How many models does each LLM API gateway actually support

The headline number in any LLM API gateway model count comparison is seductive but incomplete. A gateway might advertise 300 models, yet half are fine-tuned variants, deprecated snapshots, or region-locked endpoints you will never call in production. The useful question is not “how many model IDs are in the catalog” but “how many distinct capabilities can I reliably route to through one interface.”

What “support” actually means

Engineers conflate three different things when they read “we support 200 models”:

1. Catalog exposure – the gateway returns the model ID from /v1/models.
2. Inference capability – the gateway can actually complete a request to that model without erroring out.
3. Operational maturity – the model is monitored, fallbacks exist, and pricing is transparent.

A static list proves only the first. I have seen gateways list models that return 403 because the underlying provider key wasn’t provisioned. That is not support.

The naive count trap

Run a quick count against any OpenAI-compatible endpoint:

from openai import OpenAI

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

The integer you get is a starting point, not a scoreboard. OpenRouter’s catalog runs into the hundreds, including many community uploads and parameter variants (llama-3-8b-instruct vs llama-3-8b-instruct-abliterated). For a production system, you filter that list down to perhaps a dozen models you trust.

In an LLM API gateway model count comparison, the raw integer favors aggregators that expose everything. But breadth without curation creates decision paralysis and hidden failure modes.

OpenRouter: breadth as a product

OpenRouter built its brand on catalog size. It maps model IDs like anthropic/claude-3.5-sonnet and google/gemini-pro-1.5 behind one key. The strength is exactly that: one endpoint, many providers, and a normalized chat API.

The weakness is variability. Some models are served by third-party providers with loose SLAs. If you request a smaller open-weight model, it may be hosted on a single GPU box that gets pulled offline. The gateway will return an error, not a fallback, unless you explicitly handle it.

{
  "id": "mistralai/mixtral-8x7b-instruct",
  "object": "model",
  "created": 1700000000,
  "owned_by": "mistralai"
}

That JSON is real in shape. But the created timestamp and availability change daily.

Self-hosted gateways: LiteLLM and Cloudflare

LiteLLM’s proxy is the opposite philosophy. It does not ship a fixed catalog. You write a config mapping logical names to provider credentials:

model_list:
  - model_name: "gpt-4o"
    litellm_params:
      model: "azure/gpt-4o"
      api_key: "os.environ/AZURE_KEY"
  - model_name: "claude-prod"
    litellm_params:
      model: "anthropic/claude-3-5-sonnet-20240620"
      api_key: "os.environ/ANTHROPIC_KEY"

The “model count” is whatever you configure. For a team that already knows exactly which 15 models it needs, this is ideal. You get full control, spend tracking, and virtual keys. The cost is operational ownership: you patch the proxy, you rotate keys, you monitor health.

Cloudflare AI Gateway sits between. It is a pass-through proxy; you bring your own provider accounts and it logs, caches, and rate-limits. It does not expand your catalog—it multiplies your existing one across Cloudflare’s edge. If you only have OpenAI and Anthropic keys, you support exactly those models, no more.

n4n.ai and the 240+ endpoint

A gateway like n4n.ai collapses the provider matrix into one OpenAI-compatible endpoint addressing 240+ models, with automatic fallback when a provider is rate-limited or degraded. That number is not just a list; the fallback behavior means a request to claude-3-5-sonnet can shift to an equivalent model if Anthropic’s API throws 429. For a latency-sensitive service, that operational guarantee matters more than a static count.

The tradeoff is reduced ability to pin a specific provider region or fine-tune routing logic compared to self-hosted LiteLLM. You trade control for zero infrastructure.

Provider-native gateways

Do not ignore the source. OpenAI’s API supports maybe a dozen first-party models. Azure AI Studio adds a curated set of open-weight and partner models—perhaps 30–40 in a given region. These numbers are small but the models are battle-tested and SLA-backed.

If your LLM API gateway model count comparison includes native gateways, they will lose on breadth. They win on predictability. A gpt-4o call to Azure has a stated uptime commitment; the same model behind a third-party aggregator may not.

Catalog breadth vs routing control

The real axis is not count, it is leverage per model.

• Aggregator (OpenRouter, n4n.ai): High breadth, low setup. You accept their normalization, their pricing markup, and their fallback logic.
• Proxy (LiteLLM): Infinite flexibility, zero catalog until you build it. You own the reliability story.
• Native (OpenAI, Azure): Minimal catalog, maximum provider assurance.

An LLM API gateway model count comparison that ranks by integer size pushes you toward aggregators. That is fine if you are prototyping. It is risky if you run a paid product with a p99 latency budget.

Hidden costs of a large catalog

More models means more surface area for:

• Version drift – claude-3-opus-20240229 vs claude-3-opus-latest. Which one did you call yesterday?
• Price changes – Aggregators pass through provider pricing but add margin. A 240-model gateway may reprice 40 of them in a month.
• Capability mismatch – A model listed as “vision” may not accept the same image URL format across providers.

I have debugged a production incident where a gateway silently swapped a deprecated Mistral variant because the ID string matched a regex. The output quality dropped; nobody noticed for a day because the chat API still returned 200.

How to evaluate for your system

Stop counting. Do this instead:

1. List the five models you actually need.
2. Confirm the gateway returns them from /v1/models with correct context window metadata.
3. Send a synthetic request with a timeout. Measure error rate over 100 calls.
4. Check whether the gateway forwards cache_control hints or lets you set a routing directive.

curl -s https://your-gateway/v1/models \
  -H "Authorization: Bearer $KEY" | jq '.data[] | select(.id | test("claude-3-5"))'

If that command returns nothing, the gateway’s total count is irrelevant to you.

Decisive takeaway

The LLM API gateway model count comparison is only useful as a filter, not a ranking. If you need maximum model diversity with zero ops, an aggregator with automatic fallback (such as n4n.ai or OpenRouter) is the pragmatic choice—pick based on fallback behavior and metering, not the headline number. If you need strict control over provider routing and spend, self-host LiteLLM and treat the catalog as a configuration file. Native provider APIs remain the best option when you need fewer than ten models and contractual uptime.

Choose the gateway that supports the models you will ship, not the ones that inflate a landing page. The count is a vanity metric; effective routing is the real feature.