AI tool comparison
pi-llm vs Qwen3.6-35B-A3B
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Local AI
pi-llm
Run a private LLM server on Raspberry Pi 4 with hardware tool calling
75%
Panel ship
—
Community
Paid
Entry
pi-llm turns a stock Raspberry Pi 4 (4GB RAM) into a private local LLM server using 1-bit quantized Bonsai models (1.7B and 4B parameters, under 1GB each). It includes a web chat UI accessible across your home network and implements native tool calling for physical hardware control — LEDs, displays, servo motors, and GPIO peripherals. The setup requires no GPU and no cloud dependency. The Bonsai-8B model family (recently covered here) runs efficiently enough on Pi-class hardware that the tool calling loop — chat message → model decision → GPIO action → result back to model — completes in a few seconds on 1.7B parameters. The project is a clean demonstration of where sub-1GB quantized models are genuinely useful: edge AI applications where latency to a cloud API is unacceptable, privacy matters, and the task is constrained enough that a small model performs adequately. It ships with working examples for five hardware configurations.
Open Source Models
Qwen3.6-35B-A3B
35B total, 3B active: Alibaba's lean MoE coding beast goes fully open source
75%
Panel ship
—
Community
Free
Entry
Alibaba's Qwen team open-sourced Qwen3.6-35B-A3B on April 16, 2026 — a sparse Mixture-of-Experts model with 35 billion total parameters but only ~3 billion active per forward pass. That architectural trick is the whole story: you get near-frontier performance while consuming compute comparable to a 3B dense model. It's available under Apache 2.0 on Hugging Face and ModelScope. The model supports a 262K token context window (extensible to 1M with YaRN), multimodal inputs including text, images, and video, and is purpose-built for agentic coding workflows. On SWE-bench and Terminal-Bench it outperforms the much larger dense Qwen3.5-27B, matching Gemma4-31B on several benchmarks. RefCOCO visual grounding score hits 92.0 — some multimodal metrics reach Claude Sonnet 4.5 territory. Community reaction has been immediate: r/LocalLLaMA lit up with benchmarks showing it solving coding tasks that models with 10x the active parameters couldn't handle. The FP8 quantized variant runs comfortably on a single 24GB consumer GPU, making this the most capable locally-runnable coding agent most developers have ever had access to.
Reviewer scorecard
“The tool calling implementation on hardware GPIO is the genuinely novel part. Most Pi LLM projects just do chat — this one closes the loop so the model can actually actuate things based on conversation. The 1.7B model is fast enough that it doesn't feel like waiting, which changes the interaction model entirely.”
“3B active parameters with 35B parameter breadth is engineering magic. I'm getting near-frontier coding results in Cline and running it locally on a 3090 — the refusals are lower than Claude for security research too. Apache 2.0 means I can fine-tune it on my codebase. This is the best open-source coding model I've used.”
“A 1.7B model doing hardware control is a liability waiting to happen. The model hallucinates — what happens when it hallucinates a servo command? The project has no safety layer, no command confirmation, and no rate limiting on tool calls. Cool demo, genuinely dangerous in any real deployment.”
“MoE models have notoriously bad batching throughput — if you're serving this at scale, the economics don't work out. And Alibaba's track record on long-term model support and safety filtering is shakier than Google or Anthropic. It's impressive in isolation, but enterprise teams should pressure-test it before replacing frontier APIs.”
“This is a preview of the embedded AI future. When every Pi-class device can run a local model with tool calling, the 'smart home' becomes genuinely conversational without routing everything through a cloud API. Pi-llm is early and rough but it's pointing at something real: private, offline, embodied AI agents.”
“The gap between open and closed models is closing faster than anyone predicted. When a freely downloadable model matches Claude Sonnet on multimodal benchmarks, the frontier lab pricing power evaporates. Qwen3.6-35B-A3B is another milestone in the commoditization of intelligence — and commoditization always accelerates adoption.”
“The creative applications here are underrated — conversational LED lighting, AI-triggered displays for studio ambiance, physical generative art installations that respond to natural language. The fact that it runs offline matters enormously for gallery or installation contexts where cloud reliability is a risk.”
“I don't often care about coding models, but this one handles image + video understanding for design briefs surprisingly well. I used it to analyze a competitor's UI and generate a full redesign spec. The 262K context means I can feed entire brand guidelines without chunking.”
Weekly AI Tool Verdicts
Get the next comparison in your inbox
New AI tools ship daily. We compare them before you waste an afternoon.