Which Chinese Open‑Source LLM Wins the Tech‑Selection Battle: GLM‑5, MiniMax‑M2.1 or Kimi‑K2.5?

Core Model Specifications

GLM-5 – Developed by Zhipu AI; MoE architecture; 744 B total parameters, 40 B activation parameters; pre‑trained on 28.5 T tokens; 200 K context window, 128 K max output; text‑only input; commercial license (negotiable).

MiniMax‑M2.1 – Developed by MiniMax; architecture not disclosed; parameter count not disclosed; activation parameters not disclosed; pre‑training data not disclosed; context window not disclosed; max output not disclosed; text‑only input; open‑source license.

Kimi‑K2.5 – Developed by Moonshot AI; MoE architecture with 1 T total parameters (384 experts, 8 active per token); 32 B activation parameters; pre‑trained on 15 T vision‑text tokens; 256 K context window, 64 K+ max output; supports text, image, and video input; Modified MIT license.

Programming Benchmark Performance

SWE‑bench Verified – GLM‑5: 77.8, MiniMax‑M2.1: ~70+, Kimi‑K2.5: 76.8.

Terminal Bench 2.0 – GLM‑5: 56.2, Kimi‑K2.5: 50.8 (MiniMax‑M2.1 not reported).

VIBE‑Web – MiniMax‑M2.1: 91.5 (GLM‑5 and Kimi‑K2.5 not reported).

VIBE‑Android – MiniMax‑M2.1: 89.7.

LiveCodeBench (v6) – Kimi‑K2.5: 85.0.

SWE‑Bench Multilingual – Kimi‑K2.5: 73.0.

Agent Capability Benchmarks

BrowseComp – GLM‑5 achieves open‑source SOTA; Kimi‑K2.5 reaches 78.4 with its Agent Swarm architecture.

MCP‑Atlas – GLM‑5 achieves open‑source SOTA (MiniMax‑M2.1 not reported).

τ²‑Bench – GLM‑5 achieves open‑source SOTA.

Agent Swarm latency – Kimi‑K2.5 reduces latency by 4.5× compared with single‑agent baselines.

Framework Compatibility – GLM‑5: strong; MiniMax‑M2.1: strongest (compatible with six major Agent frameworks); Kimi‑K2.5: strong.

MiniMax‑M2.1 Framework Compatibility Details

Claude Code – ✅ stable

Cline – ✅ stable (platform‑popular model)

Droid (Factory AI) – ✅ stable

Kilo Code – ✅ stable

Roo Code – ✅ stable

BlackBox – ✅ stable

OpenClaw Installation & Startup

# macOS/Linux
curl -fsSL https://openclaw.ai/install.sh | bash

# Windows (PowerShell)
iwr -useb https://openclaw.ai/install.ps1 | iex

# Install & configure daemon
openclaw onboard --install-daemon

# Check gateway status
openclaw gateway status

# Open dashboard
openclaw dashboard

Configure Multiple Model Agents in OpenClaw

{
  "agents": {
    "list": [
      {
        "id": "glm5-coding",
        "name": "GLM-5 编程助手",
        "workspace": "~/.openclaw/workspace-glm5",
        "model": "zhipu/glm-5"
      },
      {
        "id": "minimax-fullstack",
        "name": "MiniMax 全栈开发",
        "workspace": "~/.openclaw/workspace-minimax",
        "model": "minimax/m2.1"
      },
      {
        "id": "kimi-agent",
        "name": "Kimi 智能体",
        "workspace": "~/.openclaw/workspace-kimi",
        "model": "moonshot/kimi-k2.5"
      }
    ]
  },
  "bindings": [
    { "agentId": "glm5-coding", "match": { "channel": "whatsapp" } },
    { "agentId": "minimax-fullstack", "match": { "channel": "telegram" } },
    { "agentId": "kimi-agent", "match": { "channel": "discord" } }
  ]
}

Common Pitfalls

API token cost mis‑estimation – GLM‑5’s “Thinking Mode” can consume up to five times more tokens than normal mode, inflating cost for long‑running or high‑frequency calls.

Compatibility is not full‑feature – MiniMax‑M2.1 claims six‑framework compatibility, but advanced features (e.g., streaming control) may fail on Cline.

Multimodal boundaries – Kimi‑K2.5 handles simple UI mockups well; complex multi‑layer designs produce inconsistent code quality.

Context length trade‑off – Filling the 256 K context of Kimi‑K2.5 degrades response speed and can confuse the model.

Deployment cost – GLM‑5 (744 B) requires hundreds of GB VRAM; MiniMax‑M2.1’s hidden size likely incurs similar hardware costs.

Reference Resources

GLM‑5 documentation: https://docs.bigmodel.cn/cn/guide/models/text/glm-5 GLM‑5 ModelScope: https://www.modelscope.cn/models/ZhipuAI/GLM-5 MiniMax‑M2.1 announcement: https://www.minimaxi.com/news/minimax-m21 MiniMax‑M2.1 ModelScope: https://www.modelscope.cn/models/MiniMax/MiniMax-M2.1 Kimi‑K2.5 HuggingFace: https://huggingface.co/moonshotai/Kimi-K2.5 Kimi‑K2.5 ModelScope: https://www.modelscope.cn/models/moonshotai/Kimi-K2.5 Kimi‑K2.5 paper: https://arxiv.org/abs/2602.02276 OpenClaw documentation:

https://docs.openclaw.ai