Compute Comparison

Meta vs Z.AI: Token Pricing, Speed & Intelligence

Full comparison of Meta and Z.AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.

Meta

Llama 4 & Muse Spark — the world's most widely deployed open-weight models

Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.

Self-hosted inferenceCost-optimised at scaleEdge/on-deviceChatVisionCoding
Proprietary modelsHosts open weights

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.

ChatCodingLong-document analysisEnterprise AI
Proprietary models

Key metrics

Cheapest input ($/1M)

Cheapest output ($/1M)

Peak throughput

Best latency (TTFT)

Intelligence score

Context window

Live token pricing

Strengths & weaknesses

Meta

Open-weight models — self-host on any GPU cloud for lowest per-token cost at scale
Llama 4 Behemoth: 2T parameter frontier model with 1M context window
Widest third-party hosting ecosystem — available on AWS, Azure, GCP, Together AI, Groq, and 20+ others
Llama 3.2 1B/3B models run on-device (mobile, edge)
No vendor lock-in — switch inference providers without changing model weights
Self-hosting requires GPU infrastructure expertise
Meta's own API has limited availability vs third-party hosts
Llama 4 Behemoth pricing not yet publicly listed
Smaller proprietary model lineup vs OpenAI/Anthropic

Z.AI

1M token context window
Strong Chinese and English bilingual performance
Enterprise-grade reliability
Smaller international developer community
Fewer third-party integrations than OpenAI

Key differentiators

Meta

The only frontier-class model family available as open weights — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume workloads.

Z.AI

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

Meta FAQs

What is the Llama 4 context window?

Llama 4 Scout and Maverick support 1,000,000-token (1M) context windows. Llama 4 Behemoth also targets 1M context. This makes Llama 4 competitive with Gemini 1.5 Pro for long-document and multi-document tasks.

How much does the Meta Llama API cost?

Llama 3.2 1B is $0.02/1M tokens in/out. Llama 3.2 3B is $0.03/$0.05. Llama 3.1 8B is $0.02/$0.05. Llama 3.2 90B Vision is $1.20/$1.20. Muse Spark 1.1 is $1.25/$4.25. Llama 4 Behemoth pricing is not yet publicly listed.

Can I self-host Llama models?

Yes — all Llama 3.x and Llama 4 Scout/Maverick weights are publicly available under the Llama Community License. You can run them on any GPU cloud provider. A single H100 at ~$2.50/hr can serve Llama 3.1 8B at very high throughput, making self-hosting cost-effective above ~10M tokens/day.

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

MetaLlama 4 & Muse Spark — the world's most widely deployed open-weight models

Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.

The only frontier-class model family available as open weights — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume workloads.

Z.AIGLM 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

Meta

  • Open-weight models — self-host on any GPU cloud for lowest per-token cost at scale
  • Llama 4 Behemoth: 2T parameter frontier model with 1M context window
  • Widest third-party hosting ecosystem — available on AWS, Azure, GCP, Together AI, Groq, and 20+ others

Z.AI

  • 1M token context window
  • Strong Chinese and English bilingual performance
  • Enterprise-grade reliability

Provider category context

Meta is a open source host, founded in 2023. Z.AI is a frontier lab, founded in 2019. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.

How to choose between them

Choose Meta if you need open-weight models — self-host on any gpu cloud for lowest per-token cost at scale. Choose Z.AI if you need 1m token context window. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.