README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: question
    dtype: string
  - name: choices
    list: string
  - name: answer
    dtype: int32
  - name: meta_info
    struct:
    - name: title
      dtype: string
    - name: journal
      dtype: string
    - name: doi
      dtype: string
    - name: url
      dtype: string
  - name: question_type
    dtype: string
  splits:
  - name: en
    num_bytes: 546653187.125
    num_examples: 1525
  - name: zh
    num_bytes: 546319847.125
    num_examples: 1525
  download_size: 218606009
  dataset_size: 1092973034.25
configs:
- config_name: RxnBench-VQA
  data_files:
  - split: en
    path: data/en-*
  - split: zh
    path: data/zh-*
license: cc-by-nc-sa-4.0
task_categories:
- visual-question-answering
language:
- en
- zh
tags:
- chemistry
---

# RxnBench: A Benchmark for Chemical Reaction Figure Understanding


**News:** This work has been accepted by [*Journal of Chemical Information and Modeling*](https://pubs.acs.org/doi/10.1021/acs.jcim.6c00286)


## 📘 Benchmark Summary

RxnBench (SF-QA) is a visual question answering (VQA) benchmark comprising 1,525 multiple-choice questions (MCQs) at the PhD-level of organic chemistry reaction understanding. 

The benchmark is built from 305 scientific figures drawn from high-impact OpenAssess journals. 
For each figure, domain experts carefully designed five multiple-choice VQA questions targeting the interpretation of organic reaction diagrams.
These questions were further refined through multiple rounds of rigorous review and revision to ensure both clarity and scientific accuracy. 
The questions cover a variety of types, including the description of chemical reaction images, extraction of reaction content, recognition of molecules or Markush structures, and determination of mechanisms.
This benchmark challenges visual-language models on their foundational knowledge of organic chemistry, multimodal contextual reasoning, and chemical reasoning skills. 


The benchmark is released in both English and Chinese versions. 



## 📑 Task Types

We categorize chemical reaction visual question answering tasks into six types:

- **Type 0 — Fact Extraction**: Direct retrieval of textual or numerical information from reaction schemes.
- **Type 1 — Reagent Roles and Functions Identification**: Identification of reagents and their functional roles, requiring chemical knowledge and reaction-type awareness.
- **Type 2 — Reaction Mechanism and Process Understanding**: Interpretation of reaction progression, including intermediates, catalytic cycles, and mechanistic steps.
- **Type 3 — Comparative Analysis and Reasoning**: Comparative evaluation, causal explanation, or outcome prediction under varying conditions.
- **Type 4 — Multi-step Synthesis and Global Understanding**: Comprehension of multi-step pathways, step-to-step coherence, and overall synthetic design.
- **Type 5 — Chemical Structure Recognition**: Extraction and reasoning-based parsing of chemical structures in SMILES or E-SMILES (as defined in the [MolParser](https://arxiv.org/abs/2411.11098) paper).

![output3](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F65f7f16fb6941db5c2e7c4bf%2FoTOMcZE7oz-Pv4fUUpi0J.png%3C%2Fspan%3E)


## 🎯 Benchmark Evaluation

This benchmark evaluates model performance on multiple-choice question answering (MCQ) tasks.

We provide two versions of the prompt template, depending on the language setting.

**English Prompt**

```
Question: {question}
Choices:
A. {choice_A}
B. {choice_B}
C. {choice_C}
D. {choice_D}
Based on the image and the question, choose the most appropriate answer.
**Only output a single letter (A, B, C, or D)**. Do NOT output any other text or explanation.
```

**Chinese Prompt**

```
问题: {question}
选项:
A. {choice_A}
B. {choice_B}
C. {choice_C}
D. {choice_D}

请根据图像和问题，从以上四个选项中选择最合适的答案。
只输出单个字母 (A, B, C 或 D)，不要输出选项内容，也不要输出任何解释。
```

**Evaluation Protocol**

If the model’s output is not one of A, B, C, or D, we use GPT-4o to map the output to A–D based on the option content. 
The final evaluation reports the absolute accuracy of the benchmark in both English and Chinese versions.

Code Example: https://github.com/uni-parser/RxnBench

## 📊 Benchmark Leaderboard

We evaluated several of the latest popular MLLMs, including both closed-source and open-source models. 

(continuously updated)

|  Moldel   | Think | Weight | API-Version | RxnBench-En  | RxnBench-Zh | Mean Score |
|  ---- |:----:|:----:|:----:|:----:|:----:|:----:|
| Qwen3.5-397B-A17B | √ | Open | - | **0.9613** | 0.9639 | **0.9626** |
| Gemini-3-Flash-preview | √ | Proprietary | 20251217 | 0.9593 | 0.9652 | 0.9623 |
| Gemini-3.1-Pro-preview | √ | Proprietary | 20260219 | 0.9548 | **0.9666** | 0.9607 |
| Qwen3.5-122B-A10B | √ | Open | - | 0.9528 | 0.9580 | 0.9554 |
| Qwen3.5-27B | √ | Open | - | 0.9495 | 0.9593 | 0.9544 | 
| Seed2.0 Pro | √ | Proprietary | 20260215 | 0.9502 | 0.9561 | 0.9531 |
| Gemma-4-31B-it | √ | Open | - | 0.9475 | 0.9508 | 0.9492 |
| Qwen3.5-35B-A3B | √ | Open | - | 0.9456 | 0.9489 | 0.9472 | 
| Kimi K2.5  | √ | Open | - | 0.9464 | 0.9462 | 0.9463 |
| GPT-5.2(high) | √ | Proprietary | 20251211 | 0.9430 | 0.9462 | 0.9446 |
| Seed1.8-Think | √ | Proprietary | 20251218 | 0.9325 | 0.9403 | 0.9364 |
| Gemini-3-Pro-preview | √ | Proprietary | 20251119 | 0.9318 | 0.9403 | 0.9361 |
| Qwen3.5-9B | √ | Open | - | 0.9298 | 0.9246 | 0.9272 | 
| GPT-5(high) | √ | Proprietary | 20250807 | 0.9279 | 0.9246 | 0.9263 |
| Gemini-2.5-Pro | √ | Proprietary | 20250617 | 0.9095 | 0.9423 | 0.9259 |
| GPT-5.1(high) | √ | Proprietary | 20251113 | 0.9213 | 0.9220 | 0.9216 |
| GPT-5(medium) | √ | Proprietary | 20250807 | 0.9207 | 0.9226 | 0.9216 |
| Qwen3-VL-235BA22B-Think | √ | Open | - | 0.9220 | 0.9134 | 0.9177 |
| Qwen3-VL-32B-Think | √ | Open | - | 0.9128 | 0.9161 | 0.9144 |
| GPT-5.1(medium) | √ | Proprietary | 20251113 | 0.9108 | 0.9141 | 0.9125 |
| GPT-5-mini | √ | Proprietary | 20250807 | 0.9108 | 0.9128 | 0.9118 |
| Qwen3.5-4B | √ | Open | - | 0.9056 | 0.9180 | 0.9118 | 
| Seed1.5-VL-Think | √ | Proprietary | 20250428 | 0.9056 | 0.9161 | 0.9109 |
| GPT o3 | √ | Proprietary | 20250416 | 0.9056 | 0.9115 | 0.9086 |
| GPT o4 mini | √ | Proprietary | 20250416  | 0.9069 | 0.9082 | 0.9075 |
| Gemini-3.1-Flash-Lite-preview |  x   | Proprietary |  20260303 | 0.9036 | 0.9062 | 0.9049 |
| InternVL3.5-241B-A28B | √ | Open | - | 0.9003 | 0.9062 | 0.9033 |
| Intern-S1 | √ | Open | - | 0.8938 | 0.8944 | 0.8941 |
| Qwen3-VL-30BA3B-Think | √ | Open | - | 0.8689 | 0.8590 | 0.8689 |
| Step3-VL-10B | √ | Open | - | 0.8630 | 0.8597 | 0.8613 |
| Qwen3-VL-Plus | × | Proprietary | 20250923 | 0.8551 | 0.8656 | 0.8604 |
| Qwen3-VL-8B-Think | √ | Open | - | 0.8636 | 0.8564 | 0.8600 |
| Seed1.5-VL | × | Proprietary | 20250328 | 0.8518 | 0.8669 | 0.8594 |
| Qwen3-VL-235BA22B-Instruct | × | Open | - | 0.8492 | 0.8675 | 0.8584 |
| InternVL3-78b | × | Open | - | 0.8531 | 0.8308 | 0.8420 |
| Qwen3-VL-4B-Think | √ | Open | - | 0.8577 | 0.8256 | 0.8416 |
| Intern-S1-mini | √ | Open | - | 0.8521 | 0.8282 | 0.8402 |
| GLM-4.1V-9B-Thinking | √ | Open | - | 0.8392 | 0.8341 | 0.8367 |
| Qwen3-VL-32B-Instruct | × | Open | - | 0.8315 | 0.8407 | 0.8361 |
| Qwen2.5-VL-72B | × | Open | - | 0.8341 | 0.8308 | 0.8325 |
| Qwen2.5-VL-Max | × | Proprietary | 20250813 | 0.8192 | 0.8262 | 0.8227 |
| GPT-5-nano | √ | Proprietary | 20250807 | 0.7980 | 0.7941 | 0.7961 |
| Qwen2.5-VL-32B | × | Open | - | 0.7980 | 0.7908 | 0.7944 |
| Gemini-2.5-Flash | √ | Proprietary | 20250617 | 0.6925 | 0.8557 | 0.7741 |
| Qwen3-VL-8B-Instruct | × | Open | - | 0.7548 | 0.7495 | 0.7521 |
| Qwen3-VL-30BA3B-Instruct | × | Open | - | 0.7456 | 0.7436 | 0.7456 |
| GPT-4o | × | Proprietary | 20240806  | 0.7462 | 0.7436 | 0.7449 |
| Qwen2.5-VL-7B | × | Open | - | 0.7082 | 0.7233 | 0.7158 |
| Qwen3-VL-4B-Instruct | × | Open | - | 0.7023 | 0.7023 | 0.7023 |
| Qwen3-VL-2B-Think | √ | Open | - | 0.6780 | 0.6708 | 0.6744 |
| Qwen2.5-VL-3B | × | Open | - | 0.6748 | 0.6643 | 0.6696 |
| Qwen3.5-2B | √ | Open | - | 0.6597 | 0.6616 | 0.6607 | 
| GPT-4o mini | × | Proprietary | 20240718  | 0.6636 | 0.6066 | 0.6351 |
| Qwen3.5-0.8B | √ | Open | - | 0.6007 | 0.6066 | 0.6036 | 
| Qwen3-VL-2B-Instruct | × | Open | - | 0.5711 | 0.5928 | 0.5820 |
| *Choice longest answer* | - | - | - | 0.4262 | 0.4525 | 0.4394 |
| Deepseek-VL2 | × | Open | - | 0.4426 | 0.4216 | 0.4321 |
| *Random* | - | - | - | 0.2500 | 0.2500 |  0.2500 |


We also conducted separate evaluations for different task types (in RxnBench-en).

|  Moldel   | Think | Weight | API-Version | Type0  | Type1 | Type2 | Type3 | Type4 | Type5 |
|  ---- |:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
| Qwen3.5-397B-A17B | √ | Open | - | **0.9736** | **0.9722** | 0.9696 | **0.9769** | 0.9492 | 0.8507 |
| Gemini-3-Flash-preview | √ | Proprietary | 20251217 | 0.9613 | 0.9643 | **0.9764** | 0.9630 | 0.9492 | **0.9030** |
| Gemini-3.1-Pro-preview | √ | Proprietary | 20260219 | **0.9736** | 0.9524 | 0.9696 | 0.9583 | 0.9492 | 0.8433 |
| Qwen3.5-122B-A10B | √ | Open | - |  0.9718 | 0.9563 | 0.9595 | 0.9583 | **0.9831** | 0.8284 |
| Qwen3.5-27B | √ | Open | - |  0.9718 | **0.9722** | 0.9561 | 0.9630 | 0.9492 | 0.7761 |
| Seed2.0 Pro | √ | Proprietary | 20260215 | 0.9560 | 0.9524 | 0.9628 | 0.9676 | 0.9322 | 0.8731 |
| Gemma-4-31B-it | √ | Open | - | 0.9577 | **0.9722** | 0.9628 | 0.9630 | 0.9492 | 0.7985 |
| Qwen3.5-35B-A3B | √ | Open | - |  0.9683 | 0.9563 | 0.9595 | 0.9491 | 0.9661 | 0.7836 |
| Kimi K2.5  | √ | Open | - | 0.9542   | 0.9563 | 0.9561 | 0.9630 | 0.9661  | 0.8433 |
| GPT-5.2(high) | √ | Proprietary | 20251211 | 0.9542 | 0.9643 | 0.9662 | 0.9491 | 0.9492 | 0.7910 |
| Seed1.8-Think | √ | Proprietary | 20251218 | 0.9331 | 0.9484 | 0.9527 | 0.9444 | 0.9492 | 0.8284 |
| Gemini-3-Pro-preview | √ | Proprietary | 20251119 | 0.9648 | 0.9246 | 0.9527 | 0.9398 | 0.9322 | 0.7463 |
| Qwen3.5-9B | √ | Open | - | 0.9542 | 0.9444 | 0.9426  | 0.9398  | 0.9661 | 0.7388  |
| GPT-5(high) | √ | Proprietary | 20250807 | 0.9313 | 0.9444 | 0.9527 | 0.9167 | 0.9661 | 0.8358 |
| Gemini-2.5-Pro | √ | Proprietary | 20250617 | 0.9331 | 0.9246 | 0.9459 | 0.9491 | 0.9322 | 0.6343 |
| GPT-5.1(high) | √ | Proprietary | 20251113 | 0.9243 | 0.9524 | 0.9426 | 0.9167 | 0.9661 | 0.7910  |
| GPT-5(medium) | √ | Proprietary | 20250807 | 0.9349 | 0.9325 | 0.9493 | 0.9167 | 0.9492 | 0.7761 |
| Qwen3-VL-235BA22B-Think | √ | Open | - | 0.9190 | 0.9405 | 0.9459 | 0.9213 | 0.9322 | 0.8433 |
| Qwen3-VL-32B-Think | √ | Open | - | 0.9296 | 0.9405 | 0.9426 | 0.9259 | 0.9153 | 0.7015 |
| GPT-5.1(medium) | √ | Proprietary | 20251113 | 0.9243 | 0.9365 | 0.9426 | 0.9167 | 0.9492 | 0.7090 |
| GPT-5-mini | √ | Proprietary | 20250807 | 0.9225 | 0.9325 | 0.9257 | 0.9259 | 0.9831 | 0.7388 |
| Qwen3.5-4B | √ | Open | - | 0.9366 | 0.9206 |  0.9257 | 0.9398 | 0.8983 | 0.6493 |
| Seed1.5-VL-Think | √ | Proprietary | 20250428 | 0.8996 | 0.9365 | 0.9358 | 0.9074 | 0.9153 | 0.8060 |
| GPT o3 | √ | Proprietary | 20250416 | 0.9313 | 0.9325 | 0.9223 | 0.8981 | 0.9492 | 0.7090 |
| GPT o4 mini | √ | Proprietary | 20250416  | 0.9261 | 0.9286  | 0.9358 | 0.9074 | 0.9492 | 0.7015 |
| Gemini-3.1-Flash-Lite-preview |  x   | Proprietary |  20260303 | 0.8785 | 0.9365 | 0.9426 | 0.9167 | 0.8814 | 0.8507 | 
| InternVL3.5-241B-A28B | √ | Open | - | 0.8944 | 0.9127 | 0.9291 | 0.9167 | 0.9153 | 0.8134 |
| Intern-S1 | √ | Open | - | 0.9014 | 0.9127 | 0.9223 | 0.9028 | 0.8814 | 0.7463 | 
| Qwen3-VL-30BA3B-Think | √ | Open | - | 0.8732 | 0.8810 | 0.9054 | 0.8843 | 0.9322 | 0.6940 |
| Step3-VL-10B | √ | Open | - | 0.8926 | 0.8492 | 0.9054 | 0.8935 | 0.8983 | 0.6119 |
| Qwen3-VL-Plus | × | Proprietary | 20250923 | 0.8275 | 0.8968 | 0.8986 | 0.8565 | 0.9153 | 0.7687 |
| Qwen3-VL-8B-Think | √ | Open | - | 0.8768 | 0.8730 | 0.8885 | 0.9028 | 0.8983 | 0.6567 |
| Seed1.5-VL | × | Proprietary | 20250328 | 0.9327 | 0.9127 | 0.9122 | 0.8472 | 0.8305 | 0.7015 |
| Qwen3-VL-235BA22B-Instruct | × | Open | - | 0.8204 | 0.8929 | 0.8986 | 0.8426 | 0.8814 | 0.7761 |
| InternVL3-78b | × | Open | - | 0.8556 | 0.8730 | 0.8885 | 0.8981 | 0.9153 | 0.6194 |
| Qwen3-VL-4B-Think | √ | Open | - | 0.8838 | 0.8770 | 0.8615 | 0.9074 | 0.8983 | 0.6045 |
| Intern-S1-mini | √ | Open | - | 0.8239 | 0.8690 | 0.8547 | 0.8611 | 0.8475 | 0.6791 |
| GLM-4.1V-9B-Thinking | √ | Open | - | 0.8433 | 0.8690 | 0.8649 | 0.8657 | 0.8814 | 0.6493 |
| Qwen3-VL-32B-Instruct | × | Open | - | 0.8169 | 0.8571 | 0.8885 | 0.8519 | 0.8305 | 0.6866 |
| Qwen2.5-VL-72B | × | Open | - | 0.8063 | 0.8063 | 0.8770 | 0.9088 | 0.8102 | 0.9322 | 0.7090 |
| Qwen2.5-VL-Max | × | Proprietary | 20250813 | 0.7958 | 0.8571 | 0.8885 | 0.8194 | 0.8983 | 0.6642 |
| GPT-5-nano | √ | Proprietary | 20250807 | 0.8063 | 0.8452 | 0.8311 | 0.8241 | 0.7797 | 0.5672 | 
| Qwen2.5-VL-32B | × | Open | - | 0.7729 | 0.8413 | 0.8750 | 0.8009 | 0.8305 | 0.6418 |
| Gemini-2.5-Flash | √ | Proprietary | 20250617 | 0.7799 | 0.6111 | 0.6757 | 0.6620 | 0.7627 | 0.5373 |
| Qwen3-VL-8B-Instruct | × | Open | - | 0.7113 | 0.8175 | 0.8446 | 0.8241 | 0.7627 | 0.5075 |
| Qwen3-VL-30BA3B-Instruct | × | Open | - | 0.7042 | 0.7937 | 0.8311 | 0.7824 | 0.7119 | 0.5970 | 
| GPT-4o | × | Proprietary | 20240806  | 0.7359 | 0.8175 | 0.7973 | 0.7500 | 0.7627 | 0.5224 |
| Qwen2.5-VL-7B | × | Open | - | 0.6678 | 0.7659 | 0.8041 | 0.7130 | 0.6441 | 0.5373 |
| Qwen3-VL-4B-Instruct | × | Open | - | 0.6708 | 0.7302 | 0.7804 | 0.7222 | 0.6610 | 0.5970 |
| Qwen3-VL-2B-Think | √ | Open | - | 0.7342 | 0.6706 | 0.7128 | 0.7083 | 0.6102 | 0.3657 |
| Qwen2.5-VL-3B | × | Open | - | 0.6426 | 0.7381 | 0.7635 | 0.6898 | 0.6610 | 0.4776 |
| Qwen3.5-2B | √ | Open | - | 0.6620 | 0.7103 |  0.7027  | 0.7083 | 0.6271 | 0.3955 |
| GPT-4o mini | × | Proprietary | 20240718  | 0.6391 | 0.7302 | 0.7500 | 0.6667 | 0.6271 | 0.4627 |
| Qwen3.5-0.8B | √ | Open | - | 0.6215 | 0.6310 | 0.6486 | 0.6620 | 0.5254 | 0.2836 |
| Qwen3-VL-2B-Instruct | × | Open | - | 0.5405 | 0.6190 | 0.6318 | 0.6250 | 0.6102 | 0.3731 |
| Deepseek-VL2 | × | Open | - | 0.4120 | 0.5040 | 0.4899 | 0.4907 | 0.3729 | 0.3060 |

*We welcome pull requests with your model results.*  
*Alternatively, if you would like to provide an API for evaluation, please contact fangxi@dp.tech*


> ⚠️ Attention
>
> We observe that the **Qwen3.5** series models exhibit unusually strong performance on **RxnBench**, with metrics that are significantly higher than expected. Notably, the release date of Qwen3.5 is substantially later than the publication date of RxnBench. Therefore, we cannot rule out the possibility of potential data leakage.
>
> We recommend interpreting the corresponding results with caution.


## 🆕 RxnBench-Doc

A single reaction image often lacks the information needed for full interpretation, requiring contextual text from the literature. Therefore, we also provide a benchmark for chemical reaction literature understanding.

https://huggingface.co/datasets/UniParser/RxnBench-Doc

## 📖 Citation

```bibtex
@article{li2025rxnbench,
  title={RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature},
  author={Li, Hanzheng and Fang, Xi and Li, Yixuan and Huang, Chaozheng and Wang, Junjie and Wang, Xi and Bai, Hongzhe and Hao, Bojun and Lin, Shenyu and Liang, Huiqi and Zhang, Linfeng and Ke, Guolin},
  journal={arXiv preprint arXiv:2512.23565},
  year={2025}
}
```