OmniBench

Overview

OmniBench is a pioneering universal multimodal benchmark designed to rigorously evaluate MLLMs' capability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously.

Task Description

Task Type: Universal Multimodal Understanding (Image + Audio + Text)
Input: Image, audio, and text question with choices
Output: Correct answer choice
Modalities: Visual, acoustic, and textual

Key Features

Tri-modal evaluation (image, audio, text)
Tests cross-modal reasoning abilities
Multiple-choice format
Configurable modality usage
Chain-of-thought prompting

Evaluation Notes

Default configuration uses 0-shot evaluation
Simple accuracy metric
Extra params: use_image (bool), use_audio (bool)
Supports textual alternatives for image/audio

Properties

Property	Value
Benchmark Name	`omni_bench`
Dataset ID	m-a-p/OmniBench
Paper	N/A
Tags	`Knowledge`, `MCQ`, `MultiModal`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`train`

Data Statistics

Metric	Value
Total Samples	1,142
Prompt Length (Mean)	477.61 chars
Prompt Length (Min/Max)	310 / 1280 chars

Image Statistics:

Metric	Value
Total Images	1,142
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	361x203 - 5184x3456
Formats	jpeg, png

Audio Statistics:

Metric	Value
Total Audio Files	1,142
Audio per Sample	min: 1, max: 1, mean: 1
Formats	mp3

Sample Example

Subset: default

{
  "input": [
    {
      "id": "a5cb4fd0",
      "content": [
        {
          "text": "Answer the following multiple choice question based on the image and audio content. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D. Think step by step before answering.\n\nWhat are the men doing?\n\nA) The man in jeans is taking notes from the newspaper.\nB) The man in purple is reading the newspaper.\nC) The man in jeans is playing a crossword puzzle.\nD) The man on the table is doing a crossword puzzle."
        },
        {
          "image": "[BASE64_IMAGE: png, ~3.7MB]"
        },
        {
          "audio": "[BASE64_AUDIO: mp3, ~103.6KB]",
          "format": "mp3"
        }
      ]
    }
  ],
  "choices": [
    "The man in jeans is taking notes from the newspaper.",
    "The man in purple is reading the newspaper.",
    "The man in jeans is playing a crossword puzzle.",
    "The man on the table is doing a crossword puzzle."
  ],
  "target": "C",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "index": 0,
    "task_type": "Action and Activity",
    "audio_type": "speech",
    "answer": "The man in jeans is playing a crossword puzzle.",
    "image_content": "The image depicts a scene from a television show set in a cozy, well-furnished apartment. The main focus is on two individuals sitting on a couch in the foreground. The man on the left is wearing a dark blue jacket and jeans, and he is holdin ... [TRUNCATED] ... ted with a mix of modern and vintage elements, including a colorful rug, patterned curtains, and a variety of decorative items on the shelves and walls. The overall atmosphere is warm and inviting, suggesting a comfortable and lived-in space.",
    "audio_content": "Two people talking: A: Four letters, \"circle or hoop.\" B: Ring! Damn it, ring! A: Thanks."
  }
}

Note: Some content was truncated for display.

Prompt Template

Prompt Template:

Answer the following multiple choice question based on the image and audio content. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.

{question}

{choices}

Extra Parameters

Parameter	Type	Default	Description
`use_image`	`bool`	`True`	Whether to provide the raw image. False uses textual alternative.
`use_audio`	`bool`	`True`	Whether to provide the raw audio. False uses textual alternative.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets omni_bench \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['omni_bench'],
    dataset_args={
        'omni_bench': {
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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