Multimodal capability benchmark for vision-language models, covering perception and reasoning abilities across multiple dimensions.
8 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.
| # | Model | Org | Submitted | Paper / code | accuracy |
|---|---|---|---|---|---|
| 01 | Qwen2.5-VL 72BOSS | Alibaba | Feb 2025 | Qwen2.5-VL Technical Report | 90.50 |
| 02 | InternVL3-78BOSS | Shanghai AI Lab | Jan 2025 | InternVL3: Exploring Advanced Training and Test-Time Rec… | 90.10 |
| 03 | Qwen2-VL 72BOSS | Alibaba | Sep 2024 | Qwen2-VL: Enhancing Vision-Language Model's Perception o… | 88 |
| 04 | InternVL2-76BOSS | Shanghai AI Lab | Apr 2024 | InternVL: Scaling up Vision Foundation Models and Aligni… | 86.50 |
| 05 | GPT-4oAPI | OpenAI | Oct 2024 | SWE-bench Verified | 83.40 |
| 06 | GPT-4V | — | Mar 2023 | GPT-4 Technical Report | 75.80 |
| 07 | Gemini 1.5 ProAPI | Feb 2024 | Gemini 1.5: Unlocking multimodal understanding across mi… | 73.90 | |
| 08 | LLaVA-1.5OSS | UW-Madison / Microsoft | Oct 2023 | Improved Baselines with Visual Instruction Tuning (LLaVA… | 67.70 |
Each row below marks a model that broke the previous record on accuracy. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.
Higher scores win. Each subsequent entry improved upon the previous best.
Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.
Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.