp** — CPU/GPU inference with GGUF quantization. Best for one consumer GPU. This is what Pi uses.
- **Ollama** — Easy local serving. Does not run in the Pi setup today.
- **vLLM** — High-throughput serving. Best for server and multi-GPU MoE models.
- **SGLang** — Strong for MoE serving.

```bash
# On the Pi llama.cpp host
# Serve a downloaded GGUF with the OpenAI-compatible API
llama-server -m qwen3-8b-q4_k_m.gguf -c 131072 --port 7770
```

## The Trade-Off

| Need | Pick |
|------|------|
| Best tool calling, one consumer GPU | Qwen3-8B or 14B |
| Visually read a web page | Qwen3-VL-4B / 8B |
| Server-scale reliable agent | Qwen3-235B-A22B |
| Cheapest acceptable agent | Qwen3-8B on a mid GPU |

## Citations

- Qwen3 technical report: <https://qwenlm.github.io/blog/qwen3/>
- Qwen3 repository: <https://github.com/QwenLM/Qwen3>
- Qwen3-VL repository: <https://github.com/QwenLM/Qwen3-VL> (tech report: <https://arxiv.org/abs/2511.21631>)
- Qwen2.5-VL-7B model card (predecessor): <https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct>
- Meta AI Llama 4 announcement: <https://ai.meta.com/blog/llama-4-multimodal-intelligence/>
- Ollama library: <https://ollama.com/library>
- Browser Use supported models: <https://docs.browser-use.com/open-source/supported-models>

## See Also

- [Self-Hosted Web Agents](self-hosted-web-agents.md) — The tools these models drive.
- [Model Context Protocol (MCP)](mcp.md) — Connect tools to the model.