Groq vs Cerebras: 2026 comparison

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

Frequently asked questions

How much faster is Cerebras than Groq?

On Llama 3.3 70B, Cerebras delivers approximately 2,100 tokens/second versus Groq at approximately 750 tokens/second, a roughly 2.5-3x advantage. On Llama 3.1 405B, Cerebras maintains hundreds of tokens/second where Groq is slower per token due to model size. Time-to-first-token is similar between the two (around 70-100ms on a warm endpoint).

What hardware does each use?

Groq uses its LPU (Language Processing Unit), a deterministic, single-purpose inference chip optimized for low-latency token streaming. Cerebras uses the WSE-3 (wafer-scale engine), a single silicon wafer with around 900,000 cores and 44GB on-chip SRAM, which is what enables its very high tokens/second on large models.

How does pricing compare?

Both price per 1M tokens and differ by model. On Llama 3.3 70B, Groq is approximately $0.59 / $0.79 per 1M input/output and Cerebras is approximately $0.85 / $1.20. Cerebras is more expensive per token but ships more tokens per second, so cost per second of output is comparable. GPU providers (Together, Fireworks, DeepInfra) are typically 2-4x cheaper on tokens but slower.

Which models can I actually run on each?

Both host open-weights models only. Groq's catalog includes Llama 3.x, Mistral, Mixtral, Gemma, Whisper, and Qwen. Cerebras hosts Llama 3.x (including 3.1 405B), Qwen, DeepSeek, and a curated set of larger open models. You cannot run GPT-4o or Claude on either, but you can call both through one OpenAI-compatible endpoint via VerticalAPI.

When should I pick Groq vs Cerebras?

Pick Groq for general low-latency inference on mid-size open models (Llama 70B and below), best SDK polish and broad availability. Pick Cerebras when you need the absolute fastest tokens/second on the largest open models (Llama 405B-class, very long outputs) or massive-context inference. For high-volume batch where latency does not matter, GPU providers are usually cheaper per token.

Caveats

Limitations of this comparison

Tokens/second figures are published by the vendors and vary with prompt length, output length, temperature, and current load; real production numbers can be lower than headline speeds.
Pricing per 1M tokens is revised regularly and is model-specific; figures here are mid-2026 list prices for Llama 3.3 70B.
Both providers host only open-weights models. Closed models (GPT-4o, Claude, Gemini) cannot run on Groq or Cerebras.
Model availability differs and changes month-to-month; the largest open models often land on Cerebras first because of its memory advantage.
Quality is a function of the underlying model, not the inference hardware; speed does not improve answers, it only delivers them faster.

Outlook

What may change in 12-24 months

Both vendors are expected to ship next-generation silicon (LPU v2 and WSE-4) that push tokens/second further, particularly on long-context workloads.
Cerebras is likely to expand its catalog of supported open models (more Mistral, Qwen, DeepSeek variants) and add cheaper tiers for smaller models.
Groq is expected to keep cutting prices to defend market share against GPU providers and Cerebras on the 70B class.
OpenAI-compatible APIs (already shipped by both) will become the universal default, making it routine to A/B test Groq vs Cerebras on the same traffic.

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