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	multimodal	1227	5	7.81 s	Anthropic
6	Claude Sonnet 4.5	multimodal	1226	5	13.57 s	Anthropic
7	Mistral Medium 3.1	multimodal	1225	3	6.52 s	Mistral
8	GPT-5.4	multimodal	1223	4	7.48 s	OpenAI
9	Gemini 3.1 Pro	multimodal	1222	4	28.71 s	Google
10	GPT-5	multimodal	1222	5	34.40 s	OpenAI
11	Gemma 4 26B A4B	multimodal	1222	4	2.54 s	Google
12	Qwen3 VL 30B A3B Instruct	multimodal	1221	3	15.94 s	Qwen
13	Qwen3.5 27B	multimodal	1220	3	13.55 s	Qwen
14	Gemini 2.5 Flash-Lite	multimodal	1219	5	2.74 s	Google
15	Claude Opus 4.6	multimodal	1218	5	15.94 s	Anthropic
16	Llama 4 Scout	multimodal	1216	3	3.48 s	Meta
17	Claude Opus 4.1	multimodal	1214	5	19.19 s	Anthropic
18	Claude Opus 4.7	multimodal	1214	5	8.71 s	Anthropic
19	Qwen3.5 397B A17B	multimodal	1213	3	37.79 s	Qwen
20	Mistral Small 3.1 24B	multimodal	1212	3	8.50 s	Mistral
21	Qwen3.6 Plus	multimodal	1212	2	12.24 s	Qwen
22	Qwen VL Max	multimodal	1212	3	11.31 s	Qwen
23	Qwen3.5 122B A10B	multimodal	1212	3	17.55 s	Qwen
24	Qwen3 VL 8B Instruct	multimodal	1211	3	8.28 s	Qwen
25	Grok 4	multimodal	1210	3	26.73 s	xAI
26	Gemini 2.5 Pro	multimodal	1209	5	15.64 s	Google
27	Claude Opus 4.5	multimodal	1201	5	15.49 s	Anthropic
28	Claude Sonnet 4.6	multimodal	1201	5	11.46 s	Anthropic
29	Qwen3 VL 235B A22B Instruct	multimodal	1200	3	2.79 s	Qwen
30	GPT-5.4 Mini	multimodal	1200	4	2.68 s	OpenAI
31	Claude Haiku 4.5	multimodal	1197	5	4.12 s	Anthropic
32	Gemma 4 31B	multimodal	1197	4	7.39 s	Google
33	GPT-5.1	multimodal	1191	5	6.31 s	OpenAI
34	Qwen3.5 35B A3B	multimodal	1189	2	40.53 s	Qwen
35	Qwen3.6 35B A3B	multimodal	1188	4	32.64 s	Qwen
36	Gemma 3 12B	multimodal	1166	3	5.69 s	Google
37	Google Vision OCR	vision	1151	1	869 ms	Google
38	GPT-5 Nano	multimodal	1147	5	22.24 s	OpenAI
39	GPT-5.4 Nano	multimodal	1144	3	3.86 s	OpenAI
40	Llama 4 Maverick	multimodal	1138	3	4.29 s	Meta
41	Pixtral 12B	multimodal	1137	3	4.23 s	Mistral
42	Gemma 3 27B	multimodal	1130	3	6.12 s	Google
43	Gemma 3 4B	multimodal	1129	3	11.84 s	Google
44	Qwen2.5 VL 7B Instruct	multimodal	1127	3	5.88 s	Qwen
45	Llama 3.2 Vision 11b	multimodal	1119	4	14.41 s	Meta
46	Florence-2	multimodal	1076	3	9.93 s	Microsoft

OCR Model Rankings