Cerebras vs Groq

Models run on the Cerebras Wafer-Scale Engine, which keeps model weights in on-chip SRAM to reach published speeds in the thousands of tokens per second

Chat Completions and Completions endpoints at api.cerebras.ai/v1, usable from the OpenAI SDKs or the Cerebras Python and TypeScript SDKs

Public endpoints serve unmodified open-weight models such as OpenAI GPT OSS and Qwen, with a documented policy of no pruning on hosted models

Configurable reasoning effort, streaming, structured outputs, parallel tool calling, and prompt caching across the catalog

Vision-capable models accept PNG and JPEG images alongside text

Private, provisioned endpoints on reserved capacity with fine-tuning, weight management, and additional model families, arranged through sales

Custom Language Processing Units run inference, with 256 LPUs and 128 GB of on-chip SRAM per rack

Drop-in replacement for the OpenAI API at api.groq.com/openai/v1, including a Responses API

GroqMetal bare-metal infrastructure, GroqCore inference stack, and GroqAssured enterprise governance, each including the layers below

Text generation plus speech-to-text, text-to-speech, OCR and image recognition, and content moderation models

Compound systems that combine models with web search, code execution, remote MCP tools, and Google Workspace connectors

Performance, flex, and batch processing tiers, plus prompt caching, structured outputs, and LoRA inference