Deep Dive: How Three MoE Reasoning Models Actually Work — Trinity, DeepSeek R1, Kimi K2 - Part 2

Three frontier open-weight MoE reasoning models — Trinity Large Thinking (~400B), DeepSeek R1 (671B), and Kimi K2 Thinking (1T) — are compared side by side. Architecture, training, and post-training, explained from first principles. ⭐️⭐️⭐️ More content on Substack at https://www.airealist.ai ⭐️⭐️⭐️ In Part 1 (https://youtu.be/2uQQ8nKNq1U), I broke down how these three models are actually built — not benchmarks, not vibes, but the engineering decisions and why they matter. Now the question that actually matters: which one should you use? Benchmarks, costs, deployment, and practical details. *** Models Trinity Large Thinking: ~400B total, ~13B active, 256 experts, 512K context, Apache 2.0 https://huggingface.co/arcee-ai/Trinity-Large-Thinking NVFP4: https://huggingface.co/arcee-ai/Trinity-Large-Thinking-NVFP4 DeepSeek R1: 671B total, ~37B active, 256 experts, 128K context, MIT https://huggingface.co/deepseek-ai/DeepSeek-R1 Kimi K2 Thinking: 1T total, ~32B active, 384 experts, 256K context, Modified MIT https://huggingface.co/moonshotai/Kimi-K2-Instruct *** Papers & blogs Trinity architecture blog: https://www.arcee.ai/blog/trinity-large Trinity tech report: https://arxiv.org/abs/2602.17004 OpenRouter (all three): https://openrouter.ai/ #llm #moe #deepseek #reasoning #architecture #training #openweight #inference #arcee #kimi #trinity #mixtureofexperts