OpenClaw is most powerful when you treat it like an agent: multi-step planning, repeated tool calls, long context, retries, and long-running tasks. That's also exactly the kind of workflow GLM-4.7 is designed to handle, with stable multi-step reasoning and execution, modern tool-call primitives, and a 200K context window (with up to 128K max output).
Additionally, OpenClaw is explicitly documented as a GLM Coding Plan supported tool in z.ai's Tool Guide (not a "hacky workaround" that may get cut off without warning).
Proof from Code Arena (real-world, human-voted agentic coding)
If you want an independent signal beyond vendor marketing, Arena's Code Arena is one of the best public "in-the-wild" indicators available right now.
What Code Arena is measuring (why it matters):
- It evaluates agentic coding, where models plan and execute multi-step builds using tool calls in controlled environments, and humans vote on which result is better.
- Scores are derived from large-scale pairwise votes using a Bradley-Terry system (similar in spirit to Elo), and Arena publishes uncertainty via confidence intervals and rank spread.
Where GLM-4.7 ranks (and what it's ahead of)
On the Code Arena → WebDev leaderboard snapshot dated Feb 9, 2026:
- GLM-4.7 is ranked #9 out of 40 models
- Score: 1441 (rank spread 6-10)
- Votes: 5,130
For context, in that same snapshot GLM-4.7 sits ahead of:
- OpenAI GPT-5.2 (rank #13, score 1397)
- Claude Sonnet 4.5 (rank #18, score 1386)
And the very top of the leaderboard is dominated by Claude Opus variants (for example, #1 is Claude Opus 4.6 Thinking at 1576).
If your goal is "best possible model regardless of cost," Claude Opus may still win. But if your goal is “a model that performs like a frontier coding agent and is practical to run continuously in OpenClaw,” GLM‑4.7 is a very rational default.
Subscription economics that fit OpenClaw usage (why this matters more than per-token pricing)
OpenClaw can be extremely token-hungry in real workflows because it's not just answering once — it's looping: planning, tool calls, edits, tests, retries, and follow-ups.
My real usage (OpenClaw-only)
I'm on z.ai's $30/month Pro plan, month-to-month (I received 50% off the first month). From Feb 1 to Feb 10, OpenClaw alone used 200,000,000+ tokens — and it was fully covered by the subscription with no additional API fees.
That's the point: for agentic workflows, what you want is a plan that can absorb real usage without you micromanaging token burn.
How z.ai frames the plan limits (so users know what to expect)
z.ai describes the Coding Plan in terms of prompt quotas that refresh every 5 hours (not token billing in supported tools). In their docs, Pro is listed at roughly ~600 prompts per 5 hours, and they note token usage can add up to very large totals depending on how many tool/model calls each prompt triggers.
They also state:
- When you use the Coding Plan inside supported tools, you use the plan quota, and if it's exhausted it refreshes at the next 5‑hour cycle.
- The system does not auto-consume your account balance/resource packs when the plan quota is exhausted; you wait for refresh.
- API calls are billed separately and do not use the Coding Plan quota; the plan is intended for supported coding/IDE tools.
This lines up with why GLM‑4.7 + OpenClaw feels so “set it and forget it”: you're operating inside a usage model that's built for continuous agent work.
Why GLM‑4.7 specifically works well inside OpenClaw
- Long context + big outputs for real refactors. GLM‑4.7 lists 200K context and up to 128K output, which is exactly what you want when OpenClaw is juggling logs, diffs, multi-file edits, and long instructions.
- Better fit for multi-step agents, not just one-shot completions. GLM‑4.7 is positioned around “task completion” and stable multi-step execution — the kind of behavior that matters when OpenClaw is acting as a builder, not a chatbot.
- OpenClaw is actually in the supported tool ecosystem. z.ai's docs explicitly include OpenClaw in the Coding Plan Tool Guide, and provide configuration steps.
When I'd still choose Claude or OpenAI instead
GLM‑4.7 is my default for OpenClaw coding agents, but there are cases where I'd switch:
- You need the absolute highest-end performance on the hardest planning/orchestration tasks (especially if you're trying to minimize retries) — top Claude Opus variants often lead Code Arena.
- You want provider-specific platform features (enterprise governance, proprietary tooling, internal workflow features) that are unique to a given vendor.
- You're building a SaaS/product that calls models directly via API — z.ai's Coding Plan is for supported tools; API usage is separate.