Choosing the right LLM for your OpenClaw agents is one of the most important decisions you will make. Different models have vastly different tool calling reliability, and a model that fails 20% of the time will make your AI employees unreliable and frustrating.
The OpenClaw community regularly debates this — especially after reports of GLM-5 and other models failing on tool calls. This guide breaks down the real-world performance of every major LLM with OpenClaw's tool calling system.
Tool calling (also called function calling) is how OpenClaw agents take actions beyond generating text. When an agent needs to search the web, read a file, send an email, or query a database, it uses tool calls.
The LLM must:
Each step can fail. A model that is great at writing prose but poor at structured output will be a bad OpenClaw agent. This is why model choice matters so much.
Based on community testing and documented OpenClaw behavior with each model:
If you are experiencing GLM tool calling failures in OpenClaw, the community has found several workarounds:
# Instead of:
Tool: search_web
Description: Search the internet
# Use:
Tool: search_web
Description: Search the internet for current information.
Parameters:
query (string, required): The exact search query to use. Must be specific and concise.
max_results (integer, optional, default: 5): Number of results to return. Range: 1-10.
Return value: Array of {title, url, snippet} objects.GLM models struggle more as tool chains get longer. Break complex workflows into smaller steps, or use Claude/GPT-4o for agents that need to chain 4+ tools together.
The most pragmatic fix: use Claude 3.5 Haiku as your OpenClaw model. At similar cost to GLM API pricing but dramatically better tool calling:
# In your agent config or SOUL.md
provider: anthropic
model: claude-haiku-3-5
api_key: your-anthropic-keyA typical OpenClaw agent processing 100 messages per day with 2,000 tokens per interaction (input + output) costs approximately:
Claude 3.5 Sonnet: 100 * 2000 * $0.009/1000 = $1.80/day ≈ $54/month
GPT-4o: 100 * 2000 * $0.010/1000 = $2.00/day ≈ $60/month
Claude 3.5 Haiku: 100 * 2000 * $0.0012/1000 = $0.24/day ≈ $7.20/month
GPT-4o mini: 100 * 2000 * $0.00038/1000 = $0.076/day ≈ $2.28/month
Gemini 1.5 Flash: 100 * 2000 * $0.000113/1000 = $0.02/day ≈ $0.68/month
Ollama (local): $0 API cost (server electricity only)For most personal and small business use cases, Claude 3.5 Haiku at $7-10/month per agent hits the sweet spot of reliability and cost.
Ready to deploy OpenClaw agents with proper model configuration? CrewClaw lets you choose your model when setting up your AI employee and generates the complete configuration — including optimized SOUL.md tool descriptions tuned for your chosen LLM.
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Claude 3.5 Sonnet and GPT-4o consistently deliver the highest tool calling reliability — both above 95% success rate on complex multi-step tool chains. For cost-conscious setups, Claude 3.5 Haiku and GPT-4o mini are solid choices with good tool calling support at lower cost.
GLM-4's tool calling implementation differs from the OpenAI function calling spec that OpenClaw is optimized for. GLM models sometimes hallucinate tool parameters or fail to properly format tool responses. For critical tasks, stick to Claude or GPT-4o. GLM works better for simple, single-tool tasks.
Yes, but reliability varies by model. Llama 3.1 8B and Mistral 7B have reasonable tool calling support via Ollama. Smaller models (3B and below) often fail on complex tool chains. For production agents, use API models. Ollama works well for local development and testing.
In your agent's configuration, set the model parameter. For Claude: model: claude-3-5-sonnet-20241022. For GPT-4o: model: gpt-4o. For Gemini: model: gemini-1.5-pro. You can also specify this in your SOUL.md provider section. Different agents in the same OpenClaw instance can use different models.
Claude 3.5 Haiku offers the best price-to-reliability ratio for tool-calling agents. At $0.80/$4 per million tokens, it costs about 5-10x less than Sonnet with perhaps 10-15% lower tool calling reliability on complex tasks. For simple agents, Haiku is excellent value. GPT-4o mini is a close second.
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