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	GPT-5 Mini	multimodal	1263	5	20.06 s	OpenAI
2	Gemini 2.5 Flash	multimodal	1260	5	7.44 s	Google
3	Gemini 3 Flash	multimodal	1247	5	9.39 s	Google
4	GPT-5.2	multimodal	1244	5	7.13 s	OpenAI
5	Claude Sonnet 4.5	multimodal	1238	5	12.54 s	Anthropic
6	GPT-5.4	multimodal	1230	5	6.91 s	OpenAI
7	Mistral Medium 3.1	multimodal	1224	3	6.31 s	Mistral
8	Gemini 3.1 Pro	multimodal	1222	4	28.71 s	Google
9	Gemma 4 26B A4B	multimodal	1222	4	2.54 s	Google
10	Qwen3 VL 30B A3B Instruct	multimodal	1221	3	15.94 s	Qwen
11	Qwen3.5 27B	multimodal	1220	3	13.55 s	Qwen
12	Claude Opus 4.6	multimodal	1218	5	15.94 s	Anthropic
13	Gemini 2.5 Flash-Lite	multimodal	1216	5	2.69 s	Google
14	Llama 4 Scout	multimodal	1216	3	3.48 s	Meta
15	Claude Opus 4.1	multimodal	1214	5	19.19 s	Anthropic
16	Claude Opus 4.5	multimodal	1214	5	15.00 s	Anthropic
17	Claude Opus 4.7	multimodal	1214	5	8.71 s	Anthropic
18	Kimi K2.5	multimodal	1213	2	54.56 s	Moonshot AI
19	Mistral Small 3.1 24B	multimodal	1212	3	8.50 s	Mistral
20	Qwen3.6 Plus	multimodal	1212	3	12.24 s	Qwen
21	Claude Sonnet 5	multimodal	1212	3	4.51 s	Anthropic
22	Qwen VL Max	multimodal	1212	3	11.31 s	Qwen
23	Qwen3.5 122B A10B	multimodal	1212	3	17.55 s	Qwen
24	Grok 4	multimodal	1210	3	26.73 s	xAI
25	Qwen3 VL 8B Instruct	multimodal	1210	3	6.96 s	Qwen
26	GPT-5	multimodal	1209	5	34.13 s	OpenAI
27	Gemini 2.5 Pro	multimodal	1209	5	15.64 s	Google
28	Qwen3.5 397B A17B	multimodal	1202	3	29.13 s	Qwen
29	Qwen3 VL 235B A22B Instruct	multimodal	1201	3	2.46 s	Qwen
30	Claude Sonnet 4.6	multimodal	1201	5	11.46 s	Anthropic
31	Claude Haiku 4.5	multimodal	1197	5	4.12 s	Anthropic
32	GPT-5.1	multimodal	1191	5	6.31 s	OpenAI
33	Qwen3.5 35B A3B	multimodal	1189	3	40.53 s	Qwen
34	Qwen3.6 35B A3B	multimodal	1188	4	32.64 s	Qwen
35	GPT-5.4 Mini	multimodal	1188	4	2.47 s	OpenAI
36	Gemma 4 31B	multimodal	1185	5	5.31 s	Google
37	Gemma 3 12B	multimodal	1166	3	5.69 s	Google
38	Google Vision OCR	vision	1151	1	869 ms	Google
39	GPT-5 Nano	multimodal	1147	5	22.24 s	OpenAI
40	GPT-5.4 Nano	multimodal	1144	3	3.86 s	OpenAI
41	Llama 4 Maverick	multimodal	1141	3	4.18 s	Meta
42	Pixtral 12B	multimodal	1137	3	4.23 s	Mistral
43	Gemma 3 27B	multimodal	1130	3	6.12 s	Google
44	Gemma 3 4B	multimodal	1129	3	11.84 s	Google
45	Qwen2.5 VL 7B Instruct	multimodal	1127	3	5.88 s	Qwen
46	Llama 3.2 Vision 11b	multimodal	1119	4	14.41 s	Meta
47	Florence-2	multimodal	1069	3	9.42 s	Microsoft

OCR Model Rankings