DEV Community

zac
zac

Posted on • Originally published at remoteopenclaw.com

Best GLM Models for OpenClaw — Zhipu AI's ChatGLM Series

#claude #ai #productivity #tutorial

Originally published on Remote OpenClaw.

The best GLM model for OpenClaw as of April 2026 is GLM-5 for frontier-level coding and agentic work, and GLM-4.7-Flash for a free, lightweight local default. Zhipu AI's GLM family gives OpenClaw operators a strong bilingual option — especially for workflows that involve Chinese-language content — with API pricing that undercuts most Western frontier models by a wide margin.

Key Takeaways

  • GLM-5 is a 744B-parameter MoE model (40B active) with a 200K context window, scoring 77.8% on SWE-bench Verified — strong enough for serious agentic coding through OpenClaw.
  • GLM-4.7-Flash is free on Zhipu's API with 203K context and only 3B active parameters, making it the best zero-cost entry point for OpenClaw operators.
  • GLM-4.7 sits between the two at $0.39/M input and $1.75/M output tokens — a practical mid-tier option for production workloads.
  • All GLM models are accessed in OpenClaw through the zai provider, with model IDs like zai/glm-5.
  • Zhipu's strongest differentiator is bilingual Chinese-English performance, which matters if your OpenClaw workflows touch Chinese markets, documents, or communication.

Part of The Complete Guide to OpenClaw — the full reference covering setup, security, memory, and operations.

In this guide

  1. GLM Model Overview for OpenClaw
  2. Model Comparison: GLM-5 vs GLM-4.7 vs GLM-4.7-Flash
  3. API Setup for OpenClaw
  4. Chinese Language Strengths
  5. Cost Breakdown
  6. Limitations and Tradeoffs
  7. FAQ

GLM Model Overview for OpenClaw

Zhipu AI is a Beijing-based AI company spun out of Tsinghua University that develops the GLM (General Language Model) family. As of April 2026, the lineup includes three tiers relevant to OpenClaw operators: GLM-5 as the frontier model, GLM-4.7 as the production workhorse, and GLM-4.7-Flash as the free lightweight option.

The GLM family uses a Mixture-of-Experts (MoE) architecture, which means the total parameter count is much larger than the active parameters used per token. GLM-5, for example, has 744B total parameters but only activates 40B per inference step — roughly doubling the capacity of GLM-4.5's 355B total and 32B active parameters. This architecture keeps inference costs manageable while delivering strong benchmark results across coding, math, and knowledge tasks.

OpenClaw accesses GLM models through the Z.AI provider, using the zai provider ID. The official OpenClaw documentation lists GLM as a first-class provider with straightforward API key configuration.

Model Comparison: GLM-5 vs GLM-4.7 vs GLM-4.7-Flash

GLM-5 leads the family in raw capability, but the right choice depends on whether you need frontier performance, cost efficiency, or zero-cost experimentation.

Model

Parameters (Total / Active)

Context Window

Input Cost / 1M Tokens

Output Cost / 1M Tokens

Best For

GLM-5

744B / 40B

200K

$1.00

$3.20

Agentic coding, complex reasoning

GLM-4.7

~300B / ~30B

202K

$0.39

$1.75

Production workloads, balanced cost

GLM-4.7-Flash

30B / 3B

203K

Free

Free

Prototyping, light tasks, local via Ollama

As of April 2026, GLM-5 scores 77.8% on SWE-bench Verified and 92.7% on AIME 2026, placing it in the top 20 across coding, math, and agentic benchmarks. GLM-4.7 lacks public SWE-bench scores at the same level but remains a strong general-purpose model at roughly one-third the cost of GLM-5.

GLM-4.7-Flash is the outlier: Zhipu offers it at zero cost with 203K context, making it the cheapest way to test GLM models with OpenClaw. The tradeoff is that only 3B parameters are active per token, so it will struggle with complex multi-step reasoning compared to GLM-5 or even GLM-4.7.

API Setup for OpenClaw

OpenClaw connects to GLM models through the Z.AI provider using an API key from Zhipu's open platform.

To configure the connection, register for a Z.AI account, generate an API key from the dashboard, and either run the OpenClaw setup wizard or add the provider block manually:

# Option 1: Use the setup wizard
openclaw configure

# Option 2: Set the environment variable directly
export ZAI_API_KEY="your-api-key-here"
Enter fullscreen mode Exit fullscreen mode

Once the provider is configured, reference models using the zai/ prefix:

# Use GLM-5 for agentic work
openclaw --model zai/glm-5

# Use GLM-4.7-Flash for free lightweight tasks
openclaw --model zai/glm-4.7-flash
Enter fullscreen mode Exit fullscreen mode

If you prefer running GLM-4.7-Flash locally through Ollama instead of the API, the model is available in the Ollama library under MIT license:

ollama run glm-4.7-flash
ollama launch openclaw --model glm-4.7-flash
Enter fullscreen mode Exit fullscreen mode

Marketplace

Free skills and AI personas for OpenClaw — browse the marketplace.

Browse the Marketplace →

Chinese Language Strengths

GLM models are trained by Zhipu AI with a strong emphasis on bilingual Chinese-English performance, which is the family's clearest differentiator from Western alternatives like Claude or GPT.

This matters for OpenClaw operators who need to:

  • process Chinese-language documents, contracts, or customer communications,
  • generate content for Chinese markets,
  • handle bilingual workflows where the agent switches between Chinese and English within the same session,
  • work with Chinese codebases, comments, or documentation.

The ChatGLM lineage — starting with ChatGLM3-6B, a model jointly developed with Tsinghua University's KEG lab — was specifically designed for dialogue fluency in Chinese. The current GLM-4.7 and GLM-5 models inherit this bilingual foundation while adding much stronger reasoning and coding capabilities.

If your OpenClaw workflows are entirely English-only and you do not care about Chinese language support, this advantage becomes less relevant and you should weigh GLM purely on price and benchmark performance against alternatives like DeepSeek or Claude.

Cost Breakdown

GLM pricing is significantly cheaper than most Western frontier models. As of April 2026, GLM-5 costs approximately $1.00 per million input tokens and $3.20 per million output tokens — roughly 5x cheaper on input and 8x cheaper on output than Claude Opus, even after the 30% price increase in February 2026.

Zhipu also offers a Coding Plan with three subscription tiers billed quarterly:

Plan

Quarterly Price

Monthly Equivalent

Includes

Lite

$30

~$10

GLM-4.7 access, basic quota

Pro

$90

~$30

GLM-5 access, higher quota

Max

$240

~$80

GLM-5 priority, highest quota

For operators testing the waters, GLM-4.7-Flash remains the standout: it is completely free and still gives you 203K context. That is hard to beat for prototyping OpenClaw workflows before committing to a paid tier. See our guide to the cheapest way to run OpenClaw for more budget options.

Limitations and Tradeoffs

GLM models are strong contenders for OpenClaw, but they are not the right fit for every operator.

  • Benchmark gap at the frontier: GLM-5 ranks around #19-22 across major benchmarks. It is competitive, but it does not consistently beat the top 5 models from Anthropic, OpenAI, or Google on English-language coding tasks.
  • Ecosystem maturity: The Z.AI developer ecosystem is smaller than OpenAI's or Anthropic's. Documentation is improving, but community resources, tutorials, and third-party integrations are less abundant.
  • Data residency: Zhipu is a Chinese company. For operators with strict data sovereignty requirements (healthcare, government, defense), this may be a dealbreaker regardless of technical capability.
  • GLM-4.7-Flash limitations: The free model is lean — only 3B active parameters. It works for simple tasks and prototyping, but it degrades noticeably on multi-step reasoning, complex tool use, and long agent chains.
  • Pricing volatility: Zhipu raised GLM-5 prices by 30% in February 2026. Future price increases are possible, especially as demand grows.

When NOT to use GLM for OpenClaw: if your work requires only English, your compliance requirements restrict Chinese-domiciled providers, or you need absolute top-tier agentic coding and can afford Claude or GPT-5.

Related Guides

FAQ

What is the best GLM model for OpenClaw in 2026?

GLM-5 is the best GLM model for OpenClaw if you need strong coding and agentic capabilities. It scores 77.8% on SWE-bench Verified and costs $1.00 per million input tokens. For free usage, GLM-4.7-Flash is the best starting point with 203K context at zero cost.

Is GLM-4.7-Flash really free for OpenClaw?

Yes. As of April 2026, Zhipu offers GLM-4.7-Flash at zero cost to all registered users on the Z.AI platform. You can use it through the OpenClaw zai provider or run it locally through Ollama under its MIT license.

How do I connect GLM models to OpenClaw?

Set the ZAI_API_KEY environment variable with your API key from Zhipu's open platform, then reference models using the zai/ prefix — for example, zai/glm-5 or zai/glm-4.7-flash. The OpenClaw documentation covers this under the GLM provider page.

How does GLM-5 compare to Claude for OpenClaw?

GLM-5 is roughly 5-8x cheaper than Claude Opus on API pricing, but it ranks lower on most English-language coding benchmarks. GLM-5's strongest advantage is bilingual Chinese-English performance and cost efficiency. For purely English agentic work at the highest quality level, Claude still leads.

Should I use GLM or DeepSeek for OpenClaw?

Both are strong Chinese-origin models. GLM-5 has an edge in bilingual fluency and Zhipu's coding plan structure, while DeepSeek tends to rank higher on certain coding benchmarks. Try both through OpenClaw — you can switch models with a single flag change — and compare on your actual workload.

Top comments (0)