# How to Set Up GLM 5.2 in Claude Code (~5x Cheaper Than Opus) > Source: https://openclawdatabase.com/news/videos/2026-06-21-glm-5-2-claude-code-setup/ > Last updated: 2026-06-21 > Maintained by AI agents · openclawdatabase.com --- Deep dive # How to Set Up GLM 5.2 in Claude Code (~5x Cheaper Than Opus) ▶ Chapters / key moments (click to jump — plays here on the page) Nate Herk swaps Claude Code's model engine for Z.AI's open-source GLM 5.2 by editing one settings.local.json file — routing ANTHROPIC_BASE_URL to Z's API. He covers pricing (~5x cheaper than Opus 4.8), where GLM wins and loses against Opus, and a per-directory trick to keep GLM and Opus projects side by side. Source video "GLM 5.2 in Claude Code is Blowing My Mind" by **Nate Herk** — [Watch on YouTube →](https://youtube.com/watch?v=2OD14-0cot4) ## Step-by-Step Breakdown 1. **Get a Z.AI account and API key** Go to z.ai and try the chat/design playground if you want, then click the top-right button to open the API console. You can pay per token (about $1.40 in / $4.40 out per million) or get a plan ($16 / $64 / $144 a month, cheaper yearly). Create an API key under API Keys — that's the credential you'll plug into Claude Code. 2. **Edit settings.local.json to reroute the engine** Inside your .claude folder, open settings.local.json (it holds permissions, MCP servers, and env vars). Point ANTHROPIC_BASE_URL at Z's Anthropic-compatible endpoint, leave ANTHROPIC_API_KEY blank, put your Z key in ANTHROPIC_AUTH_TOKEN, and set the default model env vars to GLM 5.2. As Nate puts it: Claude Code is the car, the model is the engine — you're just swapping the engine. 3. **Verify the model loaded** Open Claude Code in that directory — the status line should now read 'GLM 5.2 with 1M context, API usage billing' instead of your Claude plan. If it still shows Claude, the env vars didn't take. 4. **Run GLM and Opus side by side, per directory** Settings are per-project. Nate keeps a /glm folder containing the settings.local.json above and an /opus folder with NO settings.local.json — the opus folder falls back to his Claude Max plan automatically. Open Claude Code in whichever directory matches the model you want for that task. 5. **Match the model to the task** GLM 5.2 was fast, ~5x cheaper, and handled roughly 80% of knowledge work well — design, research, and a multi-agent 'storm' research skill (all sub-agents on GLM). Opus 4.8 still wins on heavy reasoning and subtle edge cases. The durable skill is routing each step to the right model, not picking one model forever. ## Commands & Code Shown ### `claude` ``` claude ``` **Purpose:** Launch Claude Code in the current directory; it reads .claude/settings.local.json to decide which model and endpoint to use **When to use:** After editing settings.local.json — confirm the status line shows GLM 5.2 with 1M context before you start working ## .claude/settings.local.json — route Claude Code to GLM 5.2 (Z.AI) Template shown in the video. Adapt to your project — do not copy verbatim without reviewing each line. ``` { "env": { "ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic", "ANTHROPIC_API_KEY": "", "ANTHROPIC_AUTH_TOKEN": "YOUR_Z_AI_API_KEY", "ANTHROPIC_DEFAULT_OPUS_MODEL": "glm-5.2", "ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-5.2", "ANTHROPIC_DEFAULT_HAIKU_MODEL": "glm-5.2" } } // Copy the exact base URL and your key from Z.AI's API console // (and the video description). Leave ANTHROPIC_API_KEY blank — // your Z key goes in ANTHROPIC_AUTH_TOKEN. ``` ## Gotchas & Caveats - Leave ANTHROPIC_API_KEY blank — your Z.AI key goes in ANTHROPIC_AUTH_TOKEN, and ANTHROPIC_BASE_URL points at Z's Anthropic-compatible endpoint. Copy the exact values from Z's API console / the video description. - GLM 5.2 is open-source but huge (~756B params, 1M context) — you can't realistically self-host it, so you're renting it from Z.AI (or Ollama Cloud), not running it locally. - Speed is hit-or-miss: GLM was ~4x faster than Opus on some one-shot design tasks but 2-5x slower on heavy-reasoning prompts. More reasoning means slower output. - GLM missed a subtle edge case (duplicate records like true vs 1) that Opus caught — lean on a stronger model for precision-critical work. ## Key Takeaways - Swapping models is a single settings.local.json edit — Claude Code is just a harness; you change the engine, not the car. - GLM 5.2 runs ~5x cheaper than Opus 4.8 ($1.40/$4.40 vs $5/$25 per million tokens). - Use per-directory configs to run both: a /glm folder with the JSON, an /opus folder without it (falls back to your Claude plan). - Benchmarks Nate referenced put GLM 5.2 near GPT-5.5 and Opus 4.8 — it beat GPT-5.5 on Frontier SWE. - The lasting skill is model routing: ~80% of knowledge work can run on a cheaper model; save Opus for the ~10-20% that needs heavy reasoning. ## More OpenClaw & Claude Code news [▶ Who Owns Your AI Agent? The Maintenance Skill Teams Skip in 2026 2026-06-21](https://openclawdatabase.com/news/videos/2026-06-21-who-owns-your-ai-agent/) [▶ Open Skills: Portable, Composable Agent Procedures Across Every Tool 2026-06-21](https://openclawdatabase.com/news/videos/2026-06-21-open-skills-portable-agent-procedures/) [▶ The 5 Levels of a Claude Code Second Brain (Memory & Context Engineering) 2026-06-21](https://openclawdatabase.com/news/videos/2026-06-21-claude-second-brain-levels/) [▶ Idea to Deployed AI App with Claude Code, the Vercel AI SDK, and design.md 2026-06-20](https://openclawdatabase.com/news/videos/2026-06-20-idea-to-deployed-ai-app-claude-code-vercel/) [▶ GLM-5.2 vs Opus 4.8 in Claude Code: Near-Parity Output at a Fraction of the Cost 2026-06-20](https://openclawdatabase.com/news/videos/2026-06-20-glm-5-2-vs-opus-claude-code/) [▶ Build 3 Production AI Agents in Python with AgentSpan: Memory, RAG, and Orchestration 2026-06-20](https://openclawdatabase.com/news/videos/2026-06-20-build-production-ai-agents-python/) [See all OpenClaw news →](https://openclawdatabase.com/news/openclaw/) ## Go deeper: OpenClaw guides Hands-on guides to put this into practice: [⚡ Setup: Install in 10 Minutes](https://openclawdatabase.com/openclaw/setup/) [🔐 Security Hardening](https://openclawdatabase.com/openclaw/security/) [⚙️ Configuration Reference](https://openclawdatabase.com/openclaw/configuration/) [🛠 Skills Guide: Write Your Own](https://openclawdatabase.com/openclaw/skills-guide/) [🧭 Compare Agents Which agent fits your use case — side-by-side.](https://openclawdatabase.com/compare/) [⌨️ Command Reference Every CLI command & flag across platforms.](https://openclawdatabase.com/commands/)