Moonshot AI vs Z.AI: Token Pricing, Speed & Intelligence
Full comparison of Moonshot AI and Z.AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Moonshot AI
Kimi — long-context frontier models from China's leading AI lab
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K2 is a 1-trillion-parameter MoE model released as open-weight, competitive with frontier models on coding and agentic tasks. The Kimi API offers long-context processing up to 128K tokens with competitive pricing.
Z.AI
GLM frontier models with 1M context
Z.AI (formerly Zhipu AI) develops the GLM series of large language models. GLM-5.2 supports a 1M token context window and is designed for enterprise-grade chat, coding, and long-document tasks.
Key metrics
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Live token pricing
Strengths & weaknesses
Moonshot AI
Z.AI
Key differentiators
Kimi K2 is a 1-trillion-parameter open-weight MoE model that scores competitively with Claude Sonnet on coding and agentic benchmarks.
GLM-5.2 offers a 1M token context window at $1.11/1M input tokens, making it one of the most cost-effective long-context models available.
Frequently asked questions
Moonshot AI FAQs
What is Kimi K2?
Kimi K2 is a 1-trillion-parameter mixture-of-experts model from Moonshot AI, released as open-weight. It activates approximately 32B parameters per token and is designed for coding, agentic tasks, and long-context reasoning.
Is Kimi K2 open-weight?
Yes. Kimi K2 weights are publicly available on Hugging Face, making it one of the largest open-weight models available. Teams can self-host it on multi-GPU clusters or access it via the Moonshot API.
How does Kimi K2 compare to Claude Sonnet?
Kimi K2 scores competitively with Claude Sonnet 4 on coding benchmarks including SWE-bench. It is particularly strong on agentic tasks that require tool use and multi-step planning.
Z.AI FAQs
What is GLM-5.2?
GLM-5.2 is the latest model in Zhipu AI's GLM series, supporting a 1M token context window. It is designed for long-document analysis, coding, and enterprise chat applications.
How does Z.AI compare to other Chinese LLM providers?
Z.AI's GLM models compete with Alibaba's Qwen and Baidu's ERNIE series. GLM-5.2 stands out for its 1M context window and competitive pricing.
Provider resources
Moonshot AI — Kimi — long-context frontier models from China's leading AI lab
Moonshot AI is a Chinese AI startup behind the Kimi model family. Kimi K2 is a 1-trillion-parameter MoE model released as open-weight, competitive with frontier models on coding and agentic tasks. The Kimi API offers long-context processing up to 128K tokens with competitive pricing.
Kimi K2 is a 1-trillion-parameter open-weight MoE model that scores competitively with Claude Sonnet on coding and agentic benchmarks.
Z.AI — GLM frontier models with 1M context
Z.AI (formerly Zhipu AI) develops the GLM series of large language models. GLM-5.2 supports a 1M token context window and is designed for enterprise-grade chat, coding, and long-document tasks.
GLM-5.2 offers a 1M token context window at $1.11/1M input tokens, making it one of the most cost-effective long-context models available.
Key strengths compared
Moonshot AI
- ▸Kimi K2 is a 1T MoE open-weight model with strong coding scores
- ▸Competitive on agentic and tool-use benchmarks
- ▸Long-context support up to 128K tokens
Z.AI
- ▸1M token context window
- ▸Strong Chinese and English bilingual performance
- ▸Enterprise-grade reliability
Provider category context
Moonshot AI is a frontier lab, founded in 2023. Z.AI is a frontier lab, founded in 2019. Both are frontier lab providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Moonshot AI and Z.AI are frontier labs with proprietary models. Choose based on benchmark performance for your specific task: Moonshot AI leads on kimi k2 is a 1t moe open-weight model with strong coding scores, while Z.AI leads on 1m token context window. For cost-sensitive workloads, compare the cheapest model tier from each provider in the pricing table above — the gap between efficient-tier models is often larger than between flagship models.