NEW 1.6 TRILLION Open Source AI vs Claude & GLM | LongCat-2.0 🫣
Credibility score: 50/100 — Mixed Credibility. Several questionable claims detected. Watch with healthy skepticism.
Claims analyzed
Video opens with a highlight reel preview, setting up a big claim about model performance. — Just Vibes (50/100)
Starting with a bold claim about 'no model will get this' is pure hype-building for the intro. Get ready for the main event! 🚀
Claims Long Cat 2.0 is a 'massive openweight model' with '1.6 trillion total parameters'. — No Frame (75/100)
A 1.6 trillion parameter model is genuinely massive in the AI world. The number itself is verifiable. 🤯
Mentions the full weight version was pulled, only 8-bit quantization released, with uncertainty on why. — Missing Context (45/100)
They dropped a huge model, then pulled the full version and just shrugged. 'I don't know' isn't exactly confidence-inspiring for a trillion-parameter release. 🤷♀️
Explaining memory requirements for running the model locally 💾 — No Frame (75/100)
Just laying out the specs for running this beast locally. Two terabytes for 8-bit, one for 4-bit. That's a lot of RAM, chief. 🤯
Throws out massive storage requirements with casual confidence. 🤯 — Confidence Mismatch (45/100)
Two terabytes for 8-bit, one for 4-bit, just like it's a USB stick. The numbers are huge but the delivery is chill. 💾
Benchmarking against specific models but omitting others 🚩 — Cherry-Picked (20/100)
They're showing benchmarks against some models, but conveniently skipping the 'current top two' in his opinion. Selective data much? 🍒
Declares it 'definitely better' than Gemini, then immediately qualifies it. 🚩 — Volume Game (45/100)
Starts with 'definitely better' than Gemini, then immediately says it's 'just above' Opus 4.6 and not even tested against the top two. That's a quick walk-back. 🚶♂️
Praising a Chinese delivery company's AI models despite their origin 🇨🇳 — No Frame (75/100)
Giving props to a Chinese delivery company for their 'impressive work' in AI models. Good tech is good tech, no matter where it's from. 👏
Acknowledges 'delivery of China' then pivots to 'impressive work.' 🇨🇳 — Missing Context (45/100)
Mentions 'delivery of China' like it's a footnote, then immediately praises their 'impressive work.' Skips over any potential implications. 👀
Compares LongCat-2.0's generation to Gemini and Opus, calling Gemini's 'horrible' and LongCat's 'better'. — Loaded Language (45/100)
Calling Gemini's output 'horrible' is a vibe, not a metric. It's just a subjective opinion dressed up as a definitive judgment 💀.
Comparing LongCat-2.0's game generation to Gemini and Opus, calling Gemini's 'horrible' and Opus's potentially worse with thinking disabled. — Confidence Mismatch (45/100)
Calling Gemini's generation 'horrible' and Opus's potentially worse, but it's all based on a single, subjective test run. That's a strong take for one game. 🎮
Setting up a comprehensive comparison for model competence 🧪 — No Frame (75/100)
He's gonna test this model against the 'best local generation' and cloud versions. This is how you actually test things, folks. Let's see it! 💪
Dismissing 'LongCat' for coding despite claims it's 'better than Gemini' — a classic 'trust my gut, not the marketing' move 🤷♂️ — Confidence Mismatch (45/100)
The speaker's personal experience with 'LongCat' for coding contradicts its advertised superiority over Gemini. It's a 'show, don't tell' moment where the 'show' isn't impressive. 📉
Doubts LongCat's coding ability despite claims of being 'better than Gemini'. — Confidence Mismatch (45/100)
Dismissing 'better than Gemini' with a 'maybe isn't the best' after one bad example. That's a quick judgment call. 🚩
Declares an answer, then immediately calls it 'debatable.' The confidence whiplash is real. 🎢 — Volume Game (45/100)
Says it's 'the answer' then immediately 'debatable.' The conviction lasted like 2 seconds. 💀
Declares an answer, then immediately says it's debatable. The old volume game. 📢 — Volume Game (45/100)
Drops a definitive answer, then immediately hedges it. The confidence is a rental. 🤡
Claiming models are 'very very expensive to run' without specific costs 💸 — Confidence Mismatch (45/100)
Says 'very very expensive' after blowing $5 on three prompts. That's not 'very very expensive,' that's a coffee. ☕
DeepSync took 30+ seconds to say the pronunciation is 'how you already say it,' then called it a joke. — Confidence Mismatch (45/100)
30 seconds to say 'you already know it,' then 'it's a joke'? That's a lot of processing for a non-answer. 🤡⏳
AI got lost and started talking about witches instead of pronunciation. Classic AI tangent 🧙♀️ — Just Vibes (50/100)
The AI went full 'Hansel and Gretel' when asked for pronunciation. Not a claim, just a funny fail 💀
Suggests AI improvement paths, making a speculative leap — Confidence Mismatch (45/100)
He's confidently suggesting specific fixes like 'verbosifizing the transcript' as if it's a known solution, not just a guess. 🔮✨
Acknowledges current model flaws but pivots to future 'flash version' improvements. — Volume Game (45/100)
Admits the current version is 'not beating' others, then immediately hypes up a future 'flash version.' Classic 'it's bad now but wait!' move 🙄
See the full analysis with sources and timestamps →