NEW Open Source Qwen? BEATS GLM 5.2 & Claude? 🧐 Ornith 397B TESTED
Credibility score: 48/100 — Mixed Credibility. Several questionable claims detected. Watch with healthy skepticism.
BSmeter analyzed "NEW Open Source Qwen? BEATS GLM 5.2 & Claude? 🧐 Ornith 397B TESTED" and rated it 48/100 for credibility (a BS score of 52/100 — mixed credibility), on 2026-06-28. Its weakest claim — "Claiming OR N 'promises to be the best state-of-the-art coding agent' based on 'amazing' benchmarks." — scored 45/100 and was flagged as confidence mismatch. 20 claims were checked against the video transcript. Scores are produced by BSmeter's AI analysis of the transcript, not independent human verification.
Claims analyzed
Opening with a random mask bit and 'dudy stuffs' for rich people. Just vibes. — Just Vibes (50/100)
Starting off with a completely random, unhinged intro about masks and 'dudy stuffs' — pure chaos for engagement 🎭
Claiming OR N 'promises to be the best state-of-the-art coding agent' based on 'amazing' benchmarks. — Confidence Mismatch (45/100)
Calling it 'the best state-of-the-art' and 'amazing' right out the gate, before showing any actual data. That's a lot of hype for a 'promise' 🚀
Points out GLM's benchmark omission, implying shadiness. — Missing Context (45/100)
Noticed a benchmark was conveniently left out — that's like showing your best angle and cropping out the double chin 😬
Claiming 'shady' behavior because a benchmark was omitted, implying malice. — Loaded Language (45/100)
Omitting a benchmark is one thing, but jumping straight to 'shady' and 'hiding' is a leap. Maybe it was an oversight? 🕵️♂️
Ornith model got into a runaway loop with thinking disabled, so it 'didn't count' 🤡 — Missing Context (45/100)
Dismissing a failure because 'it didn't count' is a convenient way to ignore bad results. The loop happened, regardless of the 'rules' 🙄
Qwen's less tokenized result is better, but acknowledges luck with the seed. — Volume Game (45/100)
Declares Qwen's result 'better' then immediately hedges with 'could have just been lucky with the seed.' The confidence giveth, and the confidence taketh away 🎲
Suggests finetuning might have overfit to pass benchmarks. — Confidence Mismatch (45/100)
Went from 'maybe' to a pretty confident 'overfit somehow' without any actual evidence. That's a leap of faith, chief. 🤸♀️
Giving specific token counts and speed for a model run, but the visual doesn't match the instruction. — Confidence Mismatch (45/100)
He's rattling off numbers like they're gospel, but the visual on screen is clearly not doing what he asked. The cat's still too close! 🤦♂️
Randomly linking a mask to 'Trumpian parties' and 'dodgy stuffs' — Loaded Language (45/100)
Went from 'what is this mask?' to 'Trumpian parties' and 'dodgy stuffs' real quick. That's a leap, not a logical step 🤡
Claiming the smaller 3.6 version beats the 'big guy' on Flappy Birds. — Confidence Mismatch (45/100)
Saying the smaller version is better, but then the demo shows it's 'a bit broken.' The confidence is doing overtime here 🤡
Comparing models, saying smaller is better, but where's the proof? 🧐 — Confidence Mismatch (45/100)
Claims the smaller version is better without showing the 'big guy's' results for comparison. Just vibes, no data. 🤷♀️
Claiming 'thinking enabled' is essential for general knowledge tasks. — Confidence Mismatch (45/100)
Declaring 'thinking enabled' is a MUST for general knowledge. Bro, it's a setting, not a magic wand. 🧙♂️⚙️
AI knows Fort Minor, not Fallout Boy, with 'thinking enabled' — a specific capability claim. — No Frame (75/100)
The AI correctly identified Fort Minor when 'thinking enabled' was on. That's a win for the AI, I guess. 🤖
Dismisses models as 'fail' without showing the actual 'fail' 💀 — Missing Context (45/100)
Calls it a 'fail' but we don't see the actual output that failed. Just trust me, bro energy. 🤷♂️
Calls a car animation 'definitely a good generation' — then immediately backtracks — Volume Game (45/100)
Starts with 'definitely a good generation' then immediately pivots to 'going a bit off road' and 'quality loss.' The confidence evaporated fast 💨
Bragging about speed with 'thinking disabled' — that's a choice 🏎️💨 — Missing Context (45/100)
Calling 25 tokens/second 'super fast' for a model with 'thinking disabled' is like praising a car for speed when it's on a flatbed. Context matters! 🤷♂️
Bragging about 25 tokens/second speed for Super Mario, but the 'thinking disabled' part is sus 🚩 — Missing Context (45/100)
Yeah, it's fast when you turn off its brain. That's like saying my car is fast if I take out the engine and push it. 🏎️💨
Exaggerating token usage for a simple task — the 'Miniax went to the max' bit 💀 — Loaded Language (45/100)
100,000 tokens for a train schedule? That's a lot of words for a timetable, sounds like a bit of an overstatement for comedic effect. 😂
GLM 5.2 suggested 'cut the children' as a top token, Ornith doesn't. — No Frame (75/100)
Comparing model outputs on a specific prompt — the GLM 5.2 result is wild, and Ornith avoids it. Good catch! 💀
Describes a 'gorgeous generation' of code, then notes a random output difference based on 'thinking enabled/disabled'. — Confidence Mismatch (45/100)
Calls it 'gorgeous generation' then immediately points out a weird, inconsistent output. The confidence in 'gorgeous' is doing some heavy lifting there 🤡.
See the full analysis with sources and timestamps →