CharXiv (RQ)
CharXiv (RQ) - reasoning questions over arXiv figures and tables.
Category
Document Understanding
Score type
percent
Scale
100%
higher is better
Models tested
20
What is CharXiv (RQ)?
CharXiv (RQ) is a benchmark for reading and parsing real documents. The official description says: "CharXiv (RQ) - reasoning questions over arXiv figures and tables". Below we describe in simple words what benchmarks of this type generally examine.
What does it test?
CharXiv (RQ) measures reading and parsing real documents. In detail, it checks:
- extracting structured text from scans, PDFs and photos
- keeping tables, headings and reading order intact
- answering comprehension questions about the document content
- handling noisy layouts, handwriting and multi-page documents
How to read the score
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Official methodology note: CharXiv (RQ) - reasoning questions over arXiv figures and tables.
Frequently asked questions
What is the CharXiv (RQ) benchmark?
CharXiv (RQ) is a benchmark for reading and parsing real documents. The official description says: 'CharXiv (RQ) - reasoning questions over arXiv figures and tables'. Below we describe in simple words what benchmarks of this type generally examine.
What does CharXiv (RQ) test?
CharXiv (RQ) mainly measures reading and parsing real documents. Concretely, it checks: extracting structured text from scans, PDFs and photos; keeping tables, headings and reading order intact; answering comprehension questions about the document content.
How is the CharXiv (RQ) score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.
Where do I get CharXiv (RQ) data and scores?
The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/charxiv-rq/scores) or pull it directly from the Open LLM Radar API with any HTTP client.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 20 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Kimi K3
Moonshot AI
|
91.3%
|
18.08.2026 | Report | Verified |
| 2 |
Kimi K3.1 Pro
Moonshot AI
|
89%
|
07.11.2026 | Report | Verified |
| 3 |
Qwen3.8-Flash-Next
Qwen Team (Alibaba)
|
84.6%
|
26.08.2026 | Report | Verified |
| 4 |
Qwen3.8-27B
Qwen Team (Alibaba Cloud)
|
83.7%
|
18.08.2026 | Report | Verified |
| 5 |
MiniMax-M3.1-Flash
MiniMaxAI
|
82.5%
|
19.10.2026 | Report | Verified |
| 6 |
Kimi K2.6
Moonshot AI
|
80.4%
|
23.08.2026 | Report | Verified |
| 7 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
79.5%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 8 |
Muse-Glimmer-30B
Meta Superintelligence Lab
|
78.8%
|
01.08.2026 | Meta Superintelligence Lab (official model card) | Verified |
| 9 |
Qwen3.6-27B
Qwen Team (Alibaba Cloud)
|
78.4%
|
22.04.2026 | Qwen Team (official model card) | Verified |
| 10 |
Qwen3.6-35B-A3B
Qwen Team (Alibaba Cloud)
|
78%
|
16.04.2026 | Qwen Team (official model card) | Verified |
| 11 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
77.5%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 12 |
Kimi K2.5
Moonshot AI
|
77.5%
|
23.08.2026 | Report | Verified |
| 13 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
77.2%
|
01.02.2026 | Report | Verified |
| 14 |
DeepSeek-V4.1-Flash-1017
DeepSeek
|
77%
|
07.11.2026 | Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro | Unverified |
| 15 |
Qwen3.5-4B
Qwen
|
70.8%
|
01.02.2026 | Report | Verified |
| 16 |
LFM3-VL-2B
LiquidAI
|
70.2%
|
— | Report | Verified |
| 17 |
Gemma4-26B-A4B
Google
|
69%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 18 |
DeepSeek-V4.1-Flash
DeepSeek
|
69%
|
19.10.2026 | Report | Unverified |
| 19 |
Gemma4-31B-it
Google
|
67.9%
|
16.04.2026 | Qwen Team (official model card - comparison) | Unverified |
| 20 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
66.1%
|
01.02.2026 | Report | Unverified |