Cerebras vs Fireworks AI

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

Text, vision, audio, image, and embedding models, including recent releases such as Kimi K3, GLM 5.2, DeepSeek V4, MiniMax M3, and Qwen3.7 Plus

Industry-leading throughput and latency with fast inference engine

SFT, DPO, and reinforcement fine-tuning of models up to 1T+ parameters, from guided runs through writing your own trainer and RL loop

Drop-in replacement for closed-model APIs - change the base URL to migrate, with the same SFT data format for fine-tuning

H100, H200, B200, B300, and GB300 deployments with per-second billing and autoscaling

Async bulk inference jobs run at scale for less than standard serverless requests