Question 1

What are the most common uses for OCR?

Accepted Answer

Document digitization, invoice and receipt processing, form data extraction, ID verification, accessibility tooling like auto-generated alt text, and extracting text from product labels or packaging.

Question 2

Which industries use OCR most?

Accepted Answer

Finance and insurance for document processing, logistics for label and waybill scanning, healthcare for digitizing patient records, retail for receipt and inventory management, and legal for contract review workflows.

Question 3

Should I use a VLM or a traditional OCR engine?

Accepted Answer

VLMs handle complex cases better: handwriting, varied layouts, and mixed scripts. Traditional OCR engines are faster and cheaper for clean, well-structured documents. Google Vision OCR is included in this ranking alongside VLMs so you can compare both approaches directly.

Question 4

How are OCR models ranked here?

Accepted Answer

Rankings are based on ELO scores from head-to-head battles in the OCR Arena. Users upload an image and vote on which model produces the better output.

Question 5

Can I test these models on my own images?

Accepted Answer

Yes. Open the OCR Playground, select the models you want to compare, upload your image, and results generate automatically.


1	Gemini 2.5 Flash	multimodal	1269	5	6.95s	Google
2	GPT-5.2	multimodal	1255	5	7.67s	OpenAI
3	GPT-5 Mini	multimodal	1249	5	19.71s	OpenAI
4	Gemini 3 Flash	multimodal	1247	5	9.39s	Google
5	Qwen3.5 27B	multimodal	1243	4	11.53s	Qwen
6	GPT-5.4	multimodal	1242	5	6.51s	OpenAI
7	Claude Sonnet 4.5	multimodal	1238	5	12.54s	Anthropic
8	Muse Spark 1.1	multimodal	1224	3	9.33s	Meta
9	Gemma 4 26B A4B	multimodal	1222	4	2.54s	Google
10	GPT-5.5	multimodal	1222	4	7.47s	OpenAI
11	Qwen3 VL 30B A3B Instruct	multimodal	1221	3	15.94s	Qwen
12	Llama 4 Scout	multimodal	1216	3	3.48s	Meta
13	Gemini 2.5 Flash-Lite	multimodal	1216	5	2.65s	Google
14	Claude Opus 4.5	multimodal	1214	5	13.24s	Anthropic
15	GPT-5.6 Luna	multimodal	1213	4	19.98s	OpenAI
16	Kimi K2.5	multimodal	1213	2	54.56s	Moonshot AI
17	Claude Opus 5	multimodal	1212	2	8.70s	Anthropic
18	Qwen3.6 Plus	multimodal	1212	3	12.24s	Qwen
19	Claude Sonnet 5	multimodal	1212	3	5.76s	Anthropic
20	Qwen VL Max	multimodal	1212	3	11.31s	Qwen
21	Qwen3.5 122B A10B	multimodal	1212	3	17.55s	Qwen
22	Mistral Medium 3.1	multimodal	1211	3	6.81s	Mistral
23	Gemini 3.5 Flash	multimodal	1211	3	3.27s	Google
24	Grok 4	multimodal	1210	3	26.73s	xAI
25	Gemma 4 31B	multimodal	1210	5	4.29s	Google
26	Qwen3 VL 8B Instruct	multimodal	1210	3	6.96s	Qwen
27	Gemini 2.5 Pro	multimodal	1209	5	15.64s	Google
28	Claude Haiku 4.5	multimodal	1207	5	4.46s	Anthropic
29	Gemini 3.1 Pro	multimodal	1207	5	33.59s	Google
30	Claude Opus 4.6	multimodal	1206	5	17.58s	Anthropic
31	Qwen3.5 397B A17B	multimodal	1202	3	42.99s	Qwen
32	Claude Fable 5	multimodal	1201	2	14.27s	Anthropic
33	Qwen3 VL 235B A22B Instruct	multimodal	1201	3	2.46s	Qwen
34	Claude Sonnet 4.6	multimodal	1201	5	11.46s	Anthropic
35	Mistral Small 3.1 24B	multimodal	1200	3	7.85s	Mistral
36	GPT-5	multimodal	1197	5	32.11s	OpenAI
37	GPT-5.6 Sol	multimodal	1195	3	80.27s	OpenAI
38	Qwen3.6 27B	multimodal	1195	3	52.24s	Qwen
39	GPT-5.1	multimodal	1191	5	6.31s	OpenAI
40	Claude Opus 4.7	multimodal	1191	5	9.06s	Anthropic
41	Qwen3.5 35B A3B	multimodal	1189	3	34.22s	Qwen
42	Qwen3.6 35B A3B	multimodal	1188	4	32.64s	Qwen
43	GLM-OCR	multimodal	1188	1	1.81s	Z.ai
44	GPT-5.4 Mini	multimodal	1188	5	2.47s	OpenAI
45	Llama 4 Maverick	multimodal	1168	3	5.03s	Meta
46	Gemma 3 12B	multimodal	1147	3	5.30s	Google
47	GPT-5 Nano	multimodal	1147	5	22.24s	OpenAI
48	Pixtral 12B	multimodal	1137	3	4.23s	Mistral
49	GPT-5.4 Nano	multimodal	1135	4	3.61s	OpenAI
50	Gemma 3 27B	multimodal	1133	3	6.20s	Google
51	Gemma 3 4B	multimodal	1129	3	11.84s	Google
52	Google Vision OCR	vision	1129	1	1.77s	Google
53	Qwen2.5 VL 7B Instruct	multimodal	1127	3	5.88s	Qwen
54	Florence-2	multimodal	1054	3	8.70s	Microsoft

OCR Model Rankings