n4n vs Groq API: pricing per million tokens

The phrase n4n vs Groq API pricing per million tokens implies a like-for-like contest, but the two services sit at different layers of the stack. Groq is a single-vendor inference platform with published per-token rates for a fixed set of open-weight models; n4n is an OpenRouter-class gateway that routes to many providers, including Groq, behind one endpoint. Below we compare them on the dimensions that actually move the needle for a shipping system.

Cost Model Per Million Tokens

Groq publishes a transparent price sheet. Each model lists a distinct input and output rate denominated per million tokens. You pay exactly that, with no platform surcharge. If you send 1 million prompt tokens and receive 500k completion tokens on Llama 3.1 70B, your bill is the sum of those two line items at Groq’s printed rates.

n4n.ai aggregates provider pricing behind one OpenAI-compatible endpoint spanning 240+ models, so the per-million-token cost equals the underlying provider’s rate for the model you route to, metered per token. When you pin a request to a Groq model through n4n, your token consumption matches what Groq would have counted. The gateway adds unified metering and, if applicable, a disclosed margin. The n4n vs Groq API pricing difference therefore reduces to routing overhead and the value of not maintaining separate vendor accounts.

Both return standard usage objects:

{
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 34,
    "total_tokens": 46
  }
}

Groq bills on those counts directly. n4n sums them across whichever provider served the request, which simplifies reconciliation when you mix models.

from openai import OpenAI

groq = OpenAI(base_url="https://api.groq.com/openai/v1", api_key="GROQ_KEY")
n4n = OpenAI(base_url="https://api.n4n.ai/v1", api_key="N4N_KEY")

# Direct Groq
groq_resp = groq.chat.completions.create(
    model="llama-3.1-70b-versatile",
    messages=[{"role": "user", "content": "Status?"}]
)

# n4n with explicit Groq routing
n4n_resp = n4n.chat.completions.create(
    model="groq/llama-3.1-70b-versatile",
    messages=[{"role": "user", "content": "Status?"}]
)

Capabilities

Groq’s catalog is constrained to models they have ported to their LPU stack. Today that means Llama 3.1 8B, 70B, Mistral variants, and a handful of others. You get exceptional throughput on those, but no closed models like Claude or GPT-4o.

n4n exposes 240+ models from many providers. The same client can call a Groq model, then an Anthropic model, then a Meta model hosted elsewhere, without code changes beyond the model string. This breadth is the core tradeoff in n4n vs Groq API pricing: you pay for optionality and portability. If your feature requires only one of Groq’s optimized models, the gateway’s breadth is unused overhead.

Latency and Throughput

Groq’s LPU delivers some of the highest token rates in the industry—often hundreds of tokens per second for 70B-class models. The network path is direct: your request hits Groq’s edge, computes, returns. For latency-sensitive UX (streaming assistants, real-time agents), this is hard to beat.

n4n adds a proxy layer. The extra hop costs single-digit milliseconds in most regions, but the gateway’s automatic fallback can reduce tail latency when a primary provider throttles. If Groq returns 429, n4n can reroute to an equivalent model on another provider if you’ve allowed it. That resilience has value beyond the per-token number.

Ergonomics

Both speak the OpenAI chat completions schema. Groq’s integration is a one-line base URL change. n4n honors client routing directives and forwards provider cache-control hints, so you can send cache_control and have it passed upstream.

# n4n forwards cache-control to the routed provider
n4n.chat.completions.create(
    model="groq/llama-3.1-8b",
    messages=[
        {"role": "system", "content": "You are terse."},
        {"role": "user", "content": "Explain TCP.",
         "cache_control": {"type": "ephemeral"}}
    ]
)

Groq’s own API accepts similar hints natively; no translation needed. Where n4n diverges is in failure handling. With Groq direct, you write your own retry:

from openai import RateLimitError
try:
    groq.chat.completions.create(model="llama-3.1-70b", messages=[...])
except RateLimitError:
    # implement fallback or queue
    pass

With n4n, you set a routing policy once and the gateway handles degradation. That is a concrete engineering cost saved.

Ecosystem and Limits

Groq enforces per-minute token and request caps that scale with your tier. You monitor those in their console. n4n surfaces per-token usage metering and respects the underlying provider’s limits, but abstracts multi-provider quota into a single balance. You no longer juggle separate Groq, Anthropic, and OpenAI rate limits; you watch one meter.

Head-to-Head Comparison

Practical Cost Scenarios

Consider a service processing 100M input and 50M output tokens monthly on Llama 3.1 8B. Groq’s published rate for that model is among the lowest available; your invoice is simply the token math. Through n4n with a Groq routing pin, the token count is identical, so the n4n vs Groq API pricing comparison comes down to any gateway fee and the saved engineering time of not building multi-provider logic.

If you later need to shift 20% of traffic to a Claude model for quality, Groq cannot serve it. n4n routes that slice without a new contract. The marginal cost of that flexibility is invisible at the token level but real in ops.

Which to Choose

Choose Groq if: You only need Llama/Mistral-class models, want the absolute lowest latency, and can tolerate writing fallback code yourself. Your cost per million tokens is Groq’s printed price, full stop.

Choose n4n if: Your product must switch models at runtime, you want one contract and one meter for 240+ models, or you need automatic fallback when Groq (or any provider) is degraded. The n4n vs Groq API pricing gap narrows to routing overhead, which is negligible against the engineering cost of building multi-provider resilience.

Hybrid pattern: Pin hot paths to Groq direct for max speed; use n4n for long-tail model access, staging environments, and graceful degradation in production. This splits the difference and keeps your per-token economics transparent.