Episode Details

Hey everyone, Alex here 👋

Welcome back to ThursdAI! And wow, what a week. Seriously, strap in, because the AI landscape just went through some seismic shifts. We're talking about a monumental open-source release from Alibaba with Qwen 3 that has everyone buzzing (including us!), Microsoft dropping Phi-4 with Reasoning, a rather poignant farewell to a legend (RIP GPT-4 – we'll get to the wake shortly), major drama around ChatGPT's "glazing" incident and the subsequent rollback, updates from LlamaCon, a critical look at Chatbot Arena, and a fantastic deep dive into the world of AI evaluations with two absolute experts, Hamel Husain and Shreya Shankar.

This week felt like a whirlwind, with open source absolutely dominating the headlines. Qwen 3 didn't just release a model; they dropped an entire ecosystem, setting a potential new benchmark for open-weight releases. And while we pour one out for GPT-4, we also have to grapple with the real-world impact of models like ChatGPT, highlighted by the "glazing" fiasco. Plus, video consistency takes a leap forward with Runway, and we got breaking news live on the show from Claude!

So grab your coffee (or beverage of choice), settle in, and let's unpack this incredibly eventful week in AI.

Open-Source LLMs

Qwen 3 — “Hybrid Thinking” on Tap

Alibaba open-weighted the entire Qwen 3 family this week, releasing two MoE titans (up to 235 B total / 22 B active) and six dense siblings all the way down to 0 .6 B, all under Apache 2.0. Day-one support landed in LM Studio, Ollama, vLLM, MLX and llama.cpp.

The headline trick is a runtime thinking toggle—drop “/think” to expand chain-of-thought or “/no_think” to sprint. On my Mac, the 30 B-A3B model hit 57 tokens/s when paired with speculative decoding (drafted by the 0 .6 B sibling).

Other goodies:

* 36 T pre-training tokens (2 × Qwen 2.5)

* 128 K context on ≥ 8 B variants (32 K on the tinies)

* 119-language coverage, widest in open source

* Built-in MCP schema so you can pair with Qwen-Agent

* The dense 4 B model actually beats Qwen 2.5-72B-Instruct on several evals—at Raspberry-Pi footprint

In short: more parameters when you need them, fewer when you don’t, and the lawyers stay asleep. Read the full drop on the Qwen blog or pull weights from the HF collection.

Performance & Efficiency: "Sonnet at Home"?

The benchmarks are where things get really exciting.

* The 235B MoE rivals or surpasses models like DeepSeek-R1 (which rocked the boat just months ago!), O1, O3-mini, and even Gemini 2.5 Pro on coding and math.

* The 4B dense model incredibly beats the previous generation's 72B Instruct model (Qwen 2.5) on multiple benchmarks! 🤯

* The 30B MoE (with only 3B active parameters) is perhaps the star. Nisten pointed out people are getting 100+ tokens/sec on MacBooks. Wolfram achieved an 80% MMLU Pro score locally with a quantized version. The efficiency math is crazy – hitting Qwen 2.5 performance with only ~10% of the active parameters.

Nisten dubbed the larger model "Sonnet 3.5 at home," and while acknowledging Sonnet still has an edge in complex "vibe coding," the performance, especially in reasoning and tool use, is remarkably close for an open model you can run yourself.

I ran the 30B MoE (3B active) locally using LLM Studio (shoutout for day-one support!) through my Weave evaluation dashboard (