Alternatives
Products that do what We benchmarked 18 LLMs on OCR (7K+ calls) – cheaper models win does
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- 2BV
Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
2025 · getomni.ai
- 3LA
I recently submitted another project for using LLMs to correct errors and improve formatting of OCRed documents which was well received. The low cost and high quality/speed of the latest "value tier" models from OpenAI and Anthropic have made it possible to get compelling results at a very reasonable price in that application. It occured to me that the same approach taken there (namely, splitting documents into chunks and sending each chunk through a chain of LLM prompts that each take the output of the previous prompt and apply an additional layer of processing) could be easily applied…
2024 · github.com
- 4OB
OCR/Document extraction field has seen lot of action recently with releases like Mixtral OCR, Andrew Ng's agentic document processing etc. Also there are several benchmarks for OCR, however all testing for something slightly different which make good comparison of models very hard. To give an example, some models like mixtral-ocr only try to convert a document to markdown format. You have to use another LLM on top of it to get the final result. Some VLM’s directly give structured information like key fields from documents like invoices, but you have to either add business rules on top…
2025 · nanonets.com
- 5WB
Apr 2026 · github.com
- 6LD
Mar 2026 · github.com
- 7LB
2025 · github.com
- 8OW
This was not supposed to become a product. When PaddleOCR-VL-1.6 dropped, independent benchmarks put it at the top of document parsing models. I had to try it. I needed a provider, but there simply isn't one ready for production that I would trust. So i set one up myself. I assumed that even after getting it running, serving a vision-language model would be expensive. It turns out the opposite is true. Once I had it running properly, the cost was absurdly low. At proper GPU utilization, the cost is only around $1 per 1,000 pages. The nearest competitors are either much lower quality (Azure…
Jul 2026 · openparser.dev
- 9AN
2023 · github.com
- 10RM
Apr 2026 · github.com
- 11L1
PoC for something some the potential to yield some interesting results eventually.
2025 · github.com
- 12CU
Feb 2026 · github.com
- 13M1
2024 · ttsdemo.themetavoice.xyz
- 14LR
Sep 2025 · github.com
- 15IB
I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
2025 · llm-stats.com
- 16KO
Sep 2025 · huggingface.co
- 17
- 18LD
2024 · github.com
- 19KO
2019 · github.com
- 20
- 21CL
2023 · convoclash.net
- 22CO
May 2026 · github.com
- 23LC
Sep 2025 · github.com
- 24LA
Feb 2026 · github.com
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