New Open Source Qwen 397B BEATS GLM 5.1 & Claude? 🤯 | Nex N2 Pro TESTED

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: New Open Source Qwen 397B BEATS GLM 5.1 & Claude? 🤯 | Nex N2 Pro TESTED
- URL: https://www.youtube.com/watch?v=HDaUWU-3NyE
- Topic: Agent Architecture
- My current learning frame: Use Open Source Qwen 397B BEATS GLM 5.1 & Claude? 🤯 as a transcript-backed agent architecture walkthrough: at 0:21, it frames Qweny.
- Why this matters: New playlist item from xCreate; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:21 / Evidence 1: "Qweny. Qweny used to be the open source hero, but nowadays they're trying to monetize their platform, which good for them. But someone has picked up the reins and apparently this they're taking Qwen the the big one,..."
- 2:34 / Evidence 2: "Pro is more detailed. So, there's potential here. So, the next thing I'm going to do is subsequent prompts. So, I'm going to ask Quen to animate it. So, it produced 3,500 tokens here. So, I'm going to..."
- 4:19 / Evidence 3: "mathematics. So this is a math Olympiad question. I think Nimatron recently tried it out and it just gave the answer straight away. So I think these models have been trained with these answers but let's just see..."
- 6:07 / Evidence 4: "picked up on 3js.org. So, it's it what it it it did a job, okay? I didn't actually specify in the prompt that you need to be unique and you can't reuse code and you have to make..."
- 7:46 / Evidence 5: "thinking is good for coding, but let's just see what Qwen does with thinking enabled. And that is a worse generation, but again, we got gravity and we got no runtime errors. Can't click on the mouse. So,..."
- 9:24 / Evidence 6: "definitely better with thinking disabled. It's definitely better than Qwen. It's nowhere near better than GLM 5.1. So, I don't know how they say they can beat it, unfortunately. But, at least it's better than Qwen. and because..."
- 11:40 / Evidence 7: "At least here a lot we got a drop down for the font. You can change the font. Where is the drop down for a font here? Very, very similar. But yeah. Yeah, that's that's not an improvement."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A one-page agent harness map with tool boundaries and proof signals.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Model -> Harness -> Tools -> Verifier -> Artifact
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "New Open Source Qwen 397B BEATS GLM 5.1 & Claude? 🤯 | Nex N2 Pro TESTED", not a generic Agent Architecture essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.