Groq vs Cerebras: which fast-inference platform wins in 2026?
Groq wins on broad model availability, OpenAI-compatible API, and competitive pricing. Cerebras wins on raw throughput (wafer-scale CS-3) and largest open-weight models. Pick Groq for general-purpose fast inference, Cerebras for absolute throughput + large open-weight LLMs.
At a glance
| Dimension | Groq | Cerebras |
|---|---|---|
| Hardware | LPU (Language Processing Unit) | Wafer-scale CS-3 / CS-4 |
| Raw throughput (tokens/s) | ~500-800 tok/s on 70B | ~1500-2500 tok/s on 70B (CS-3 / Inference)WIN |
| Model availability | Llama, Mixtral, Gemma, Qwen, WhisperWIN | Llama, Qwen, DeepSeek, smaller Mistral |
| Largest open-weight model | Up to 70B / 405B (Llama) | Up to 405B Llama + 671B DeepSeekWIN |
| OpenAI-compatible API | Yes | Yes |
| Pricing | Per-token, competitive | Per-token, competitive at high volume |
| Latency (TTFT) | Very low (~50-100ms)WIN | Very low (~80-150ms) |
| Function calling / tool use | Yes | Yes |
| Best for | General fast inference, voice agents, broad model menu | Raw throughput, largest models, high-volume batch |
Verdict
Groq is the right pick for general-purpose fast inference — broadest model menu (including Whisper STT), OpenAI-compatible API, very low TTFT — ideal for voice agents and real-time apps. Cerebras is the right pick for raw throughput on the largest open-weight models — wafer-scale CS-3 / CS-4 deliver 2-4× Groq's tokens-per-second on 70B+ models, valuable for bulk batch or super-low-latency. Both ship OpenAI-compatible APIs; switching is one URL change.
When to pick which
Pick Groq
General fast inference, voice agents, broad model + Whisper STT.
Pick Cerebras
Maximum throughput on largest open-weight models, bulk batch.
FAQ
Fastest pure throughput?
Cerebras CS-3 — leads on 70B+ models.
Most models available?
Groq — broader catalog including Whisper STT.
OpenAI-compatible?
Yes — both ship OpenAI-format APIs; switching is one URL change.
Last updated: 2026-06-01.