Groq vs Cerebras
Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.
| Dimension | Groq | Cerebras |
|---|---|---|
| Overall | 8.0 | 7.6 |
| Capability fit | 7.8 | 8.0 |
| Price / performance | 9.0 | 8.2 |
| Latency / reliability | 8.2 | 7.8 |
| Developer & agent DX | 8.8 | 7.5 |
| Privacy / data retention | 7.0 | 7.2 |
| Lock-in risk (higher = worse) | 5.0 | 5.5 |
| Maturity / track record | 7.8 | 7.5 |
| Pricing | Per-token with free tier; LPU inference; narrow production model set (Llama/Qwen/GPT-OSS etc.); batch discounts. Llama Guard 4 12B (model id meta-llama/llama-guard-4-12b) is still documented at console.groq.com/docs/mode… | As of 2026-09-14: Official public models API (api.cerebras.ai/public/v1/models, HuggingFace format) pins GPT-OSS-120B $0.35/$0.75 per 1M in/out (131k ctx) and Qwen3.8-27B $0.99/$1.49 (65k ctx). Marketing cerebras.ai/infe… |
| Tagline | Use when latency is the product. Do not build your whole company on one Groq model SKU — they rotate. | Best ultra-throughput play when your model is on Cerebras (official token matrix now pinned). Pair with… |
Groq
Use when latency is the product. Do not build your whole company on one Groq model SKU — they rotate. Strengths: Blazing tokens/sec latency; Simple API and free tier for prototypes; Great for real-time agent loops on supported models.
Strengths
- Blazing tokens/sec latency
- Simple API and free tier for prototypes
- Great for real-time agent loops on supported models
Weaknesses
- Limited model variety
- Aggressive model deprecations
- Not for frontier closed models
Cerebras
Best ultra-throughput play when your model is on Cerebras (official token matrix now pinned). Pair with Groq/Fireworks fallback for catalog holes and rate-limit spikes. Strengths: Extreme throughput on supported models; Differentiated hardware story; Attractive for high-QPS serving.
Strengths
- Extreme throughput on supported models
- Differentiated hardware story
- Attractive for high-QPS serving
Weaknesses
- Narrow model/support surface vs GPU hosts
- Availability and waitlists historically
- Less commodity than NVIDIA ecosystem
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