Parameter

Execute evalscope perf --help to get a full parameter description.

Basic Settings

Parameter	Type	Description	Default
--model	str	Name or path of the test model	-
--url	str	API address, supporting /chat/completions, /completions, and /responses endpoints	-
--name	str	Name for wandb/swanlab database result and result database	{model_name}_{current_time}
--api	str	Service API type • openai: OpenAI-compatible Chat Completions API (requires --url) • openai_responses: OpenAI official Responses API • openai_embedding: OpenAI-compatible Embedding API • openai_rerank: OpenAI/Cohere-compatible Rerank API • local: Start local transformers inference • local_vllm: Start local vLLM inference service • Custom: See Custom API Guide	-
--port	int	Port for local inference service Only applicable to local and local_vllm	8877
--attn-implementation	str	Attention implementation method Only effective when api=local	None (Optional: flash_attention_2, eager, sdpa)
--api-key	str	API key	None
--debug	bool	Whether to output debug information	False

Network Configuration

Parameter	Type	Description	Default
--total-timeout	int	Total timeout for each request (seconds)	21600 (6 hours)
--connect-timeout	int	Network connection timeout (seconds)	None
--read-timeout	int	Network read timeout (seconds)	None
--headers	str	Additional HTTP headers Format: key1=value1 key2=value2 Will be used for each query	-
--no-test-connection	bool	Do not send connection test, start stress test directly	False

Request Control

Parameter	Type	Description	Default
--parallel	list[int]	Number of concurrent requests Can input multiple values separated by spaces	1
--number	list[int]	Total number of requests to be sent Can input multiple values (must correspond one-to-one with parallel)	1000
--rate	float	Request scheduling rate (requests/second) • -1: No rate pacing; in the default closed-loop mode, requests are scheduled as fast as possible, but the number of in-flight HTTP requests is still capped by --parallel, so requests are not all sent to the server at once • > 0: Requests are scheduled following a Poisson arrival model — the inter-arrival interval follows an exponential distribution with mean 1/rate, resulting in an average of rate scheduled requests per second	-1
--log-every-n-query	int	Log every N queries	100
--stream	bool	Whether to use SSE stream output Must be enabled to measure TTFT (Time to First Token) metric	True
--sleep-interval	int	Sleep time between each performance test (seconds) Helps avoid overloading the server	5
--open-loop	bool	Enable open-loop mode: dispatch requests following a Poisson arrival schedule without semaphore backpressure. Requests are fired at the rate set by --rate regardless of whether the server has finished processing previous requests. • --rate becomes the sweep variable (accepts multiple values), replacing --parallel to drive multi-run iterations • --number must have the same length as --rate; each pair (rate, number) corresponds to one independent run • --parallel is ignored in this mode (internally set to -1 / INF) See Usage Example	False
--warmup-num	float	Number or ratio of warmup requests: • 0: disabled (default) • >= 1: absolute count, e.g. --warmup-num 10 sends 10 warmup requests • 0 < value < 1: ratio mode, e.g. --warmup-num 0.1 = 10% of --number Warmup requests are sent with the same concurrency/rate as the benchmark but excluded from performance metrics Useful for eliminating cold-start effects (KV-cache filling, JIT compilation, etc.) See Usage Example	0
--duration	float	Wall-clock budget for one benchmark run (seconds) Soft-exit semantics: once the deadline elapses no new requests are dispatched, but already in-flight requests are allowed to finish before exit In multi-turn mode “in-flight” means already-claimed traces run every remaining turn (trace-level soft exit, aligned with upstream trie) When combined with --number, whichever cap is hit first ends the run	None

Tip

Closed-loop (default) vs Open-loop (--open-loop) — parameter behaviour comparison:

	Closed-loop (default)	Open-loop (--open-loop)
--rate	Controls request scheduling rate (-1 = no pacing, but still bounded by the --parallel concurrency cap; R = Poisson-arrival mean)	Controls dispatch rate; must be > 0; accepts multiple values (e.g. 5 10 20), each driving one independent run
--number	Total requests per run; must match --parallel in length	Total requests per run; must match --rate in length
--parallel	Max in-flight requests; each worker waits for a response before sending the next (backpressure)	Ignored; concurrency is unbounded (INF); requests are fired on schedule without waiting for responses
Use case	Measure latency and throughput under controlled concurrency	Simulate realistic traffic (arrivals independent of service time); sweep throughput-latency curve across multiple rates

SLA Settings

Parameter	Type	Description	Default
--sla-auto-tune	bool	Whether to enable SLA auto-tuning mode	False
--sla-variable	str	Variable for auto-tuning Options: parallel (concurrency), rate (request rate)	parallel
--sla-params	str	SLA constraint conditions JSON string Supported metrics: avg_latency, p99_latency, avg_ttft, p99_ttft, avg_tpot, p99_tpot, rps, tps Supported operators: <=, <, min (for latency metrics); >=, >, max (for throughput metrics) Example: '[{"p99_latency": "<=2"}]'	None
--sla-upper-bound	int	Upper bound of the tuned SLA variable search range	65536
--sla-lower-bound	int	Lower bound of the tuned SLA variable search range	1
--sla-fixed-parallel	int	Fixed parallel workers used when --sla-variable=rate; defaults to --sla-upper-bound for backward compatibility	None
--sla-num-runs	int	Number of runs per concurrency level (average taken)	3
--sla-number-multiplier	float	Multiplier of total requests relative to the tuned variable (concurrency or rate), i.e. number = round(variable × N); defaults to 2 when not set	None

See also

For details on using the SLA auto-tuning feature, see the Auto-tuning Guide.

Prompt Settings

Parameter	Type	Description	Default
--max-prompt-length	int	Maximum input prompt length Prompts exceeding this length will be discarded	131072
--min-prompt-length	int	Minimum input prompt length Prompts shorter than this will be discarded	0
--prefix-length	int	Length of the prompt prefix Only effective for random dataset	0
--prompt	str	Specify request prompt String or local file (specify via @/path/to/file) Higher priority than dataset Example: @./prompt.txt	-
--query-template	str	Specify query template JSON string or local file (specify via @/path/to/file) Example: @./query_template.json	-
--apply-chat-template	bool	Whether to apply chat template	None (automatically determined based on URL suffix)
--image-width	int	Image width for random VL dataset	224
--image-height	int	Image height for random VL dataset	224
--image-format	str	Image format for random VL dataset	RGB
--image-num	int	Number of images for random VL dataset	1
--image-patch-size	int	Patch size for the image Only used for local image token calculation	28

Dataset Configuration

Parameter	Type	Description	Default
--dataset	str	Dataset mode, see table below for details	-
--dataset-path	str	Dataset file path Used in conjunction with dataset	-

Dataset Mode Description

Text / Chat

Mode	Description	Supports dataset-path
openqa	Automatically downloads OpenQA from ModelScope Prompts are relatively short (usually <100 tokens) Uses question field from jsonl file when dataset_path is specified	✓
longalpaca	Automatically downloads LongAlpaca-12k from ModelScope Prompts are much longer (generally >6000 tokens) Uses instruction field from jsonl file when dataset_path is specified	✓
line_by_line	Each line in txt file is used as a separate prompt Requires dataset_path	✓ (Required)
random	Randomly generates prompts based on prefix-length, max-prompt-length, and min-prompt-length Requires tokenizer-path Usage example	✗
custom	Custom dataset parser See Custom Dataset Guide	✓

Multimodal

Mode	Description	Supports dataset-path
flickr8k	Automatically downloads Flick8k from ModelScope Builds image-text inputs; large dataset suitable for evaluating multimodal models	✗
kontext_bench	Automatically downloads Kontext-Bench from ModelScope Builds image-text inputs; approximately 1,000 samples, suitable for quick evaluation of multimodal models	✗
random_vl	Randomly generates both image and text inputs Based on random, with additional image-related parameters Usage example	✗

Embedding

Mode	Description	Supports dataset-path
embedding	Load text data from file to evaluate Embedding model Supports Line-by-line (TXT) or JSONL format (with text field)	✓ (Required)
random_embedding	Randomly generate queries based on max-prompt-length and min-prompt-length to evaluate Embedding model Must specify tokenizer-path	✗
embedding_batch	Batch send text data to evaluate Embedding model Load data from file Supports --extra-args '{"batch_size": 8}' to set batch size	✓ (Required)
random_embedding_batch	Batch send randomly generated query data to evaluate Embedding model Must specify tokenizer-path Supports --extra-args '{"batch_size": 8}' to set batch size	✗

Rerank

Mode	Description	Supports dataset-path
rerank	Load Query-Document pairs from file to evaluate Rerank model Supports JSONL format (with query and documents fields)	✓ (Required)
random_rerank	Randomly generate query data to evaluate Rerank model Must specify tokenizer-path Supports --extra-args '{"num_documents": 10, "document_length_ratio": 5}' to set number of documents and length ratio	✗

Multi-turn Conversation

Must be used with --multi-turn. See the Multi-turn Benchmark Guide for details.

Mode	Description	Supports dataset-path
random_multi_turn	Synthetic multi-turn conversations; each turn randomly generates a token sequence Requires --tokenizer-path and --max-turns Usage example	✗
share_gpt_zh_multi_turn	Automatically downloads the Chinese ShareGPT dataset (~70k conversations) from ModelScope, preserving full multi-turn conversations Usage example	✓
share_gpt_en_multi_turn	Automatically downloads the English ShareGPT dataset (~70k conversations) from ModelScope, preserving full multi-turn conversations	✓
custom_multi_turn	Uses a local JSONL file as a custom multi-turn dataset Each line must be a JSON array of OpenAI message dicts; ideal for benchmarking with your own conversation data Requires --dataset-path Usage example	✓ (Required)

Model Settings

Parameter	Type	Description	Default
--tokenizer-path	str	Tokenizer weights path Used to calculate the number of tokens in input and output Usually located in the same directory as model weights	None
--frequency-penalty	float	frequency_penalty value	-
--logprobs	bool	Whether to return logarithmic probabilities	-
--max-tokens	int	Maximum number of tokens that can be generated	-
--min-tokens	int	Minimum number of tokens to generate Note: Not all model services support this parameter For vLLM>=0.8.1, you need to additionally set --extra-args '{"ignore_eos": true}'	-
--n-choices	int	Number of completion choices to generate	-
--seed	int	Random seed	None
--stop	str	Tokens that stop the generation	-
--stop-token-ids	list[int]	IDs of tokens that stop the generation	-
--temperature	float	Sampling temperature	0
--top-p	float	Top-p sampling	-
--top-k	int	Top-k sampling	-
--extra-args	str	Additional parameters to be passed in the request body JSON string format Example: '{"ignore_eos": true}'	-
--tokenize-prompt	bool	Tokenize the prompt client-side into a token-ID list and send it directly via /v1/completions, bypassing server-side re-tokenization	False

Data Storage

Parameter	Type	Description	Default
--visualizer	str	Visualizer to use Options: wandb, swanlab, clearml If set, metrics will be saved to the specified visualizer	None
--enable-progress-tracker	bool	Whether to enable progress tracking, writing hierarchical stress-test progress to progress.json in real time, queryable via the service API	False
--wandb-api-key	str	wandb API key for logging metrics to wandb Deprecated, please use --visualizer wandb instead	-
--swanlab-api-key	str	swanlab API key for logging metrics to swanlab Deprecated, please use --visualizer swanlab instead	-
--outputs-dir	str	Output file path	./outputs
--no-timestamp	bool	Exclude timestamp from output directory name	False

Multi-turn Settings

Parameter	Type	Description	Default
--multi-turn	bool	Enable multi-turn conversation benchmark mode; --number is the total number of turns to send and --parallel is the number of concurrent turn-level requests	False
--min-turns	int	Minimum number of user turns per conversation; used by random_multi_turn and swe_smith	1
--max-turns	int	Maximum number of user turns per conversation; required for random_multi_turn; optional for ShareGPT / custom_multi_turn (truncates long conversations); for swe_smith it’s the upper bound for per-conversation turn sampling, falling back to --min-turns when unset	None

See also

For details on using the multi-turn benchmark feature, see the Multi-turn Benchmark Guide.

Other Parameters

Parameter	Type	Description	Default
--db-commit-interval	int	Number of rows buffered before writing results to SQLite database	1000
--queue-size-multiplier	int	Maximum size of the request queue Calculated as: parallel * multiplier	5
--in-flight-task-multiplier	int	Maximum number of in-flight tasks Calculated as: parallel * multiplier	2