GLM-5.1 Is Here: #1 on SWE-Bench Pro and How to Run It with OpenClaw

GLM-5.1 by the Numbers

The benchmarks are hard to ignore. On SWE-Bench Pro — the toughest standardized test for software engineering — GLM-5.1 leads the field.

Try GLM-5.1 with OpenClaw

GLM-5.1 is available via Ollama with the glm-5.1:cloud tag. The :cloud suffix routes to the hosted Z.ai endpoint — you get the full 744B model without needing local hardware.

ollama launch openclaw --model glm-5.1:cloud

ollama launch claude --model glm-5.1:cloud

ollama run glm-5.1:cloud

If you already have agents running with another model, switching to GLM-5.1 is a single line change in your config.yaml. The SOUL.md, tools, and memory stay the same.

# Before
model: claude-sonnet-4-20250514
provider: anthropic

# After — switch to GLM-5.1 cloud
model: glm-5.1:cloud
provider: ollama

Why GLM-5.1 Matters for Agent Builders

The SWE-Bench Pro result is the headline, but the cost story is where it gets interesting for production deployments. Getting 94.6% of Claude Opus 4.6 coding performance at $3-$10/month changes the math for agents that run coding tasks repeatedly.

The Architecture: 744B MoE, Zero NVIDIA

GLM-5.1 uses a Mixture-of-Experts architecture. 744 billion total parameters spread across 256 experts. Only 8 experts — roughly 40 billion parameters — activate for each token. This means the model runs at the quality of a 744B dense model while using the compute of a 40B model at inference time.

The entire training run used 100,000 Huawei Ascend 910B chips. No NVIDIA. This is the most significant large-scale training result outside of the US GPU stack, and it produced a model that tops the hardest coding benchmark in the industry.

The 28% Jump: Post-Training, Not a New Model

GLM-5.1 uses the same base weights as GLM-5. The 28% improvement in coding performance — from a score of 35.4 to 45.3 — came entirely from post-training optimization. Z.ai used a progressive alignment pipeline:

The takeaway for agent builders: if you were running GLM-5 agents, upgrading to GLM-5.1 is a model tag swap. No architectural changes, no SOUL.md rewrites. You get a 28% better model with the same interface.

The Case for Model-Agnostic Agent Architecture

GLM-5.1 topping SWE-Bench Pro is a reminder that model rankings shift. Three months ago Claude Opus 4.6 led coding benchmarks. Today GLM-5.1 leads. In three months something else may lead. Agents built around a specific model API are fragile.

The right structure separates identity (SOUL.md), tools, and the model into independent layers. When the model layer is a single config line, you can follow performance improvements without rebuilding anything.

agent/
├── SOUL.md           # Identity: role, personality, rules
├── config.yaml       # Model layer — change one line
│   └── model: glm-5.1:cloud     # ← previously claude-sonnet
├── tools/            # Integrations (unchanged)
│   ├── github-api
│   ├── web-search
│   └── slack-webhook
└── memory/           # Knowledge (unchanged)
    └── context.md

# Switch to GLM-5.1 without changing anything else:
#   model: claude-sonnet-4-20250514  →  model: glm-5.1:cloud
#   model: gpt-5.4                   →  model: glm-5.1:cloud
#   model: ollama/qwen3              →  model: glm-5.1:cloud

Build GLM-5.1 Agents with CrewClaw

CrewClaw generates a complete, deployable agent package. Pick GLM-5.1 as the model, design the agent in the visual builder, and download a zip with SOUL.md, config.yaml, Docker setup, and a Telegram bot. No subscription, no lock-in, you own the files.

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