---
title: "GLM-5 for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using GLM-5 with OpenClaw. Costs $0.80/M input, $2.56/M output. 203K context window. Here's when to use it and when to pick something else."
date: 2026-03-12
updatedDate: 2026-03-12
image: /images/models/provider-z-ai-hero.jpg
keywords: "glm-5 openclaw, glm-5 pricing, glm-5 review, zhipu ai openclaw, glm-5 api"
category: model-guide
modelData:
  provider: "Zhipu AI"
  provider_slug: "z-ai"
  full_id: "z-ai/glm-5"
  input_cost_per_1m: 0.7999999999999999
  output_cost_per_1m: 2.56
  max_input_tokens: 202752
  max_output_tokens: 128000
  features: ["function_calling", "reasoning"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the exact cost of using GLM-5?"
    answer: "Input costs $0.80 per million tokens and output costs $2.56 per million tokens."
  - question: "How much context can it actually handle?"
    answer: "It supports a 203K token context window and can generate up to 128K tokens in a single response."
---

Current as of March 2026. GLM-5 is Zhipu's top-tier model: $0.80/$2.56 per million tokens, 203K context, and 128K output. The jump from GLM-4.7's 64K output ceiling to GLM-5's 128K is the main reason to pay the higher price. If you need to generate very large artifacts from a large context, GLM-5 is where that combination becomes available in this family.

## Specs

| | |
|---|---|
| **Provider** | Zhipu AI |
| **Input cost** | $0.80 / M tokens |
| **Output cost** | $2.56 / M tokens |
| **Context window** | 203K tokens |
| **Max output** | 128K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, reasoning |

## What it's good at

#### 128K Output Capacity

This is the main differentiator over the rest of the GLM family. Generating full codebases, comprehensive technical documentation, or large transformation tasks that need long continuous output.

#### Reasoning

Compared to the flash and 4.6/4.7 models, GLM-5's reasoning is more reliable on multi-step logic. It's not GPT-4o level, but it stays on task better through complex tool chains.

#### 203K Context + 128K Output Combination

Big in, big out. That's a useful combination for transformation pipelines where you don't want to chunk input or truncate output.

## Where it falls short

#### Reasoning Latency

Built-in reasoning adds time before the first token. For background tasks this is fine; for anything interactive it's noticeable.

#### Cultural Bias

On ambiguous prompts, it occasionally defaults to Chinese cultural contexts or idioms. Worth testing on your specific content types.

## Best use cases with OpenClaw

- **Long-form Content Generation** — 128K out means you won't hit a truncation wall mid-document.
- **Complex Agentic Workflows** — Reasoning and function calling together handle OpenClaw's tool loops more reliably than the cheaper models in this family.

## Not ideal for

- **Real-time Chatbots** — The reasoning overhead makes it too slow for responsive user interactions.
- **Simple Classification** — You're paying for reasoning capability you don't need. GLM-4.7 Flash does basic labeling for a fraction of the price.

## Run it through Haimaker

Skip juggling API keys. One Haimaker key gives you access to every model on the platform. Tell OpenClaw:

```
Add Haimaker as a custom provider to my OpenClaw config. Use these details:

- Provider name: haimaker
- Base URL: https://api.haimaker.ai/v1
- API key: [PASTE YOUR HAIMAKER API KEY HERE]
- API type: openai-completions

Add the auto-router model:
- haimaker/auto (reasoning: false, context: 128000, max tokens: 32000)

Create an alias "auto" for easy switching. Apply the config when done.
```

Or skip model selection entirely — Haimaker's [auto-router](/blog/openclaw-auto-router-setup/) picks the best model for each task so you don't have to.

## OpenClaw setup

Configure your provider base URL to api.haimaker.ai/v1 and use the model ID z-ai/glm-5. Ensure your timeout settings are high enough to accommodate the reasoning phase before output begins.

```json
{
  "models": {
    "mode": "merge",
    "providers": {
      "z-ai": {
        "baseUrl": "https://api.haimaker.ai/v1",
        "apiKey": "YOUR-Z-AI-API-KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "glm-5",
            "name": "GLM-5",
            "cost": {
              "input": 0.7999999999999999,
              "output": 2.56
            },
            "contextWindow": 202752,
            "maxTokens": 128000
          }
        ]
      }
    }
  }
}
```

## How it compares

- **vs GPT-4o-mini** — 4o-mini is cheaper for high-volume simple tasks but caps at 128K context and 16K output. GLM-5 wins on both.
- **vs Claude 3 Haiku** — Haiku is faster for short responses. GLM-5's 128K output ceiling is in a different class for document generation tasks.

## Bottom line

Use GLM-5 when you need the full combination of large context, large output, and reasoning — and you can't justify the price of Western frontier models. For simpler tasks, GLM-4.7 Flash at $0.07/M is the better call.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=glm-5-openclaw" class="cta-button">TRY GLM-5 ON HAIMAKER</a>

---

*For setup instructions, see our [API key guide](/blog/openclaw-api-key-setup). For all available models, see the [complete models guide](/blog/best-models-for-openclaw).*