Groq vs Cerebras: pricing, speed, and use cases (2026)

Updated May 04, 2026·By VerticalAPI Team

Groq's LPUs and Cerebras' wafer-scale CS-3 are both engineered for speed-of-light inference on open-weights models. They beat Nvidia GPUs on tokens-per-second by an order of magnitude — but at slightly different price points and with different model availability. Here's the comparison developers actually care about.

Side-by-side

Groq vs Cerebras — at a glance

Dimension	Groq	Cerebras
Flagship model	Llama 3.3 70B (LPU)	Llama 3.3 70B (CS-3)
Context window	128K	128K
Input price (per 1M tok)	$0.59	$0.85
Output price (per 1M tok)	$0.79	$1.20
Latency (typical)	~80ms TTFT, ~500 tok/s	~70ms TTFT, ~2000 tok/s
Free tier	Yes (developer tier)	Yes (limited)
Best for	Real-time agents, voice (Whisper), broad model lineup	Highest tokens/sec, code completion UX, interactive coding agents

When to choose which

Pick Groq or Cerebras?

When to choose Groq

Choose Groq when you need the fastest open-weight inference at production scale. Groq's LPU architecture pushes Llama 3.1 70B past 250 tokens/second — roughly 5x faster than the same model on standard GPUs. The pricing is aggressive ($0.59 / $0.79 per 1M for Llama 70B) and the OpenAI-compatible API drops in cleanly. Groq's catalog covers Llama 3.1/3.3, Mixtral, Gemma, and Whisper for transcription.

~250 tokens/second on Llama 3.1 70B (5x typical GPU)
$0.59 / $0.79 per 1M tokens (Llama 70B)
OpenAI-compatible API, no SDK changes needed
Strong on real-time chat, voice agents, and live coding assistants
Whisper-large-v3 for fast transcription

When to choose Cerebras

Choose Cerebras when you want the absolute peak token speed and don't mind a smaller model catalog. Cerebras' wafer-scale engines clock Llama 3.1 70B at over 450 tokens/second — nearly 2x Groq — and they offer a 405B option that nobody else runs near real-time. Pricing is flat ($0.60 / $0.60) which makes long-output workloads (code generation, long answers) predictable. The catalog is narrower (Llama 3.1 8B/70B/405B, mostly) but extremely well-tuned.

~450 tokens/second on Llama 3.1 70B (industry record)
Llama 3.1 405B available at near-real-time speeds
Flat $0.60 / $0.60 pricing — predictable cost on long outputs
Best for streaming UX, real-time agents, voice loops
Smaller model catalog (Llama 3.1 family primarily)

Why not both?

Run Groq and Cerebras side-by-side

VerticalAPI exposes both Groq and Cerebras through the same OpenAI-compatible endpoint. Route latency-critical traffic to Cerebras and broader workloads to Groq, all without SDK changes — and pay both providers directly with BYOK, zero markup.

from openai import OpenAI
client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...")

# Groq
resp_x = client.chat.completions.create(
    model="groq/llama-3.1-70b-versatile",
    messages=[{"role": "user", "content": "Hello"}],
    extra_headers={"X-Provider-Key": "sk-..."},
)

# Cerebras — same SDK, same client, different model + key
resp_y = client.chat.completions.create(
    model="cerebras/llama-3.1-70b",
    messages=[{"role": "user", "content": "Hello"}],
    extra_headers={"X-Provider-Key": "..."},
)

Try VerticalAPI free →

VerticalAPI verdict

Use Cerebras when you need raw throughput (~2000 tok/s on Llama 3.3 70B) or sub-100ms first-token latency for interactive UX (real-time code completion, voice). Use Groq when you want broader model lineup (Whisper, Mixtral, multiple Llama sizes), more mature dashboards, and slightly cheaper per-token pricing on small models. Both are routable through VerticalAPI's OpenAI-compatible endpoint.

Get started — BYOK both providers →

FAQ

Common questions about Groq vs Cerebras

How much faster is Cerebras than Groq exactly?

On Llama 3.3 70B, Cerebras typically delivers ~2000 tok/s vs Groq's ~500 tok/s — about 4x. First-token latency is similar (~70-80ms). Verify with your own traffic via VerticalAPI's per-request latency logs.

Is Cerebras production-stable?

Cerebras' inference cloud has been GA since 2024 and powers production traffic for several major AI products. SLA terms are listed in the Cerebras enterprise contract; VerticalAPI surfaces real latency so you can verify against your traffic.

Why not just always use the faster one?

Cost. For high-volume batch where latency doesn't matter, GPU providers (Together, DeepInfra, Fireworks) are 2-4x cheaper per token. Use Cerebras / Groq for the latency-critical hops, GPU providers for the rest.