Uploaded July 2026 | Updated September 2026, 2 weeks ago
Kimi K3: The First Open-Weights Model Setting the Frontier (3T Params, MOE, Agentic Coding)
I break down why Kimi K3 feels like a new “DeepSeek moment,” arguing it’s the first open-weights model that actually sets the frontier for specialized agentic coding. I cover its 3T-parameter scale, MOE architecture (896 experts, 16 routed per token), native multimodal vision, and 1M context window, plus how it ranks highly on the AI Index and excels in coding/web dev benchmarks like Terminal Bench 2.1 while lagging in general chat and some tests like Humanity’s Last Exam and hallucination rate. I explain why Kimi models are ecosystem-friendly bases for post-training (e.g., Cursor, Cognition/Windsor, Thinking Machines) and discuss frontier-level pricing, token efficiency, enterprise focus, and how this could pressure other labs to lower prices. I also preview upcoming comparisons vs Fable 5, GPT 5.6, Sol, and GLM 5.2.
LINKS:
kimi.com/blog/kimi-k3
artificialanalysis.ai/models/kimi-k3
My voice to text App: whryte.com
Website: engineerprompt.ai
RAG Beyond Basics Course:
prompt-s-site.thinkific.com/courses/rag
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https://tally.so/r/3y9bb0
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🦾 Discord: discord.com/invite/t4eYQRUcXB
☕ Buy me a Coffee: ko-fi.com/promptengineering
|🔴 Patreon: patreon.com/PromptEngineering
💼Consulting: calendly.com/engineerprompt/consulting-call
📧 Business Contact: engineerprompt@gmail.com
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💻 Pre-configured localGPT VM: bit.ly/localGPT (use Code: PromptEngineering for 50% off).
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00:00 Kimi K3 Breakthrough
01:21 Frontier But Specialized
02:02 Agentic Coding Benchmarks
03:06 Ecosystem And Post Training
04:45 Architecture And Context
05:43 Pricing And Token Efficiency
07:36 What It Means For Users
Kimi K3: The First Open-Weights Model Setting the Frontier (3T Params, MOE, Agentic Coding)
I break down why Kimi K3 feels like a new “DeepSeek moment,” arguing it’s the first open-weights model that actually sets the frontier for specialized agentic coding. I cover its 3T-parameter scale, MOE architecture (896 experts, 16 routed per token), native multimodal vision, and 1M context window, plus how it ranks highly on the AI Index and excels in coding/web dev benchmarks like Terminal Bench 2.1 while lagging in general chat and some tests like Humanity’s Last Exam and hallucination rate. I explain why Kimi models are ecosystem-friendly bases for post-training (e.g., Cursor, Cognition/Windsor, Thinking Machines) and discuss frontier-level pricing, token efficiency, enterprise focus, and how this could pressure other labs to lower prices. I also preview upcoming comparisons vs Fable 5, GPT 5.6, Sol, and GLM 5.2.
LINKS:
kimi.com/blog/kimi-k3
artificialanalysis.ai/models/kimi-k3
My voice to text App: whryte.com
Website: engineerprompt.ai
RAG Beyond Basics Course:
prompt-s-site.thinkific.com/courses/rag
Signup for Newsletter, localgpt:
https://tally.so/r/3y9bb0
Let's Connect:
🦾 Discord: discord.com/invite/t4eYQRUcXB
☕ Buy me a Coffee: ko-fi.com/promptengineering
|🔴 Patreon: patreon.com/PromptEngineering
💼Consulting: calendly.com/engineerprompt/consulting-call
📧 Business Contact: engineerprompt@gmail.com
Become Member: tinyurl.com/y5h28s6h
💻 Pre-configured localGPT VM: bit.ly/localGPT (use Code: PromptEngineering for 50% off).
Signup for Newsletter, localgpt:
https://tally.so/r/3y9bb0
00:00 Kimi K3 Breakthrough
01:21 Frontier But Specialized
02:02 Agentic Coding Benchmarks
03:06 Ecosystem And Post Training
04:45 Architecture And Context
05:43 Pricing And Token Efficiency
07:36 What It Means For Users
