VisFactor

VisFactor is a benchmark evaluating fine-grained visual factor perception and reasoning over images.

Qwen3.7-Plus from Alibaba Cloud / Qwen Team currently leads the VisFactor leaderboard with a score of 0.428 across 1 evaluated AI models.

About this benchmark

What VisFactor measures

VisFactor is a multimodal benchmark that evaluates large language models on multimodal, reasoning, and vision tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.4, with the leader reaching 0.4.

Compare leaders on the best AI for multimodal, best AI for reasoning and best AI for vision leaderboards.

Alibaba Cloud / Qwen TeamQwen3.7-Plus leads with 42.8%.

Progress Over Time

Interactive timeline showing model performance evolution on VisFactor

State-of-the-art frontier
Open
Proprietary

VisFactor Leaderboard

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

FAQ

Common questions about VisFactor.

What is the VisFactor benchmark?

VisFactor is a benchmark evaluating fine-grained visual factor perception and reasoning over images.

What is the VisFactor leaderboard?

The VisFactor leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, Qwen3.7-Plus by Alibaba Cloud / Qwen Team leads with a score of 0.428. The average score across all models is 0.428.

What is the highest VisFactor score?

The highest VisFactor score is 0.428, achieved by Qwen3.7-Plus from Alibaba Cloud / Qwen Team.

How many models are evaluated on VisFactor?

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

What categories does VisFactor cover?

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

How recent are the VisFactor leaderboard results?

The VisFactor leaderboard was last updated in June 2026 and currently includes 1 evaluated 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
226 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
129 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
113 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
101 models
MMLU

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

reasoning
100 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
85 models