FCMBench V1.1 - Key Information Extraction: leaderboard

Evaluates visual credit-document understanding on FCMBench V1.1: 26 document types, 5,198 images and 13,806 VQA pairs. Exact-match key/value sets are scored with F1; task scores macro-average fine-grained subtasks. Numeric calculation permits differences below 2. The paper prefers instruct or lowest available reasoning effort. The two-stage DeepSeek-OCR plus V3.2 system stays distinct from either model alone. These are paper V1.1 scores, not the differing repository leaderboard. This series reports FCMBench V1.1 - Key Information Extraction using F1 (%).

Metric: F1 (%). Source: arxiv.org. Saturation forecast: Around 2029. 28 models tracked.

Top models

#ModelScoreOverall rank
1Kimi K2.545.28#152
2Qwen 3.5 122B A10B45.24#184
3Qwen 3.5 35B A3B44.3#268
4Gemini 3 Flash43.99#103
5Gemini 3 Pro43.36#82
6Qwen 3 VL 32B Instruct42.66#290
7Qwen 3 VL 235B A22B Instruct42.07#280
8Claude Opus 4.540.42#87
9GLM-4.6V39.63#330
10Qwen 3.5 397B A17B38.79#154
11GLM-4.5V38#358
12GPT-5.235.47#115
13Qwen 3 VL 8B Instruct33.74#419
14GPT-5.131.75#145
15Qwen 3 VL 30B A3B Instruct31.48#390

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=fcmbench-v1-1-key-information-extraction · How It Works · Data refreshed daily, snapshot 2026-11-05.