Z.ai

Z.ai is an Enterprise Agents AI platform for organizations that want GLM large language models, agent-based services, and enterprise-ready APIs for application development, combining model access, automation products, and deployment-oriented infrastructure with enterprise procurement options and published usage-based API pricing for selected models and tools. It is aimed at organizations that need model access, automation agents, and API deployment options rather than a simple consumer chatbot.

The provided website data identifies Z.ai as the English-facing site for Zhipu AI at https://www.zhipuai.cn/en and describes the platform around the GLM model series, AutoGLM, AutoClaw, and enterprise-ready APIs. Z.ai's official developer pricing documentation at https://docs.z.ai/guides/overview/pricing lists current USD API pricing: GLM-4.5 is priced at $0.60 per 1M input tokens, $0.11 per 1M cached input tokens, and $2.20 per 1M output tokens; GLM-4.5-Air is priced at $0.20 per 1M input tokens, $0.03 per 1M cached input tokens, and $1.10 per 1M output tokens; GLM-4.5-Flash and GLM-4.7-Flash are listed as free for input, cached input, cached input storage, and output. The pricing page also lists Web Search at $0.01 per use, GLM-Image at $0.015 per image, CogView-4 at $0.01 per image, GLM Slide/Poster Agent beta at $0.70 per MTok, and General-Purpose Translation at $3.00 per MTok.

For buyers comparing Enterprise Agents tools, Z.ai should be evaluated as a platform option for teams that want access to the GLM family and agent products such as AutoGLM and AutoClaw. Compared to the other Enterprise Agents tools in our directory, the positioning is closest to vendors that combine foundation models, API access, and automation agents rather than tools that only provide workflow builders or chat interfaces. Based on our analysis of 870+ AI tools, this kind of platform is usually most relevant to product, engineering, and transformation teams that have internal development capacity and need APIs they can embed into existing software.

As of the 2026-06-12 enrichment timestamp, Z.ai's official GLM-4.5 documentation at https://docs.z.ai/guides/llm/glm-4.5 and release notes at https://docs.z.ai/release-notes/new-released provide the main source context for the 2025-2026 product signals in this record. GLM-4.5 and GLM-4.5-Air use a Mixture-of-Experts architecture, with GLM-4.5 listed at 355B total parameters and 32B active parameters and GLM-4.5-Air listed at 106B total parameters and 12B active parameters. The GLM-4.5 documentation states a 128K context length, 96K maximum output tokens, pretraining on 15 trillion tokens, and support for thinking and non-thinking modes through the thinking.type parameter. The GLM-4.5 release materials report evaluation across 12 benchmark suites and a 52-task real-world agent coding evaluation, while the release notes show GLM-5.1 on 2026-04-07, GLM-5V-Turbo on 2026-04-01, GLM-5 on 2026-02-12, GLM-4.7 on 2025-12-22, AutoGLM-Phone-Multilingual on 2025-12-11, GLM-4.6V on 2025-12-08, GLM-4.6 on 2025-09-30, GLM-4.5V on 2025-08-11, and the GLM-4.5 Series on 2025-07-28.

The main procurement question is whether Z.ai's GLM ecosystem, agent capabilities, API economics, and enterprise terms match the organization's language, compliance, infrastructure, and support requirements. Public developer pricing improves early cost modeling, but teams should still validate enterprise discounts, committed-use terms, rate limits, model availability, data-retention options, regional hosting, supported integrations, and service levels directly with the vendor before committing. Z.ai is therefore best assessed through a technical pilot: test the GLM models on your own prompts, compare AutoGLM and AutoClaw against your target workflows, and request enterprise documentation for security, deployment options, and commercial terms.