OpenAI API Load Testing: Benchmark Throughput and Latency (2026)

The OpenAI API doesn’t just respond; it actively adapts its internal resource allocation based on your request patterns.

Let’s see what that looks like when we push it. Imagine we’re hitting the gpt-3.5-turbo endpoint for a simple chat completion.

# Simulate a single request and measure latency
curl -s -o /dev/null -w "HTTP_CODE: %{http_code} TTFB: %{time_total}\n" \
  -X POST "https://api.openai.com/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

If you run this once, you’ll get a latency number, maybe around 1-2 seconds. But what if we do it a thousand times, rapidly? We’d use a tool like hey or wrk.

# Example using 'hey' to simulate 100 concurrent users for 30 seconds
hey -n 1000 -c 100 -m POST -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Hello!"}]}' \
  https://api.openai.com/v1/chat/completions

The throughput (requests per second) will be far lower than you might expect if you just extrapolated from the single-request latency. And the latency will likely increase, with a wider distribution of response times. This isn’t just network overhead; it’s the API’s internal queuing and scheduling kicking in.

The problem OpenAI’s API load balancing solves is efficiently distributing a massive, unpredictable global demand across a finite, but highly scalable, pool of GPU and CPU resources. It needs to prioritize, throttle, and manage contention without sacrificing overall system stability or user experience for the majority.

Internally, when you send a request, it doesn’t immediately hit a dedicated model instance. It enters a request queue. From there, it’s picked up by a dispatcher that determines the best available worker (a server with the model loaded). This dispatcher considers factors like:

Model Availability: Is the gpt-3.5-turbo model loaded and ready on any worker?
Worker Load: How many other requests is this worker currently processing?
Request Priority: While not explicitly exposed for general API users, OpenAI’s internal systems might have priority queues for certain types of traffic or premium tiers.
Rate Limits: Your account’s rate limits (RPM - requests per minute, TPM - tokens per minute) are enforced before the request even reaches a model worker, often at the API gateway level.

The primary levers you control are:

Model Choice: Different models (gpt-4, gpt-3.5-turbo, text-embedding-ada-002) have vastly different computational requirements and thus different throughput ceilings. gpt-3.5-turbo is significantly faster and cheaper because it can be served more densely.
Prompt/Completion Length: Longer prompts and completions consume more tokens and require more processing time, directly impacting how many requests a single worker can handle concurrently.
Concurrency: How many requests you send simultaneously. Your hey -c 100 example is a direct lever. OpenAI’s system is designed to handle high concurrency, but it will naturally hit limits.
Rate Limits: Your account’s assigned rate limits are the hard ceiling on how fast you can send requests. Exceeding them results in 429 Too Many Requests errors. You can request increases through OpenAI’s platform.

The most surprising thing about how OpenAI’s API handles load is that it doesn’t strictly allocate a fixed amount of processing power per user per request. Instead, it operates a sophisticated, multi-layered queuing and scheduling system that dynamically assigns available processing resources to incoming requests. This means that even if you have a high rate limit, your requests are still competing, albeit probabilistically, for the same pool of shared, optimized hardware. The system aims to maximize overall throughput and fairness across all users, rather than guaranteeing a dedicated slice of compute for any single user at any given millisecond.

When you hit your rate limits (e.g., 429 errors), the system has already decided your request cannot be processed at this moment by the gateway before it even attempts to schedule it onto a model worker. This prevents downstream overload and ensures a more stable experience for everyone else.

The next thing you’ll likely explore is how to manage those 429 errors gracefully, implementing exponential backoff and jitter.