Profile the OpenAI Responses API with AIPerf | NVIDIA AIPerf Documentation

This guide covers benchmarking servers that implement the OpenAI Responses API (POST /v1/responses) using AIPerf.

The Responses API is OpenAI’s newer API primitive that replaces Chat Completions for new projects. It supports text, images, audio, streaming, and reasoning output.

Overview

AIPerf’s responses endpoint type handles the key differences between the Responses API and Chat Completions:

Chat Completions	Responses API
`messages` array	`input` array
`system` role message	Top-level `instructions` field
`max_completion_tokens`	`max_output_tokens`
`{"type": "text", ...}` content	`{"type": "input_text", ...}` content
`{"type": "image_url", ...}` content	`{"type": "input_image", ...}` content
`choices[0].delta.content` (streaming)	`response.output_text.delta` event (streaming)
`choices[0].message.content` (non-streaming)	`output[].content[].text` (non-streaming)

Start a Server

Launch an OpenAI Responses API-compatible server. For example, using a vLLM server:

$ docker pull vllm/vllm-openai:latest
$ docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \
>   --model Qwen/Qwen3-0.6B --reasoning-parser qwen3

Verify the server is ready:

$ curl -s http://localhost:8000/v1/responses \
>   -H "Content-Type: application/json" \
>   -d '{
>     "model": "Qwen/Qwen3-0.6B",
>     "input": [{"role": "user", "content": "Hello"}],
>     "max_output_tokens": 10
>   }' | jq

Profile with Synthetic Inputs

Run AIPerf against the Responses API endpoint using synthetic inputs:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --synthetic-input-tokens-mean 100 \
>     --synthetic-input-tokens-stddev 0 \
>     --output-tokens-mean 200 \
>     --output-tokens-stddev 0 \
>     --url localhost:8000 \
>     --request-count 20

Sample Output:

INFO     Starting AIPerf System
INFO     AIPerf System is PROFILING
Profiling: 20/20 |████████████████████████| 100% [00:35<00:00]
INFO     Benchmark completed successfully
            NVIDIA AIPerf | LLM Metrics
┃                      Metric ┃     avg ┃     min ┃     max ┃     p99 ┃     p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│        Request Latency (ms) │ 1678.90 │ 1456.34 │ 1923.45 │ 1923.45 │ 1667.23 │
│    Time to First Token (ms) │  234.56 │  198.34 │  289.12 │  289.12 │  231.45 │
│    Inter Token Latency (ms) │   13.89 │   11.23 │   17.45 │   17.45 │   13.67 │
│ Output Token Count (tokens) │  200.00 │  200.00 │  200.00 │  200.00 │  200.00 │
│  Request Throughput (req/s) │    5.67 │       - │       - │       - │       - │
└─────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┘

Profile with Custom Input Files

Create a JSONL input file:

$ cat <<EOF > inputs.jsonl
$ {"texts": ["Explain quantum computing in simple terms."]}
$ {"texts": ["Write a haiku about machine learning."]}
$ EOF

Run AIPerf:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --input-file inputs.jsonl \
>     --custom-dataset-type single_turn \
>     --url localhost:8000 \
>     --request-count 10

System Instructions

In the Responses API, system instructions use a top-level instructions field rather than a system role message. AIPerf handles this mapping automatically when you use --shared-system-prompt-length to generate a synthetic system prompt:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --shared-system-prompt-length 50 \
>     --synthetic-input-tokens-mean 100 \
>     --output-tokens-mean 200 \
>     --url localhost:8000 \
>     --request-count 20

This generates a synthetic system prompt of approximately 50 tokens and places it in the "instructions" field of the Responses API payload, rather than adding a system message to the input array. The same prompt is shared across all requests in the session.

Vision (Image Inputs)

Profile vision-capable models with synthetic images:

$ aiperf profile \
>     --model Qwen/Qwen2-VL-2B-Instruct \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --image-width-mean 512 \
>     --image-height-mean 512 \
>     --synthetic-input-tokens-mean 100 \
>     --streaming \
>     --url localhost:8000 \
>     --request-count 20 \
>     --concurrency 4

Image inputs are formatted as {"type": "input_image", "image_url": "<url>"} in the Responses API (compared to {"type": "image_url", "image_url": {"url": "<url>"}} in Chat Completions).

Audio Inputs

Profile audio-capable models with the Responses API:

$ aiperf profile \
>     --model Qwen/Qwen2-Audio-7B-Instruct \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --audio-length-mean 5.0 \
>     --audio-format wav \
>     --audio-sample-rates 16 \
>     --url localhost:8000 \
>     --request-count 20

Audio inputs are formatted as {"type": "input_audio", "input_audio": {"data": "<base64>", "format": "<fmt>"}}, the same structure used by Chat Completions.

See the Audio tutorial for details on audio input configuration and supported formats.

Non-Streaming Mode

Run without streaming to get full responses:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --synthetic-input-tokens-mean 100 \
>     --output-tokens-mean 200 \
>     --url localhost:8000 \
>     --request-count 20

Without --streaming, time-to-first-token (TTFT) and inter-token latency (ITL) metrics are not available. Use streaming mode for the most detailed latency breakdown.

Concurrency and Rate Control

Control load generation the same way as other endpoint types:

$ # Concurrency-based
$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --concurrency 10 \
>     --url localhost:8000 \
>     --request-count 100
$ 
$ # Request-rate-based
$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --request-rate 5 \
>     --url localhost:8000 \
>     --request-count 100

Multi-Turn Conversations

Benchmark multi-turn conversations using the Responses API:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --conversation-num 10 \
>     --conversation-turn-mean 3 \
>     --synthetic-input-tokens-mean 100 \
>     --output-tokens-mean 200 \
>     --url localhost:8000

See the Multi-Turn Conversations tutorial for details on conversation control parameters.

Server Token Counts

Use server-reported token counts instead of client-side tokenization:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --use-server-token-count \
>     --url localhost:8000 \
>     --request-count 20

When --use-server-token-count is enabled with streaming, AIPerf automatically sets stream_options.include_usage in the request payload to receive usage data in the response.completed event.

Extra Parameters

Pass additional API parameters using --extra-inputs:

$ aiperf profile \
>     --model Qwen/Qwen3-0.6B \
>     --endpoint-type responses \
>     --endpoint /v1/responses \
>     --streaming \
>     --extra-inputs temperature:0.7 \
>     --extra-inputs top_p:0.9 \
>     --url localhost:8000 \
>     --request-count 20

Key Differences from Chat Completions

When migrating AIPerf benchmarks from --endpoint-type chat to --endpoint-type responses:

Change --endpoint-type chat to --endpoint-type responses
Change --endpoint /v1/chat/completions to --endpoint /v1/responses
The --use-legacy-max-tokens flag is not applicable (the Responses API always uses max_output_tokens)
All other AIPerf flags (--streaming, --concurrency, --extra-inputs, etc.) work the same way

Streaming Event Handling

For reference, AIPerf processes these Responses API streaming events:

Event Type	Data Extracted
`response.output_text.delta`	Text content delta
`response.reasoning_text.delta`	Reasoning content delta
`response.output_text.done`	Final text (fallback for providers that emit text only in done events)
`response.completed`	Usage statistics
All other events	Skipped

This enables accurate measurement of TTFT, ITL, and token throughput metrics when streaming is enabled.