KIMI K2.6: 1T Monster AI?

Discover AI · Beginner ·📄 Research Papers Explained ·3w ago
We have a new AI model, KIMI K2.6. As published today. A 1T MoE AI model with 32B activated parameters. I perform my classical elevator test, a pure causal reasoning test and examine in detail the thinking /reasoning traces of both models in parallel. Platform for my tests is (free) arena.ai, where you can duplicate all my tests for free or perform your own, domain specific test with your task complexity. At the end I have a clear recommendation if you should update to K2.6 Thinking (Agent, Instant, Agent Swarm) or not. Compare KIMI K2.6 to my Qwen3.6-35B-A3B video (identical test) here https://youtu.be/Gnk-me1UqfE All my reasoning tests (on all AI models) as Playlist https://www.youtube.com/playlist?list=PLgy71-0-2-F0Rla8lu5ZldpYQUfXM_5bT K2.6 on HuggingFace https://huggingface.co/moonshotai/Kimi-K2.6 Quantized K2.6 Here (GGUF, Smart Quant, MLX) https://huggingface.co/models?other=base_model:quantized:moonshotai/Kimi-K2.6 Download and API here https://www.kimi.com/blog/kimi-k2-6 Additional info https://www.moonshot.ai/ Access to K2.6 Instant, K2.6 Thinking, K2.6 Agent and K2.6 Agent Swarm Kimi Claw here at https://www.kimi.com/ 00:00 KIMI K2.6-1T-A32B MOE 01:07 LIVE TEST KIMI K2.6 04:55 Result by KIMI K2.5 06:48 CRASH by KIMI K2.6 09:42 Result by KIMI K2.6 11:01 Validation run K2.6 K2.5 14:40 Validation K2.6 Successful 16:47 Validation K2.5 Fails 17:43 New validation K2.5 19:52 Find new Strategy #kimiai #aitesting #aiexplained #aireasoning #moonshot #moonshotai
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

The ABCs of reading medical research and review papers these days
Learn to critically evaluate medical research papers by accepting nothing at face value, believing no one blindly, and checking everything
Medium · LLM
#1 DevLog Meta-research: I Got Tired of Tab Chaos While Reading Research Papers.
Learn to manage research paper tabs efficiently and apply meta-research techniques to improve productivity
Dev.to AI
How to Set Up a Karpathy-Style Wiki for Your Research Field
Learn to set up a Karpathy-style wiki for your research field to organize and share knowledge effectively
Medium · AI
The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
Scientific knowledge may be stuck in a local minimum, hindering optimal progress, and understanding this concept is crucial for advancing research
ArXiv cs.AI

Chapters (10)

KIMI K2.6-1T-A32B MOE
1:07 LIVE TEST KIMI K2.6
4:55 Result by KIMI K2.5
6:48 CRASH by KIMI K2.6
9:42 Result by KIMI K2.6
11:01 Validation run K2.6 K2.5
14:40 Validation K2.6 Successful
16:47 Validation K2.5 Fails
17:43 New validation K2.5
19:52 Find new Strategy
Up next
Microsoft Research Forum | Season 2, Episode 4
Microsoft Research
Watch →