{
  "id": 15863409,
  "title": "Kimi K3 vs GLM-5.3 vs DeepSeek-V4: Architecture, Benchmarks and Deployment",
  "url": "https://urgent.news/2026/10/21/kimi-k3-vs-glm-5-3-vs-deepseek-v4-architecture-benchmarks-and",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-21T08:34:21.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/ifnodoraemon/kimi-k3-vs-glm-53-vs-deepseek-v4-architecture-benchmarks-and-deployment-2oi2"
  },
  "original_language": "en",
  "account": "The report provides an in-depth analysis of three large language models: Kimi K3, GLM-5.3, and DeepSeek-V4-Pro. These models are compared based on their architecture, performance benchmarks, and deployment options.\n\nKimi K3 utilizes a hybrid approach combining 69 KDA layers with 24 Gated MLA layers, allowing for long context handling up to 1M tokens. DeepSeek-V4-Pro incorporates Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) techniques, and features shared experts for its configuration. GLM-5.3, on the other hand, builds upon the GLM-5.2 base model and enhances it through post-training.\n\nThe models exhibit varying parameter counts, with Kimi K3 boasting 2.8 trillion parameters, GLM-5.3 at approximately 753 billion, and DeepSeek-V4-Pro at 1.6 trillion. The active parameters for Kimi K3 are measured at 104 billion, while DeepSeek-V4-Pro utilizes a mixed precision approach with FP4/F8 formats.\n\nWhen comparing benchmark results, it is important to note that the values come from the model cards rather than independent reproductions. For instance, Terminal-Bench 3.0 scores for Kimi K3, GLM-5.3, and DeepSeek-V4-Pro are 17.4, 28.3, and not reported respectively. These scores are specific to certain task versions and may not indicate a regression or superiority across all tasks.\n\nIn terms of deployment, the choice of model depends on input modality and available hardware kernels. Each model has its unique requirements, such as DeepSeek-V4-Pro's native FP4/F8 mix and Kimi K3's MXFP4 format. The report emphasizes the need to consider total weights for MoE capacity, API input/output handling, and cost measurement when deploying these models.",
  "summary": "[!NOTE] Executive Summary (TL;DR) Domestic LLM Engineering Teardown : Rigorously analyzes DeepSeek-V4, Moonshot Kimi K3, Zhipu GLM-5.3 (~753B parameter class), and MiniMax across real coding, reasoning, and production deployments. Architectural Breakthroughs : DeepSeek-V4 features CSA+HCA hybrid attention while Kimi K3 deploys 69 KDA + 24 Gated MLA layers, redesigning traditional Transformer…",
  "key_points": [
    "Kimi K3 combines 69 KDA layers with 24 Gated MLA layers for long context handling up to 1M tokens",
    "GLM-5.3 builds upon GLM-5.2 base model and enhances it through post-training"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}