command-center/omp-docs/agentic-web-research/open-models.md

type, title, description, tags, verified, timestamp, resource

type	title	description	tags	verified	timestamp	resource
concept	Open Models for Web Use	Self-hostable, open-weight models that are strong at tool use, API calling, and internet-assisted tasks.	open-models tool-use function-calling llm unverified	2026-08-28	2026-08-11	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 and Pi Models Configuration. 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: Qwen2.5-VL-7B

Many web tasks need a model that can read a page image, not just text. Qwen2.5-VL-7B is the best open vision model for this. It reads text, charts, icons, and layout in screenshots. It supports computer-use and phone-use agents. Apache 2.0 license. The 7B model fits on a single GPU with 8 GB or more.

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

graph TD
    A[Consumer GPU 16-24GB] --> B[Qwen3-8B / 14B]
    A --> C[Qwen2.5-VL-7B]
    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.

# 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	Qwen2.5-VL-7B
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
• Qwen2.5-VL-7B model card: 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 — The tools these models drive.
• Model Context Protocol (MCP) — Connect tools to the model.