Comparing Minimax-M2.5 and GLM-5: Which Large Language Model is more cost-effective for OpenClaw

Author's Note: Comparing Minimax-M2.5 and GLM-5 across price, performance, and tool calling to help you pick the most cost-effective Large Language Model for OpenClaw.

OpenClaw, the most popular open-source AI Agent framework in early 2026, surpassed 175,000 GitHub Stars in less than two weeks. It can autonomously execute tasks via WhatsApp, Telegram, Slack, and more. However, your choice of model directly impacts both operating costs and task quality.

Core Value: By the end of this article, you'll know exactly whether Minimax-M2.5 or GLM-5 is better for your OpenClaw use cases and how to get the best results at the lowest cost.

Core Metrics for OpenClaw Model Selection

OpenClaw is an autonomous AI Agent. Unlike standard chat scenarios, it has specific requirements. Picking the wrong model can lead to failed tasks or skyrocketing token costs.

Minimum Requirements for OpenClaw

Based on OpenClaw community experience, the primary model should have at least 14B+ parameters (8B or smaller models often suffer from tool calling hallucinations). A context window of 32K+ is recommended. Both Minimax-M2.5 and GLM-5 far exceed these requirements. The real question is: Since they both work, which one gives you more bang for your buck?

Minimax-M2.5 vs. GLM-5: A Comprehensive OpenClaw Cost-Performance Showdown

Basic Model Specifications

Key Performance Comparison for OpenClaw

This table is the core basis for your choice—it directly impacts your experience and costs when using OpenClaw:

🎯 Key Finding: Minimax-M2.5's token price is only 38%-47% of GLM-5's, yet it performs comparably or even better in coding and tool calling—the two most critical capabilities for OpenClaw. This means for most OpenClaw users, M2.5 offers significantly higher value for money.

Model Selection Advice for Different OpenClaw Scenarios

Different use cases prioritize different model capabilities. Here are our recommendations for typical OpenClaw scenarios:

When to Choose Minimax-M2.5

• Daily Automation Tasks: For high-frequency, low-complexity tasks like scheduled messaging, calendar management, and file organization, M2.5's low price advantage really adds up over massive model invocations.
• Coding Assistance: A SWE-Bench score of 80.2% shows that M2.5 is excellent at code generation and debugging.
• Office Document Generation: M2.5 has an average win rate of 59% in Word/PPT/Excel tasks, making it ideal for document automation workflows.
• Budget-Sensitive Long-Term Runs: OpenClaw's Heartbeat scheduler periodically wakes the model to execute tasks. For long-term operations, M2.5 can save you over 50% in costs.

When to Choose GLM-5

• Complex Reasoning Tasks: With an AA Intelligence Index of 50 (compared to M2.5's 42), GLM-5 is more stable for multi-step reasoning and logical analysis.
• Knowledge-Intensive Q&A: With a 56% reduction in hallucination rates, GLM-5 is more trustworthy when tasks require highly reliable information output (like drafting emails or research summaries).
• Agent Chain Orchestration: Boasting an AA Agentic Index of 63 (the highest among open-source models), it's perfect for complex, multi-step Agent workflows.

Quick Start: Connecting OpenClaw to Minimax-M2.5 and GLM-5

Minimalist Example

With the APIYI platform, you can switch between the two models simply by changing the model parameter:

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://vip.apiyi.com/v1"
)

# Use Minimax-M2.5 (The cost-effective choice)
response = client.chat.completions.create(
    model="minimax-m2.5",
    messages=[{"role": "user", "content": "Help me write a Python script to monitor file changes in a specific directory"}]
)
print(response.choices[0].message.content)

import openai

client = openai.OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://vip.apiyi.com/v1"
)

# Use GLM-5 (Stronger reasoning capabilities)
response = client.chat.completions.create(
    model="glm-5",
    messages=[{"role": "user", "content": "Analyze the trends in this sales data and provide optimization suggestions"}]
)
print(response.choices[0].message.content)

# Tool calling example (A core capability of OpenClaw)
tools = [{
    "type": "function",
    "function": {
        "name": "run_shell_command",
        "description": "Execute a Shell command",
        "parameters": {
            "type": "object",
            "properties": {
                "command": {"type": "string", "description": "The command to execute"}
            },
            "required": ["command"]
        }
    }
}]

response = client.chat.completions.create(
    model="minimax-m2.5",
    messages=[{"role": "user", "content": "List all Python files in the current directory"}],
    tools=tools
)
print(response.choices[0].message)

Pro Tip: Get free test credits at APIYI (apiyi.com). Both models can be tested directly. The platform offers a default 12% discount (priced at 88% of official rates), which can significantly lower costs for long-term OpenClaw usage.

OpenClaw Model Cost Estimation

Let's take a typical OpenClaw user performing 50 Agent tasks per day as an example, with each task averaging 5,000 input tokens and 2,000 output tokens:

🎯 Cost Conclusion: Using Minimax-M2.5 with APIYI's 12% discount brings the monthly cost down to about $5—less than half that of GLM-5. For users on a budget who need to run OpenClaw long-term, M2.5 is the more practical choice.

Of course, if your tasks lean toward complex reasoning and high-reliability output, spending an extra $7 a month for GLM-5's superior reasoning and lower hallucination rate is a solid investment.

FAQ

Summary

Here’s the bottom line for choosing a model in OpenClaw:

1. Go with Minimax-M2.5 for pure value: It costs only 38%-47% of GLM-5's price while offering excellent coding and tool-calling capabilities. It’s the perfect fit for most daily OpenClaw scenarios.
2. Go with GLM-5 for reasoning quality: With a 19% higher AA intelligence index and a 56% lower hallucination rate, it’s the better choice for complex reasoning and knowledge-intensive tasks.
3. The best strategy is a hybrid approach: Use M2.5 for routine tasks to keep costs down, and switch to GLM-5 for complex tasks to ensure high-quality results.

Both models are now available on APIYI with a standard 12% discount off official prices. We recommend heading over to APIYI (apiyi.com) to grab some free test credits and see how these two Large Language Models perform in your own OpenClaw workflows.

📚 References

1. OpenClaw Official Docs: Model Provider Configuration Guide

   • Link: docs.openclaw.ai/concepts/model-providers
   • Description: Learn how to configure custom models within OpenClaw.

2. Minimax-M2.5 Official Intro: Tech Specs and Performance Data

   • Link: minimax.io/news/minimax-m25
   • Description: Check out the full benchmark results and architectural details for M2.5.

3. GLM-5 Tech Docs: Model Capabilities and API Reference

   • Link: docs.z.ai/guides/llm/glm-5
   • Description: Get the lowdown on GLM-5's reasoning capabilities and multimodal features.

4. Artificial Analysis Model Comparison: Independent Third-Party Benchmarks

   • Link: artificialanalysis.ai/models/comparisons/minimax-m2-5-vs-glm-5
   • Description: Objective data comparing performance, speed, and pricing.

Author: Technical Team
Join the Conversation: We'd love to hear about your experience choosing models for OpenClaw in the comments! For more insights on Large Language Models, visit APIYI at apiyi.com.