zai-org/GLM-5.1 Review 2024
zai-org/GLM-5.1
Open-weight LLM engineered for complex reasoning and agentic workflows
Starting at
$1.40 / 1M input tokens
Billing
Pay-as-you-go · Prepaid credits
Refund
Pay-as-you-go model; no refunds on consumed tokens. Unused credits may expire per provider terms.
Our Take
GLM-5.1 delivers frontier-level reasoning and coding performance under an open MIT license, but its high token cost and slower inference speed make it best suited for specialized, high-value tasks rather than high-volume, low-latency applications.
Is It Worth It?
Worth it for developers and enterprises needing a highly capable, commercially permissive model for software engineering and complex multi-step agents, provided latency and token costs fit the budget.
Best Suited For
Software engineering teams, AI agent developers, and researchers requiring strong multi-step reasoning and open-weight deployment flexibility.
What We Loved
- ✓Strong multi-step reasoning and coding performance
- ✓Commercially permissive MIT license
- ✓Large 200k context window
- ✓Open-weight with transparent architecture
- ✓High benchmark scores (Intelligence Index: 51)
What Bothered Us
- ✗Higher token pricing compared to many open models
- ✗Slower inference speed (~44 t/s)
- ✗High verbosity increases output costs
- ✗Text-only input/output requires separate vision models
- ✗Heavy hardware requirements for self-hosting
How It Performed
output Quality
Highly accurate and structured, particularly for code generation, debugging, and multi-step logical tasks. Tends toward verbose explanations unless constrained.
ai Intelligence
Scores 51 on the Artificial Analysis Intelligence Index, placing it well above the median for comparable open-weight models. Excels in reasoning and software engineering benchmarks.
speed Test
Below average at ~44 tokens per second with a time-to-first-token of ~1.66s. Suitable for asynchronous or batched workflows, but not ideal for real-time conversational UIs.
GLM-5.1 represents Z AI's push into the open-weight frontier model space. With 754B total parameters and a 40B active parameter count per token, it utilizes a mixture-of-experts (MoE) architecture to balance capability and compute efficiency. The model introduces a 'rumination' reasoning mode, allowing it to process complex internal steps before generating a final response, which significantly boosts performance on coding and agentic benchmarks. It supports a 200k-token context window and is fully compatible with OpenAI SDKs, making integration straightforward. However, its performance comes with trade-offs: inference speeds average around 44 tokens per second, and the model is notably verbose, which can drive up output token costs. Priced at $1.40/M input tokens and $4.40/M output tokens, it is positioned at a premium tier compared to many open-weight alternatives. For teams prioritizing reasoning depth, commercial licensing flexibility, and coding accuracy over raw speed or low cost, GLM-5.1 is a compelling option.
Ideal for automated code generation, complex debugging, multi-step AI agent orchestration, and technical documentation synthesis. Less suitable for high-frequency, low-latency chatbots or simple content generation due to cost and verbosity.
Competes directly with other frontier open-weight models like Meta's LLaMA series and proprietary models like GPT-4 and Claude. While it matches or exceeds many in reasoning benchmarks, it faces stiff competition on latency and pricing from optimized turbo variants and smaller, faster models.
Frequently Asked Questions
GLM-5.1 is a 754B-parameter open-weight large language model developed by Z AI (Zhipu AI). It uses a mixture-of-experts architecture and is optimized for software engineering, agentic workflows, and complex multi-step reasoning.
It utilizes a specialized 'rumination' reasoning mode that allows the model to perform iterative internal processing before generating a final output. This architecture significantly improves accuracy on coding benchmarks and complex logical tasks.
Standard pricing across major providers is $1.40 per million input tokens and $4.40 per million output tokens. Some platforms offer cached input pricing at $0.26 per million tokens. Due to high verbosity, output costs can accumulate quickly.
Yes, the model weights are available on Hugging Face under the MIT license. However, self-hosting requires substantial GPU infrastructure due to its 754B total parameter size and 40B active parameters per token.
It is not ideal for low-latency real-time chat. With an average inference speed of ~44 tokens per second and a time-to-first-token of ~1.66s, it is better suited for asynchronous, batched, or complex task execution.
No, GLM-5.1 is a text-only model. For multimodal capabilities, Z AI offers separate variants like GLM-5V-Turbo for vision tasks.
It is released under the MIT License, allowing free commercial use, modification, and distribution without restrictive vendor lock-in.
GLM-5.1 is the full-scale reasoning variant focused on maximum accuracy and complex problem-solving. GLM-5-Turbo is a lighter, faster, and more cost-optimized version designed for higher-throughput applications and general-purpose tasks.