We Benchmarked TTFT Across OpenAI, Anthropic, and Groq Over 10,000 Requests — Here Is the Data

When running LLMs in production, two numbers dictate user experience and architecture reliability:

1. Time-To-First-Token (TTFT): How long users stare at a blank loader.
2. Failover Recovery Latency: What happens when an upstream provider rate limits (HTTP 429) or experiences an edge outage.

Over the past week, we sent 10,000 automated evaluation requests across North America, Europe, and Asia-Pacific edge nodes to measure the true network and inference performance of direct provider endpoints versus intelligent routing gateways.

All methodology and runner code are open-source and reproducible at SentioGate/llm-routing-benchmarks.

The Executive Benchmark Matrix

Key Takeaway 1: The "Middleman Proxy Tax" is Worse Than You Think

Many developer teams adopt LLM proxies to simplify multi-model routing. However, our measurements revealed that typical proxy relays introduce between 45ms and 95ms of unneeded latency per request.

Worse, most commercial gateways operate as token resellers, taking a 5% to 15% markup on top of raw provider rates.

With SentioGate, the router acts strictly as a lightweight Bring-Your-Own-Key (BYOK) traffic orchestrator at the edge:

• Overhead is under 18ms across all tested regions.
• 0% token markup: Prompts are authenticated with your own keys directly against the upstream inference clusters.

Key Takeaway 2: Automatic Circuit-Breaking vs. Client 429 Failures

In our test suite, we injected randomized HTTP 429 (Rate Limit) and HTTP 503 (Overloaded) error signals into primary routes:

• Direct Client SDKs: Instantly threw exceptions back into the calling application. In production, this causes user-facing errors, stalled chat interfaces, or failed background agent jobs.
• SentioGate Hot Failover: The gateway detected the upstream 429 in 12ms, opened the circuit breaker for that replica, and rerouted the active payload to an alternate provider (e.g. Anthropic Claude 3.5 Sonnet or Gemini 1.5 Pro) in under 45ms—completely transparent to the client.

Client Request ───▶ [ SentioGate Edge ] ──(429 Throttled)──❌ OpenAI
                           │
                           └──(14ms Auto-Failover)────✅ Anthropic

How to Run This in Your Code

You don't need to rebuild your application to get zero-markup failover. SentioGate is 100% drop-in compatible with standard OpenAI client libraries:

Python

from openai import OpenAI

# Just point base_url to SentioGate
client = OpenAI(
    base_url="https://api.sentiogate.dev/v1",
    api_key=os.environ["SENTIOGATE_API_KEY"]
)

response = client.chat.completions.create(
    model="auto:fastest",  # Intelligently routes to fastest replica
    messages=[{"role": "user", "content": "Benchmark test prompt"}]
)

TypeScript / JavaScript

import { AutoRouter } from "@sentiogate/sdk";

const router = new AutoRouter({
  apiKey: process.env.SENTIOGATE_API_KEY,
});

const completion = await router.createChatCompletion({
  model: "auto:cost-efficient",
  messages: [{ role: "user", content: "Optimize database query" }]
});

Reproduce the Data & Next Steps

1. Live Dashboard & Real-Time Metrics: Explore live uptime and regional latency graphs at sentiogate.app/benchmarks.
2. Interactive Simulator: Test the routing logic in your browser at sentiogate.github.io.
3. Reproduce the Benchmark: Clone the test runner on GitHub: SentioGate/llm-routing-benchmarks.
4. Compare Solutions: Read our detailed architecture breakdown vs LiteLLM and OpenRouter at sentiogate.app/compare.