Cerebras vs Groq: Token Pricing, Speed & Intelligence
Full comparison of Cerebras and Groq — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Cerebras
CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B
Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.
Groq
LPU + LPX inference — high-throughput tokens per second, $350M Series A
Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Cerebras
Groq
Key differentiators
Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.
Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.
Frequently asked questions
Cerebras FAQs
How fast is Cerebras inference?
Cerebras delivers significantly higher throughput than conventional GPU-based providers on GPT OSS 120B and Qwen 3.8 27B, enabling real-time agentic workflows and interactive AI applications. Check cerebras.ai for current benchmark figures.
What is the Cerebras CS-4 chip?
The CS-4 is Cerebras's latest wafer-scale chip, 58× larger than a GPU. It provides 900,000+ AI cores and massive on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.
What models does Cerebras support?
Cerebras supports GPT OSS 120B and Qwen 3.8 27B on the Developer tier. Enterprise adds access to all models including production and preview models. Check cerebras.ai/pricing for current rates.
Groq FAQs
How fast is Groq inference?
Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B using custom LPU/LPX chips, making it well-suited for real-time and low-latency applications.
What is Groq's LPX chip?
The LPX is Groq's next-generation inference chip, introduced alongside the existing LPU lineup. It expands Groq's capacity and enables even higher throughput for large-scale deployments.
How much does Groq cost?
Groq uses per-token pricing across its model catalog. Check console.groq.com/docs/openai for current rates on Llama 3.3 70B, Llama 3.1 8B, and other models.
Provider resources
Cerebras — CS-4 wafer-scale — high-throughput inference on GPT OSS 120B and Qwen 3.8 27B
Cerebras uses wafer-scale silicon (the CS-4 chip) to deliver high-throughput inference significantly faster than conventional GPU systems. The Developer tier provides self-serve access; Enterprise tier adds production capacity, custom model weights, fine-tuning, and dedicated support. Available on AWS Marketplace, OpenRouter, HuggingFace, and Vercel. Check cerebras.ai/pricing for current rates and any free-credit offers.
Cerebras CS-4 wafer-scale silicon delivers high-throughput inference on GPT OSS 120B and Qwen 3.8 27B — significantly faster than conventional GPU systems, enabling real-time agentic workflows and interactive AI.
Groq — LPU + LPX inference — high-throughput tokens per second, $350M Series A
Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.
Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.
Key strengths compared
Cerebras
- GPT OSS 120B: high-throughput inference on CS-4 wafer-scale silicon
- Qwen 3.8 27B: fast efficient-tier inference
- CS-4 chip: 58× larger than GPUs, purpose-built for AI inference
- Limited model selection vs. Together AI or Fireworks
- No vision or multimodal support
Groq
- 750+ tokens/sec on Llama 3.3 70B — high-throughput GPU-class inference (Groq-published)
- Sub-100ms time-to-first-token for real-time applications
- LPX chips alongside LPUs for expanded capacity
- Limited model selection vs. Together AI or Fireworks
- No vision model support on most models
Key weaknesses compared
Cerebras
- Limited model selection vs. Together AI or Fireworks
- No vision or multimodal support
- No fine-tuning on Developer tier
Groq
- Limited model selection vs. Together AI or Fireworks
- No vision model support on most models
- No fine-tuning capability
Provider category context
Cerebras is a inference api, founded in 2016. Groq is a inference api, founded in 2016. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Cerebras and Groq host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.