Cerebras vs Tencent: Token Pricing, Speed & Intelligence
Full comparison of Cerebras and Tencent — 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.
Tencent
Hunyuan LLMs from China's largest tech company
Tencent offers the Hunyuan series of large language models via its cloud platform. Hunyuan models are optimised for Chinese and multilingual tasks, with strong performance on coding and reasoning benchmarks.
Key metrics
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Live token pricing
Strengths & weaknesses
Cerebras
Tencent
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.
Tencent FAQs
What is Tencent Hunyuan?
Hunyuan is Tencent's family of large language models, available via the Tencent Cloud API. The latest generation (hy3) supports 262K context and competitive pricing.
Is Tencent Hunyuan available internationally?
Yes. The Hunyuan API is accessible globally via Tencent Cloud, though latency may be higher outside Asia-Pacific regions.
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.
Tencent — Hunyuan LLMs from China's largest tech company
Tencent offers the Hunyuan series of large language models via its cloud platform. Hunyuan models are optimised for Chinese and multilingual tasks, with strong performance on coding and reasoning benchmarks.
Hunyuan 3 (hy3) offers a 262K context window at $0.14/1M input tokens — among the cheapest frontier-class 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
Tencent
- ▸Competitive pricing
- ▸Strong Chinese-language performance
- ▸Large context window
Provider category context
Cerebras is a inference api, founded in 2016. Tencent is a frontier lab, founded in 2023. Cerebras as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers. Tencent 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 Tencent if you need competitive pricing. 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.