Question 1

What are the most common uses for open prompt?

Accepted Answer

Extracting structured data from photos, counting specific objects in a scene, reading and interpreting charts or graphs, identifying anomalies in images, describing spatial relationships, and answering questions about image content in natural language.

Question 2

Which industries use open prompt most?

Accepted Answer

Insurance for claims assessment from photos, retail for shelf and planogram analysis, construction and real estate for site documentation, research for data extraction from visual materials, and customer support for image-based troubleshooting.

Question 3

How is open prompt different from OCR?

Accepted Answer

OCR is purpose-built for extracting text from images and is generally more accurate on that specific task. Open prompt is more flexible and can handle a wider range of questions, but for pure text extraction OCR models will perform better.

Question 4

How are models ranked here?

Accepted Answer

Rankings are based on ELO scores from head-to-head battles in the Open Prompt Arena. Users submit their own prompts and images, then vote on which model's response is more accurate or useful.

Question 5

Can I test these models on my own images?

Accepted Answer

Yes. Open the Open Prompt Playground, select the models you want to compare, upload an image, and type your question or instruction.


1	Gemini 2.5 Pro	multimodal	1311	5	17.39 s	Google
2	GPT-5 Mini	multimodal	1287	5	31.91 s	OpenAI
3	Gemini 2.5 Flash	multimodal	1262	5	9.99 s	Google
4	GPT-5.4	multimodal	1244	4	5.83 s	OpenAI
5	Qwen3.5 122B A10B	multimodal	1235	3	11.91 s	Qwen
6	Grok 4	multimodal	1225	3	34.69 s	xAI
7	Gemini 3.1 Flash-Lite	multimodal	1224	2	4.28 s	Google
8	Gemini 3.5 Flash	multimodal	1223	2	40.38 s	Google
9	Gemma 3 27B	multimodal	1222	3	14.38 s	Google
10	Qwen3.6 35B A3B	multimodal	1222	4	11.56 s	Qwen
11	Claude Opus 4.6	multimodal	1215	5	5.56 s	Anthropic
12	Claude Opus 4.5	multimodal	1211	5	7.38 s	Anthropic
13	Qwen3 VL 30B A3B Instruct	multimodal	1210	3	7.10 s	Qwen
14	Qwen3.6 27B	multimodal	1203	2	23.98 s	Qwen
15	Qwen3.5 27B	multimodal	1201	3	65.52 s	Qwen
16	GPT-5	multimodal	1200	5	15.36 s	OpenAI
17	Claude Opus 4.7	multimodal	1200	5	17.02 s	Anthropic
18	Qwen3.5 397B A17B	multimodal	1200	3	9.25 s	Qwen
19	Qwen3.6 Plus	multimodal	1200	3	15.41 s	Qwen
20	Qwen3 VL 235B A22B Instruct	multimodal	1200	3	7.39 s	Qwen
21	Gemma 4 26B A4B	multimodal	1200	4	3.42 s	Google
22	Claude Opus 4.8	multimodal	1200	2	11.52 s	Anthropic
23	Kimi K2.5	multimodal	1200	2	23.02 s	Moonshot AI
24	Claude Sonnet 4.5	multimodal	1200	5	12.55 s	Anthropic
25	Gemini 2.5 Flash-Lite	multimodal	1199	5	1.94 s	Google
26	GPT-5.2	multimodal	1197	5	4.66 s	OpenAI
27	Qwen VL Max	multimodal	1197	3	12.06 s	Qwen
28	GPT-5 Nano	multimodal	1193	5	20.49 s	OpenAI
29	Qwen3.5 35B A3B	multimodal	1192	2	11.93 s	Qwen
30	Llama 4 Maverick	multimodal	1190	3	3.56 s	Meta
31	Gemini 3 Flash	multimodal	1188	5	10.08 s	Google
32	Claude Opus 4.1	multimodal	1188	5	12.68 s	Anthropic
33	GPT-5.4 Nano	multimodal	1188	3	3.36 s	OpenAI
34	Qwen3.6 Flash	multimodal	1188	1	32.69 s	Qwen
35	GPT-5.4 Mini	multimodal	1188	4	1.92 s	OpenAI
36	Qwen3.5 9b	multimodal	1188	2	29.98 s	Qwen
37	GPT-5.1	multimodal	1187	5	8.23 s	OpenAI
38	Llama 4 Scout	multimodal	1186	3	3.41 s	Meta
39	Gemma 4 31B	multimodal	1181	4	5.43 s	Google
40	Pixtral 12B	multimodal	1180	3	6.40 s	Mistral
41	Qwen3 VL 8B Instruct	multimodal	1178	3	13.02 s	Qwen
42	Claude Sonnet 4.6	multimodal	1177	5	7.80 s	Anthropic
43	Gemma 3 12B	multimodal	1170	3	27.52 s	Google
44	Gemma 3 4B	multimodal	1168	3	8.78 s	Google
45	Qwen2.5 VL 7B Instruct	multimodal	1167	3	5.72 s	Qwen
46	Llama 3.2 Vision 11b	multimodal	1166	4	5.09 s	Meta
47	Mistral Small 3.1 24B	multimodal	1154	3	5.12 s	Mistral
48	Mistral Medium 3.1	multimodal	1148	3	7.41 s	Mistral
49	Claude Haiku 4.5	multimodal	1144	5	4.71 s	Anthropic

Open Prompt Model Rankings