Claude API vs OpenAI API Latency: A Practical Comparison

If you're choosing between the Claude API and the OpenAI API and latency is a deciding factor, the honest answer is: it depends on the model tier, the request shape, and your network path to each provider's infrastructure, and the gap changes often enough that any specific number you read today may be stale next month. Neither provider publishes guaranteed latency SLAs for standard tiers, and both ship model updates that shift performance without warning.

What you can reason about is the structure of latency — time to first token, tokens per second during streaming, and the effect of prompt length, context size, and tool use. This article breaks those down so you can benchmark your own workload instead of relying on someone else's screenshot from a different region, a different model, and a different day.

Why "which API is faster" is the wrong first question

Latency on both platforms depends on several independent variables that have nothing to do with which company built the model:

• Model size and tier — Claude Opus vs Sonnet vs Haiku, or GPT-4-class vs smaller GPT models, behave completely differently. Comparing a large reasoning model from one vendor against a small fast model from the other tells you nothing useful.
• Prompt and context length — longer system prompts and conversation history increase time to first token on every provider, because the model has to process that context before generating anything.
• Streaming vs non-streaming — if you wait for the full completion before rendering anything, you're measuring total generation time, not responsiveness. Streaming changes the perceived latency dramatically even if total time is similar.
• Tool use and function calling — a request that triggers a tool call, waits for your code to execute it, then sends the result back for a second model pass will always be slower than a single-turn text response, regardless of provider.
• Region and network hops — where your servers run relative to the provider's API endpoints affects round-trip time independently of model speed.
• Load and time of day — both providers see variable queueing under peak traffic, and this fluctuates by the hour.

If you only compare "Claude API vs OpenAI API latency" as a single number, you're averaging across all of these variables and the result won't match your actual production traffic pattern.

How to actually measure it for your use case

The only reliable comparison is one you run yourself, against your own prompts, from your own infrastructure. Measure these three things separately:

1. Time to first token (TTFB) — how long between sending the request and receiving the first streamed chunk.
2. Tokens per second — once generation starts, how fast does output arrive.
3. Total request time — relevant if you're not streaming, or if downstream logic needs the full response before acting.

A simple timing wrapper in JavaScript:

async function measureLatency(url, payload, apiKey) {
  const start = Date.now();
  let firstTokenAt = null;

  const res = await fetch(url, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${apiKey}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({ ...payload, stream: true })
  });

  const reader = res.body.getReader();
  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
    if (!firstTokenAt) firstTokenAt = Date.now();
  }

  const end = Date.now();
  return {
    ttft_ms: firstTokenAt - start,
    total_ms: end - start
  };
}

Run the same wrapper against both APIs with equivalent prompts (same length, same instructions, same expected output size) and repeat enough times to average out noise — a single request tells you almost nothing because of normal variance.

For a Claude-based request through an HTTPS endpoint, a basic curl check looks like this:

curl -s -o /dev/null -w "%{time_starttransfer} %{time_total}\n" \
  https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet",
    "max_tokens": 200,
    "messages": [{"role": "user", "content": "Summarize this in two sentences: ..."}]
  }'

time_starttransfer approximates time to first byte, and time_total gives you the full request duration. Run this against your OpenAI equivalent with the same prompt length and you have a real, workload-specific comparison instead of a generic one.

Does a wrapper layer add latency?

If you're accessing Claude through a proxy or gateway rather than calling the model provider directly, it's fair to ask whether that adds meaningful overhead. A well-built HTTPS layer that streams responses through without buffering the full output adds only the extra network hop — typically single-digit to low double-digit milliseconds, far below the variance you'll see from model generation time itself.

SubToAPI is built this way: it exposes your existing Claude access as a standard HTTPS API with streaming support, so you get token-by-token output rather than waiting for a buffered full response. See /docs/streaming for how streaming is implemented, and /docs/quickstart if you want to run the timing test above against a live endpoint in a few minutes. Plans start at the Solo tier (/pricing), with a free trial available at /signup.

Practical takeaways

• Don't compare flagship models to smaller models across providers — compare tiers that are actually equivalent in capability.
• Always test with streaming enabled if your product streams; non-streaming benchmarks overstate perceived latency differences.
• Factor in tool use separately — if your workload relies on function calling, benchmark the full round trip including your own execution time, not just the model call. See /docs/tools for how tool use requests are structured.
• Re-run your benchmark periodically. Model versions and infrastructure change on both sides, and a comparison from six months ago may no longer hold.
• Test from the same region/infrastructure you'll run in production — a benchmark run from a laptop on a home network isn't representative of server-to-server latency.

FAQ

Is Claude or OpenAI's API faster for streaming responses?

There's no fixed answer — it depends on the model tier and prompt. Smaller/faster models on both sides (like Claude Haiku or smaller GPT models) generally have lower time to first token than larger reasoning-focused models. Benchmark your specific prompts on both to get a real answer.

Does longer context slow down the Claude API more than OpenAI's?

Longer context increases time to first token on both platforms because the model must process the full input before generating output. The relative impact depends on the specific model version in use, so test with your actual context window size rather than assuming one provider degrades less.

Will using an API wrapper like SubToAPI add noticeable latency to Claude requests?

A properly implemented streaming proxy adds minimal overhead — mainly the extra network hop — since it passes tokens through as they arrive rather than buffering the full response. Check /docs/streaming for details on how response streaming is handled.