Cerebras vs Z.AI: Token Pricing, Speed & Intelligence
Full comparison of Cerebras and Z.AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Cerebras
Wafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth
Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.
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
Cerebras
Z.AI
Key differentiators
Frequently asked questions
Cerebras FAQs
How fast is Cerebras inference?
Cerebras delivers 4,500+ tokens/second on Llama 3.1 8B — roughly 10× faster than GPU-based providers like Groq (1,200 t/s) or Together AI (350 t/s). This makes it the fastest inference option available.
What is a Cerebras wafer-scale chip?
The Cerebras CS-3 chip is fabricated on a single silicon wafer rather than individual dies. This gives it 900,000 AI cores and 44GB of on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.
What models does Cerebras support?
Cerebras currently supports Llama 3.1 8B and 70B, and Llama 3.3 70B. The model selection is intentionally limited — Cerebras focuses on delivering extreme speed on a curated set of models rather than broad catalog coverage.
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
Cerebras — Wafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth
Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.
Cerebras delivers 4,500+ tokens/sec on Llama 3.1 8B — 10× faster than any GPU provider, enabling genuinely real-time AI applications.
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
Cerebras
- ▸4,500+ tokens/sec on Llama 3.1 8B — fastest inference available
- ▸Sub-50ms time-to-first-token for real-time applications
- ▸Wafer-scale chip architecture eliminates GPU memory bottlenecks
Z.AI
- ▸1M token context window
- ▸Strong Chinese and English bilingual performance
- ▸Enterprise-grade reliability
Provider category context
Cerebras is a inference api, founded in 2016. Z.AI is a frontier lab, founded in 2019. Cerebras as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. Z.AI as a frontier lab trains and serves proprietary models with capabilities not available elsewhere.
How to choose between them
Choose Cerebras if you need 4,500+ tokens/sec on llama 3.1 8b — fastest inference available. 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.