MMBench_test

Test set of MMBench, a bilingual benchmark for assessing multi-modal capabilities of vision-language models through multiple-choice questions in both English and Chinese, providing systematic evaluation across diverse vision-language tasks.

Qwen2-VL-72B-Instruct from Alibaba Cloud / Qwen Team currently leads the MMBench_test leaderboard with a score of 0.865 across 1 evaluated AI models.

Paper

Alibaba Cloud / Qwen TeamQwen2-VL-72B-Instruct leads with 86.5%.

Progress Over Time

Interactive timeline showing model performance evolution on MMBench_test

State-of-the-art frontier
Open
Proprietary

MMBench_test Leaderboard

1 models
ContextCostLicense
1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
73B
Notice missing or incorrect data?

FAQ

Common questions about MMBench_test.

What is the MMBench_test benchmark?

Test set of MMBench, a bilingual benchmark for assessing multi-modal capabilities of vision-language models through multiple-choice questions in both English and Chinese, providing systematic evaluation across diverse vision-language tasks.

What is the MMBench_test leaderboard?

The MMBench_test leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, Qwen2-VL-72B-Instruct by Alibaba Cloud / Qwen Team leads with a score of 0.865. The average score across all models is 0.865.

What is the highest MMBench_test score?

The highest MMBench_test score is 0.865, achieved by Qwen2-VL-72B-Instruct from Alibaba Cloud / Qwen Team.

How many models are evaluated on MMBench_test?

1 models have been evaluated on the MMBench_test benchmark, with 0 verified results and 1 self-reported results.

Where can I find the MMBench_test paper?

The MMBench_test paper is available at https://arxiv.org/abs/2307.06281. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does MMBench_test cover?

MMBench_test is categorized under multimodal, reasoning, and vision. The benchmark evaluates multimodal models.

More evaluations to explore

Related benchmarks in the same category

View all multimodal
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

reasoning
213 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

reasoning
119 models
AIME 2025

All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.

reasoning
107 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

reasoning
99 models
SWE-Bench Verified

A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.

reasoning
89 models
Humanity's Last Exam

Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions

reasoningmultimodal
74 models