n4n vs OpenRouter: model catalog size compared

When evaluating inference gateways, the n4n vs OpenRouter model catalog size question comes up immediately because the breadth of available models dictates what experiments you can run without swapping providers. Both route to many underlying vendors, but the way they expose that catalog and the surrounding constraints differ in ways that matter for production systems.

Capabilities: what you can actually call

OpenRouter’s catalog is a sprawling list of frontier models, open-weight checkpoints, and community fine-tunes. You can hit anthropic/claude-3.5-sonnet, meta-llama/llama-3-70b-instruct, or a random 7B fine-tune from a hobbyist. The surface area is broad enough that you can treat it as a model zoo. For discovery, the raw n4n vs OpenRouter model catalog size gap looks large: OpenRouter lists several hundred entries, including many unversioned community uploads.

n4n.ai provides a single OpenAI-compatible endpoint that addresses 240+ models spanning the same major providers. The practical difference is not raw count but curation: n4n.ai exposes production-grade weights and snapshots rather than every community upload. For a team building a product, that means fewer footguns from untested checkpoints. Model IDs are stable; a snapshot you call today will not silently change tokenizer behavior next week.

from openai import OpenAI

# OpenRouter
or_client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
or_models = or_client.models.list().data
print(len(or_models), "models on OpenRouter")

# n4n.ai (OpenAI-compatible)
n4n_client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key="sk-n4n-...",
)
n4n_models = n4n_client.models.list().data
print(len(n4n_models), "models on n4n.ai")

The code above works identically because both implement the OpenAI /v1/models contract. The returned id strings differ in namespacing: OpenRouter uses org/model while n4n.ai uses provider-native IDs or its own aliases. If you script against the list, normalize the IDs before caching.

Price and cost model

Neither gateway charges a flat subscription for model access. You pay per token, with the gateway adding a margin on top of provider cost. OpenRouter publishes a leaderboard-style pricing page where each model lists input/output per-token rates. This transparency lets you script cost comparisons.

{
  "model": "anthropic/claude-3.5-sonnet",
  "prompt": "$3.00 / 1M tokens",
  "completion": "$15.00 / 1M tokens"
}

n4n.ai applies per-token usage metering on the same underlying economics. The key engineering concern is not the listed price but the ability to attribute spend per route. With n4n.ai you get usage events per token; with OpenRouter you get them per request in the response headers and dashboard. Both pass through provider price changes; neither hides the upstream rate.

If your finance team needs to allocate cost by feature flag, both give you the raw data. OpenRouter’s UI makes ad-hoc exploration easier; n4n.ai’s metering is designed to plug into your own billing pipeline. Watch for egress: a gateway in a different region than your app can add milliseconds and cross-region bandwidth fees that dwarf token cost at high volume.

Latency and throughput

Latency is dominated by the upstream provider, not the gateway. A request to a quantized Mixtral instance will be faster on a provider with warm GPUs regardless of gateway. That said, tail latency is where gateway logic helps.

n4n.ai implements automatic fallback when a provider is rate-limited or degraded, so a single chat.completions call can silently retry against a secondary provider if the primary fails health checks. OpenRouter leaves fallback to the client unless you use its “use fallback” model suffix.

# OpenRouter: opt-in fallback via model string
curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OR_KEY" \
  -d '{"model": "anthropic/claude-3.5-sonnet:fallback", "messages": [{"role":"user","content":"hi"}]}'

For n4n.ai, fallback is transparent; you send a normal request and the gateway routes. This reduces client complexity but means you surrender some visibility into which provider served the token. Throughput under burst is similar: both open multiple downstream connections and multiplex. If you stream, set stream: true and parse SSE; both conform to the OpenAI chunk format.

Ergonomics

Both are OpenAI-compatible, so your existing openai SDK code works with a base_url swap. The differences are in headers and routing controls.

OpenRouter supports HTTP-Referer and X-Title headers for attribution, plus route parameters in the body. n4n.ai honors client routing directives and forwards provider cache-control hints, letting you force a specific provider or leverage prompt caching without custom middleware.

// TypeScript: forcing cache control through n4n.ai
const res = await fetch("https://api.n4n.ai/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${N4N_KEY}`,
    "Content-Type": "application/json",
    "X-Route": "provider:azure;cache:true", // client routing directive
  },
  body: JSON.stringify({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: "Summarize this." }],
  }),
});

OpenRouter’s equivalent is a route field in the JSON body. Both are trivial, but n4n.ai’s header-based approach composes better with existing proxy layers. Error shapes match OpenAI’s error object; you can write one retry loop for both. SDK support is identical because it is just the OpenAI client pointed elsewhere.

Ecosystem

OpenRouter has a mature web app, community forums, and a model leaderboard. You can log in, spin up a chat, and copy the curl. For solo developers, that UX is hard to beat. There are browser extensions and third-party tools that assume OpenRouter’s /api/v1 path.

n4n.ai is positioned as a infrastructure component: one endpoint, metering, fallback. It does not ship a consumer chat UI. If your system is a backend service, the lack of UI is a feature—less attack surface, fewer distractions. You integrate it like a database connection, not a website.

Limits

Rate limits are enforced at two layers: gateway and provider. OpenRouter’s free tier has low per-minute caps; paid tiers scale with spend. n4n.ai’s limits track the underlying provider quotas but mitigate spikes via fallback. Concurrency is bounded by the weakest upstream in your route.

Context windows are model-specific. Both gateways pass through the model’s native context length (e.g., 128K for Claude 3.5, 32K for some open models). Neither extends context via stacking or chunking; you must handle truncation client-side. If you exceed the limit, you get a context_length_exceeded error identical to OpenAI’s.

Head-to-head summary

Which to choose

Choose OpenRouter if: You are prototyping and want to browse a massive catalog including niche fine-tunes from a browser UI. The community leaderboard and transparent pricing page help you discover models you didn’t know existed. For hackathons or research sweeps, the raw n4n vs OpenRouter model catalog size debate goes to OpenRouter because you can pull a weird LoRA without leaving the tab.

Choose n4n.ai if: You are shipping a backend service that must not break when a provider throttles. The automatic fallback and per-token metering let you treat 240+ models as a single reliable surface. Header-based routing slots into existing service meshes without SDK changes. When the n4n vs OpenRouter model catalog size difference is filtered to “models that will stay up at 3am”, the curated set wins.

Hybrid note: Both speak the same OpenAI contract. It is reasonable to use OpenRouter in a notebook for discovery, then point your production client at n4n.ai by changing base_url. The catalog size difference shrinks once you filter to models that are actually deployable.

Engineers rarely need the absolute largest catalog; they need the models that work when traffic spikes. Pick the gateway that matches your operational posture, not the one with the bigger number on a comparison page.