Using GLM-5.1 in OpenClaw: The Agentic Powerhouse for Complex Tasks

The Rise of Agentic AI

Most AI models are built for conversation. You ask, they answer. Simple enough. But when you need an AI to actually do things—to navigate terminals, chain tool calls, recover from errors, and maintain context across dozens of steps—that’s where most models fall apart.

GLM-5.1 was built specifically for this problem.

Zhipu AI’s latest mixture-of-experts model doesn’t just answer questions. It executes. Terminal operations, multi-step workflows, complex debugging sessions—the model was designed from the ground up for agentic workloads.

For OpenClaw users, this isn’t just another model option. It’s the engine your agents have been waiting for.

What is GLM-5.1?

GLM-5.1 is a 744-billion parameter mixture-of-experts (MoE) model from Zhipu AI, a leading Chinese LLM provider and Tsinghua University spin-off. Only 40 billion parameters activate during inference, making it remarkably cost-effective despite its massive scale.

Technical Specifications

Specification	Value
Total Parameters	744B
Active Parameters	40B
Pre-training Data	28.5T tokens
Context Window	128K+
Architecture	MoE with DeepSeek Sparse Attention
Languages	English, Chinese
License	MIT (fully open source)

What Sets It Apart

DeepSeek Sparse Attention (DSA): This isn’t standard attention. DSA dramatically reduces deployment costs while preserving the model’s ability to handle long contexts. You’re not sacrificing capability for efficiency—you’re getting both.

Asynchronous RL Training (slime): Zhipu AI developed a novel reinforcement learning infrastructure that substantially improved training throughput. The result: better reasoning with fewer training iterations.

Agentic Optimization: Unlike models trained primarily for chat, GLM-5.1 was explicitly optimized for multi-step tool execution, terminal navigation, and autonomous problem-solving.

Info: The MIT license is significant. Unlike Gemma 4’s more restrictive terms or Claude’s commercial restrictions, GLM-5.1 is fully open for any use case—commercial, personal, or research.

Benchmark Performance: The Numbers That Matter

GLM-5.1 excels where other models struggle—the agentic benchmarks that test real-world utility.

Terminal Operations (Terminal-Bench 2.0)

Model	Score	Verified
GLM-5.1	56.2%	60.7%
Claude Opus 4.5	59.3%	—
Kimi K2.5	50.8%	—
DeepSeek-V3.2	39.3%	—
GLM-4.7	41.0%	—

Web Browsing (BrowseComp)

Model	Base	With Context Management
GLM-5.1	62.0%	75.9%
Kimi K2.5	60.6%	74.9%
GLM-4.7	52.0%	67.5%
DeepSeek-V3.2	51.4%	67.6%
Claude Opus 4.5	37.0%	67.8%

Coding Performance (SWE-bench Verified)

Model	Score
Claude Opus 4.5	80.9%
GLM-5.1	77.8%
Kimi K2.5	76.8%
GLM-4.7	73.8%
DeepSeek-V3.2	73.1%

The pattern is clear: GLM-5.1 punches above its weight on tasks requiring autonomous execution, tool calls, and context management.

Quick Start: Get Running in Minutes

GLM-5.1 is available through Ollama’s cloud backend, making setup trivially simple.

# Cloud access via Ollama
ollama run glm-5:cloud

# In OpenClaw, switch models
/model glm-5:cloud

That’s it. No GPU requirements. No local deployment complexity. Just cloud access to a 744B parameter model optimized for agentic workloads.

Info: GLM-5.1 is also available for local deployment through vLLM, SGLang, KTransformers, and other frameworks. See the Local Deployment section for details.

Configuration for OpenClaw

The default settings work for basic use, but proper configuration unlocks GLM-5.1’s full potential.

Basic Configuration

Edit your OpenClaw configuration file (typically ~/.openclaw/openclaw.json or openclaw.toml):

{
  "models": {
    "providers": {
      "ollama": {
        "baseUrl": "http://localhost:11434/v1",
        "api": "openai-completions",
        "models": [
          {
            "id": "glm-5:cloud",
            "name": "GLM-5.1",
            "reasoning": true,
            "contextWindow": 131072,
            "maxTokens": 16384
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "ollama/glm-5:cloud"
      }
    }
  }
}

Profile-Based Configuration

For users who want GLM-5.1 as a specialized profile rather than the default:

# Example TOML configuration
[models.profiles.glm5]
model = "ollama/glm-5:cloud"
thinking = "high"
temperature = 1.0
contextWindow = 131072

[models.profiles.glm5-reasoning]
model = "ollama/glm-5:cloud"
thinking = "high"
temperature = 0.7
systemPrompt = "You are a careful, methodical reasoning engine. Think step by step."

Model Profile Setup

{
  "profiles": {
    "coding": {
      "model": "ollama/glm-5:cloud",
      "thinking": "high",
      "systemPrompt": "You are a senior software engineer. Write clean, idiomatic code with proper error handling."
    },
    "terminal": {
      "model": "ollama/glm-5:cloud",
      "thinking": "medium",
      "systemPrompt": "You are a terminal expert. Execute commands carefully, verify results, and recover from errors gracefully."
    },
    "reasoning": {
      "model": "ollama/glm-5:cloud",
      "thinking": "high",
      "temperature": 0.3
    }
  }
}

Warning: Context window matters. GLM-5.1 supports 128K+ tokens. For complex agentic sessions, set contextWindow to at least 65536. Lower settings will truncate context and break multi-step workflows.

What GLM-5.1 Excels At

The benchmarks tell one story. Real-world usage tells another.

Strengths

Terminal Navigation

GLM-5.1 doesn’t just generate shell commands—it understands the terminal as an environment. It reads output, interprets errors, adjusts its approach, and recovers from failures.

# Example: Debugging a failing service
> The nginx container won't start. Check the logs, identify the issue, and fix it.

[GLM-5.1 reads docker logs, identifies config error, fixes it, restarts container]

Multi-Step Tool Execution

When an AI needs to chain 10+ tool calls to complete a task, most models lose the thread. GLM-5.1 maintains coherence across long tool sequences, tracking state and adjusting as needed.

# Example: Complex file operations
> Find all TypeScript files using deprecated imports, update them to the new API, run tests, and commit only the passing changes.

Coding Agents

The SWE-bench scores reflect real capability. GLM-5.1 can work through multi-file codebases, identify bugs spanning multiple modules, and implement fixes that respect existing patterns.

# Example: Multi-file refactor
> The authentication module uses a deprecated password hashing library. Update all files to use argon2id, ensure backward compatibility with existing passwords, and add migration logic.

Chinese Language Tasks

As a bilingual model trained on both English and Chinese, GLM-5.1 handles Chinese-language queries with native fluency—valuable for international teams and documentation.

Autonomous Debugging

The CyberGym benchmark (43.2% vs GLM-4.7’s 23.5%) highlights GLM-5.1’s ability to work through problems independently, exploring solutions without constant human guidance.

Weaknesses

Multimodal Tasks

Unlike Gemma 4, GLM-5.1 doesn’t process images or audio natively. It’s a text-only model. If you need vision capabilities, pair it with a multimodal model.

Extended Context (>64K tokens)

While the 128K context window is generous, quality can degrade at the extremes. For conversations exceeding 64K tokens, consider periodic summarization or fresh sessions.

Non-English/Chinese Languages

The model was trained primarily on English and Chinese. Performance on other languages (Spanish, French, German, etc.) is usable but not optimized.

GLM-5.1 vs Gemma 4: Choosing the Right Model

Both models are excellent, but they serve different purposes.

Quick Comparison

Capability	GLM-5.1	Gemma 4 E4B
Architecture	MoE (744B/40B)	Dense (30.7B)
Context Window	128K	256K
Deployment	Cloud-first	Local + Cloud
Multimodal	Text only	Text, Image, Audio
Languages	EN, ZH	140+
License	MIT	Gemma Terms
Strength	Agentic tasks	Local efficiency

Use Case Recommendations

Use Case	Recommended Model	Why
Coding agents	GLM-5.1	Better multi-file coherence
Terminal tasks	GLM-5.1	Optimized for shell operations
Local/edge	Gemma 4 E4B	Runs on consumer hardware
Long documents	Gemma 4 31B	256K context window
Multimodal	Gemma 4	Vision and audio support
Chinese content	GLM-5.1	Native bilingual training
Cost-sensitive	Gemma 4	Free local inference
Commercial use	GLM-5.1	MIT license

The Hybrid Approach

The ideal OpenClaw setup uses both models strategically:

{
  "profiles": {
    "agentic": {
      "model": "ollama/glm-5:cloud",
      "thinking": "high"
    },
    "local": {
      "model": "ollama/gemma4:latest",
      "thinking": false
    }
  }
}

Workflow:

1. Use Gemma 4 for quick, simple tasks (file reads, small edits)
2. Escalate to GLM-5.1 for complex operations (terminal, multi-file, debugging)
3. Use GLM-5.1 when you need an agent to work autonomously

Local Deployment Options

For users with substantial GPU infrastructure, GLM-5.1 can be deployed locally.

Hardware Requirements

Configuration	GPUs	VRAM per GPU	Total VRAM
Minimum	8x	24 GB	192 GB
Recommended	8x	40 GB	320 GB
Optimal	8x	80 GB	640 GB

This is enterprise-scale hardware. Most OpenClaw users will prefer cloud access.

vLLM Deployment

vllm serve zai-org/GLM-5 \
  --tensor-parallel-size 8 \
  --gpu-memory-utilization 0.85 \
  --speculative-config.method mtp \
  --speculative-config.num_speculative_tokens 3 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --enable-auto-tool-choice \
  --served-model-name glm-5

SGLang Deployment

sglang serve \
  --model-path zai-org/GLM-5 \
  --tp-size 8 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.85 \
  --served-model-name glm-5

Supported Frameworks

• vLLM (v0.19.0+)
• SGLang (v0.5.10+)
• KTransformers (v0.5.3+)
• Transformers (v0.5.4+)
• xLLM (v0.8.0+)

Info: The --tool-call-parser glm47 and --reasoning-parser glm45 flags are essential. Without them, GLM-5.1’s function-calling and reasoning capabilities won’t integrate properly.

Real-World Example: Agentic Debugging Session

Here’s GLM-5.1 in action on a realistic OpenClaw task:

User: The build is failing. Check the CI logs, identify the issue, fix it, and verify the fix works.

GLM-5.1 (abbreviated output):

Reading CI logs from .github/workflows/ci.yml run #1247...

Error identified: TypeScript compilation failure in src/utils/parser.ts:142
- Type 'string | undefined' is not assignable to type 'string'
- Variable 'result.data' may be undefined

Checking related files:
- src/types/api.ts defines Response.data as optional
- src/utils/parser.ts assumes it's always present

Applying fix: Adding null check and fallback

[Edits src/utils/parser.ts]

Running type check locally...
✓ TypeScript compilation successful

Committing fix: "fix: handle optional data field in API response parser"

This kind of autonomous, multi-step problem-solving is exactly what GLM-5.1 was built for.

When to Use GLM-5.1 vs Alternatives

Use GLM-5.1 when:

• You need an agent to work autonomously on complex tasks
• Terminal/shell operations are central to your workflow
• Multi-file refactors are common
• You need strong Chinese language support
• License clarity matters (MIT)

Use Gemma 4 when:

• Local deployment is required
• Multimodal (image/audio) processing is needed
• 256K context is essential
• You’re working in non-English/Chinese languages
• Cost is the primary constraint

Use Claude/GPT-4 when:

• Maximum reasoning capability is needed
• Complex multimodal analysis is required
• You want the absolute best regardless of cost

Pricing and Access

GLM-5.1 is available through multiple channels:

Access Method	Cost	Notes
Ollama Cloud	Pay-per-use	Easiest setup
Z.ai API	Free tier: 20M tokens	docs.z.ai
Chat Interface	Free	chat.z.ai
Self-Hosted	Infrastructure only	Requires 8x GPU setup

The free tier on Z.ai is generous—20 million tokens covers substantial usage before any cost kicks in.

The Bottom Line

GLM-5.1 fills a specific gap in the OpenClaw ecosystem: it’s the model you reach for when you need an agent to actually do things, not just talk about them.

The recommendation:

1. Add GLM-5.1 to your OpenClaw configuration (ollama/glm-5:cloud)
2. Create profiles for agentic tasks vs. quick queries
3. Use GLM-5.1 as your escalation model for complex operations
4. Pair with Gemma 4 for local/simple tasks
5. Leverage the MIT license for commercial confidence

For OpenClaw users building agentic workflows, GLM-5.1 isn’t optional—it’s the engine that makes complex automation possible.

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Resources

• GLM-5 Technical Blog — Deep dive into architecture and training
• GLM-5 on Hugging Face — Model weights and documentation
• GLM-5 on Ollama — Quick start guide
• OpenClaw GitHub — Source code and issues
• OpenClaw Docs — Configuration reference
• Zhipu AI — Company and product information
• GLM-5 Paper — Technical paper

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Last updated: April 13, 2026