ComparisonOpenClawLLMsTool Calling2026-03-11·10 min read

Best LLMs for OpenClaw Tool Calling: Claude, GPT-4o, Gemini & More (2026)

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.

What is Tool Calling in OpenClaw?

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:

  1. Decide which tool to call
  2. Format the correct parameters
  3. Parse the tool result and decide on the next action
  4. Chain multiple tool calls when needed

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.

Model Comparison: Tool Calling Reliability

Based on community testing and documented OpenClaw behavior with each model:

Tier 1: Excellent Tool Calling

  • Claude 3.5 Sonnet (claude-3-5-sonnet-20241022): Best overall for complex agents. Excellent at multi-step tool chains, rarely hallucinates parameters, and handles ambiguous tool descriptions well. Slightly slower but highly reliable. Cost: $3/$15 per million tokens.
  • GPT-4o: Very close to Sonnet in reliability. Excellent function calling, well-documented behavior, huge community knowledge base. Good choice if you prefer OpenAI. Cost: $5/$15 per million tokens.
  • Gemini 1.5 Pro: Strong tool calling, especially for tasks involving long context (documents, code). Large context window (1M tokens) is a unique advantage. Cost: $3.50/$10.50 per million tokens.

Tier 2: Good Tool Calling (Most Tasks)

  • Claude 3.5 Haiku (claude-haiku-3-5): Best value pick. About 5-10x cheaper than Sonnet with ~85-90% of the tool calling reliability. Excellent for high-volume agents or simple workflows. Cost: $0.80/$4 per million tokens.
  • GPT-4o mini: Similar value proposition to Haiku. Very fast responses, good for agents that do many small tool calls. Can struggle with complex nested tool chains. Cost: $0.15/$0.60 per million tokens.
  • Gemini 1.5 Flash: Fast and cost-effective, decent tool calling for straightforward workflows. Best Gemini option for high-frequency agents. Cost: $0.075/$0.30 per million tokens.

Tier 3: Inconsistent Tool Calling (Use Carefully)

  • GLM-4 / GLM-5: Community-reported issues with tool calling. GLM models sometimes fail to properly format tool parameters or get confused when tools return unexpected results. Usable for simple single-tool tasks but unreliable for complex agents. Reports of GLM-5 tool calling failures are common in the OpenClaw subreddit.
  • Mistral Large: Adequate tool calling for simple tasks. Can fail on complex multi-tool chains. Better results with clear, explicit tool descriptions in SOUL.md.
  • DeepSeek V3: Improving fast, but tool calling is inconsistent in OpenClaw. Community reports mixed results. Use with simple agents.

Tier 4: Limited Tool Support (Local Models via Ollama)

  • Llama 3.1 8B (via Ollama): Basic tool calling works for simple tasks. Fails often on complex multi-step chains. Good for development/testing.
  • Mistral 7B (via Ollama): Similar to Llama 3.1 8B. Works for basic tool calls. Not recommended for production agents.
  • Models under 7B: Generally too small for reliable tool calling. Frequent parameter hallucination. Avoid for agentic use.

Fixing GLM Tool Calling Issues

If you are experiencing GLM tool calling failures in OpenClaw, the community has found several workarounds:

1. Be Extremely Explicit in Tool Descriptions

# 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.

2. Reduce Tool Chain Complexity

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.

3. Switch to Claude for Critical Agents

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-key

Recommended Models by Use Case

  • Customer support agent (high volume, simple responses): Claude 3.5 Haiku or GPT-4o mini. Fast and cheap for simple tool lookups.
  • Research agent (complex analysis, web search chains): Claude 3.5 Sonnet. Handles complex tool chains reliably.
  • Document processing agent (long inputs): Gemini 1.5 Pro for large docs, Claude Sonnet for reliable output.
  • Coding agent (code generation + execution): Claude 3.5 Sonnet or GPT-4o. Both excel at code + tool use.
  • Budget setup (home lab, personal use): Claude 3.5 Haiku. Best balance of cost and reliability.
  • Private/offline (no cloud API): Llama 3.1 8B via Ollama. Accept lower reliability in exchange for privacy.

Cost Calculator: LLM Costs for Typical Agents

A 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.

Deploy Agents with Your Choice of Model

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.

Your AI employee is ready in 60 seconds with a model that actually works for tool calling.

Frequently Asked Questions

Which LLM has the best tool calling reliability with OpenClaw?

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.

Why is GLM-4 having tool calling issues in OpenClaw?

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.

Can I run a local Ollama model for OpenClaw tool calling?

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.

How do I switch the LLM for an OpenClaw agent?

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.

What is the cheapest LLM that still works reliably for OpenClaw?

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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