Congrats to the @Kimi_Moonshot team on this major milestone

@chase_ai_

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Why it worked

The video taps into the competitive landscape of AI models, highlighting a new contender that challenges established players. The "underdog" narrative of a Chinese model outperforming Western counterparts, combined with benchmark data, creates intrigue and shareability.

Summary

The video discusses the release and performance of Kimi-K3, a Chinese open-weight AI model. It highlights Kimi-K3's strong performance in various benchmarks, surpassing models like Fable 5 and GPT-5.6, while also noting its cost-effectiveness and large parameter count. However, it also points out that Kimi-K3 requires significant hardware and is slower due to its token usage.

Structure

1Introduction of Kimi-K3 and its ranking
2Comparison of Kimi-K3's performance against other models in benchmarks
3Discussion of Kimi-K3's cost and parameter size

Original caption

kimi k3

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Product placement

Kimi-K3: ACTS - it is a large language model that is being benchmarked and discussed for its performance and cost. Its performance is compared to other models like Fable 5, GPT-5.6, and GLM-5.2. Its cost and token usage are also discussed. Removing it would change the entire video's subject. Fable 5: ACTS - it is a large language model that Kimi-K3 is being compared against in benchmarks. Removing it would change the video's subject. GPT-5.6: ACTS - it is a large language model that Kimi-K3 is being compared against in benchmarks. Removing it would change the video's subject. GLM-5.2: ACTS - it is a large language model that Kimi-K3 is being compared against in benchmarks. Removing it would change the video's subject. Arena: APPEARS - it is a platform or website where models are ranked. Removing it would not change the video's content, only the context of the ranking. Arena.AI: APPEARS - it is a label associated with the Arena platform. Removing it would not change the video's content, only the context of the ranking. OpenAI SDK: ACTS - mentioned as a requirement for running examples. Removing it would change the information about how to use the model. Python 3.9+: ACTS - mentioned as a requirement for running examples. Removing it would change the information about how to use the model.

On-screen text

Congrats to the @Kimi_Moonshot team on this major milestone Frontend Code Arena Kimi-K3: Ranked #1 Arena ARENA.AI Kimi-K3 Claude Fable 5 GPT-5.6 Sol (xHigh) GLM-5.2 (Max) Claude Opus 4.8 (Thinking) This Chinese Model Beats Fable 5? so Kimi K3 DeepSWE Fable 5 Kimi K3 GPT-5.5 Opus-4.8 GLM-5.2 FrontierSWE Fable 5 Kimi K3 GPT-5.6 Sol GLM-5.2 GPT-5.5 Kimi Code Bench 2.0 (Internal) Fable 5 Kimi K3 Opus-4.8 GPT-5.5 GPT-5.6 Sol GLM-5.2 and when we at other benchmarks like Deep Swee Frontier Swee and Kimmy Code which is an internal benchmark for them its own Get Started Kimi K3 Introducing Kimi K3 Kimi K3 is Kimi's most capable model to date, with 2.8 trillion parameters. Built on a hybrid linear attention mechanism, and Attention Residuals, it offers understanding and a 1M-token context window for frontier intelligence scenario, software engineering, knowledge work, and deep reasoning. Get Started Playground Get an API Key a 2.8 trillion parameter and it is cheaper than both 5.6 and fable however even though it's open weight that doesn't mean you can run this requires serious hardware and one of its downsides is it's kind of a token hog while it's cheaper per million tokens in 5.6 or fable it uses way more tokens and much slower so it's something keep in mind but it's always when we have models that can compete with the big boys

Transcript

Kimi-K3 was just released today and it is a Chinese open weight model that is boasting numbers that actually beat Fable 5 and GPT 5.6 in certain categories. We can see here in the front-end code arena, it's actually scored above those two models. And when we take a look at other benchmarks like Deep Swee, Frontier Swee and Kimmy Code Bench 2.0 which is an internal benchmark for them, it's holding its own. Kimi K3 is a 2.8 trillion parameter model and it is cheaper than both 5.6 and fable. However, even though it's open weight, that doesn't mean you can run this requires serious hardware and one of its downsides is it's kind of a token hog. While it's cheaper per million tokens in 5.6 or fable, it uses way more tokens and is much slower. So it's something to keep in mind, but it's always when we have models that can compete with the big boys.