GLM-5.1 Pricing & Plans 2026

Name: GLM-5.1
Brand: GLM-5.1
Availability: InStock

Complete pricing guide for GLM-5.1. Compare all plans, analyze costs, and find the perfect tier for your needs.

Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether GLM-5.1 is worth it →

🆓Free Tier Available

💎1 Paid Plans

⚡No Setup Fees

Choose Your Plan

Open Weights (Self-hosted)

Free

✓Full model weights downloadable from Hugging Face
✓Use with vLLM, SGLang, Transformers, Ollama, llama.cpp
✓OpenAI-compatible API when self-served
✓Tool-calling and reasoning support
✓Subject to model license terms

Start Free →

Z.ai API Platform

Usage-based (see Z.ai)

✓Managed GLM-5 endpoints
✓No infrastructure to run
✓Pay-per-token billing
✓Standard chat and tool-calling APIs
✓Hosted by Z.ai

Start Free Trial →

Pricing sourced from GLM-5.1 · Last verified March 2026

Feature Comparison

Features	Open Weights (Self-hosted)	Z.ai API Platform
Full model weights downloadable from Hugging Face	✓	✓
Use with vLLM, SGLang, Transformers, Ollama, llama.cpp	✓	✓
OpenAI-compatible API when self-served	✓	✓
Tool-calling and reasoning support	✓	✓
Subject to model license terms	✓	✓
Managed GLM-5 endpoints	—	✓
No infrastructure to run	—	✓
Pay-per-token billing	—	✓
Standard chat and tool-calling APIs	—	✓
Hosted by Z.ai	—	✓

Is GLM-5.1 Worth It?

✅ Why Choose GLM-5.1

• Best-in-class open-source performance on reasoning, coding, and agentic tasks per Z.ai benchmarks (e.g., 77.8 on SWE-bench Verified, 96.9 on HMMT Nov. 2025)
• Free open-weights download — no per-token API costs once self-hosted
• Massive 744B-parameter MoE with only 40B active per token, balancing capacity and inference cost
• DeepSeek Sparse Attention reduces long-context deployment cost meaningfully versus dense attention
• Wide deployment support: vLLM, SGLang, Transformers, Ollama, LM Studio, llama.cpp, Docker — covering most serving stacks
• Native tool-calling and chat templates ship with the model, simplifying agent integration

⚠️ Consider This

• Running the full 744B-parameter model requires substantial GPU memory and multi-GPU infrastructure — out of reach for hobbyists
• Still trails frontier closed models like Gemini 3 Pro (91.9 GPQA) and GPT-5.2 on several benchmarks (HLE, GPQA-Diamond)
• Documentation on the Hugging Face card is sparse compared to commercial LLM platforms — most setup details live in external blogs and the GitHub repo
• No standalone polished web UI; users must self-host or use the separate Z.ai API platform
• Tool-calling uses a custom XML format that may require adapter code versus standard OpenAI function-calling JSON

What Users Say About GLM-5.1

👍 What Users Love

✓Best-in-class open-source performance on reasoning, coding, and agentic tasks per Z.ai benchmarks (e.g., 77.8 on SWE-bench Verified, 96.9 on HMMT Nov. 2025)
✓Free open-weights download — no per-token API costs once self-hosted
✓Massive 744B-parameter MoE with only 40B active per token, balancing capacity and inference cost
✓DeepSeek Sparse Attention reduces long-context deployment cost meaningfully versus dense attention
✓Wide deployment support: vLLM, SGLang, Transformers, Ollama, LM Studio, llama.cpp, Docker — covering most serving stacks
✓Native tool-calling and chat templates ship with the model, simplifying agent integration
✓Backed by Z.ai's 'slime' asynchronous RL infrastructure, with active iteration from GLM-4.5 to 4.7 to 5

👎 Common Concerns

⚠Running the full 744B-parameter model requires substantial GPU memory and multi-GPU infrastructure — out of reach for hobbyists
⚠Still trails frontier closed models like Gemini 3 Pro (91.9 GPQA) and GPT-5.2 on several benchmarks (HLE, GPQA-Diamond)
⚠Documentation on the Hugging Face card is sparse compared to commercial LLM platforms — most setup details live in external blogs and the GitHub repo
⚠No standalone polished web UI; users must self-host or use the separate Z.ai API platform
⚠Tool-calling uses a custom XML format that may require adapter code versus standard OpenAI function-calling JSON
⚠License terms and commercial-use specifics must be verified directly on the model card before production deployment

Pricing FAQ

What is GLM-5.1 and who built it?

GLM-5.1 is a large language model in the GLM-5 family released by zai-org (Z.ai), distributed as open weights on Hugging Face. It targets complex systems engineering and long-horizon agentic tasks such as multi-step coding, reasoning, and tool use. The model uses a Mixture-of-Experts architecture with 744B total parameters and 40B active per forward pass. Z.ai also offers a managed API on the Z.ai API Platform for users who prefer not to self-host.

How much does GLM-5.1 cost?

The model weights are free to download from Hugging Face, so there is no licensing fee to run it yourself. Real costs come from compute: serving a 744B-parameter MoE model requires multi-GPU infrastructure, typically high-VRAM datacenter GPUs. If you prefer a hosted endpoint, Z.ai offers a paid managed API on the Z.ai API Platform (pricing listed there). Quantized variants accessible via Ollama or LM Studio can lower hardware requirements significantly.

How does GLM-5.1 compare to GPT-5.2, Claude Opus 4.5, and Gemini 3 Pro?

On the published benchmarks, GLM-5 leads on HMMT Nov. 2025 (96.9 vs Gemini 3 Pro 93.0 and Claude Opus 4.5 91.7) and is competitive on AIME 2026 I (92.7) and SWE-bench Multilingual (73.3, ahead of Gemini 3 Pro's 65.0). It still trails frontier models on Humanity's Last Exam (30.5 vs Gemini 3 Pro 37.2) and GPQA-Diamond (86.0 vs 91.9–92.4). For open-source coding and agentic workloads, GLM-5 is the strongest contender Z.ai has shipped.

How do I deploy GLM-5.1 locally or on my own server?

The Hugging Face card documents three primary paths. With vLLM, you run pip install vllm then vllm serve "zai-org/GLM-5" to expose an OpenAI-compatible endpoint on port 8000. SGLang supports a similar flow via python3 -m sglang.launch_server with --model-path "zai-org/GLM-5" on port 30000. For lighter use, Docker Model Runner (docker model run hf.co/zai-org/GLM-5), Ollama, or LM Studio with quantized variants work well on smaller hardware.

Does GLM-5.1 support tool calling and function calling for agents?

Yes. The chat template natively handles a tools field and emits structured tool calls inside <tool_call>...</tool_call> XML blocks, with arg_key/arg_value pairs for each parameter. The model is explicitly tuned for long-horizon agentic tasks, which is a stated focus of the GLM-5 release. Note that the format is custom XML rather than OpenAI's JSON function-calling schema, so you may need a small adapter when migrating existing OpenAI agent code.

Ready to Get Started?

AI builders and operators use GLM-5.1 to streamline their workflow.

Try GLM-5.1 Now →

More about GLM-5.1

Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Compare GLM-5.1 Pricing with Alternatives

Qwen 3 Pricing

Large language model and AI assistant developed by Alibaba, offering chat-based AI capabilities.

Compare Pricing →