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by Novita Ai
RANKED #309 OF 317 ASSISTANTS · OVERALL #480 OF 493 RANKED · VIBE SCORE 4.7 · 71 VOICES ·
COPY BADGE pastes a README snippet with the rank SVG.
Multimodal model for analyzing text, images, documents, and rich media
6 mentions
6 mentions
26 weeks · 73 voices in this window
HOLDING UP
Recent vibe is steady. No clear fade in the crowd.
Open weights: download PaddleOCR VL once and it's yours. No subscription, no rate limits, works offline.
0.9B parameters, full FP16 model is about 1.8 GB and a Q4 quant is under 600 MB
Nvidia GeForce GTX 1650 (or any 4-core modern CPU laptop)
4 GB VRAM OR 8 GB RAM
~$75 USEDFP16 or Q4 quant, ~25 tok/s, standard single-page document parsing
Nvidia GeForce RTX 3060
12 GB VRAM
~$260 USEDFull FP16 with long multi-page context, batching, and vLLM acceleration, ~85 tok/s
Nvidia GeForce RTX 4070 Ti Super
16 GB VRAM
~$750 USEDFull FP16 high-concurrency document processing pipeline, ~140 tok/s
No live install guides found yet. The hardware picks above still stand.
The three cards above are researched picks. Search the machine you actually have — we only say yes if it should reply at a usable speed, not just load the weights and crawl.
HARDWARE PICKS & PRICES ARE RESEARCHED FROM THE LIVE WEB AND REFRESHED AUTOMATICALLY. TREAT THEM AS BALLPARK, NOT GOSPEL.
Everyday jobs on each of those rigs: how long PaddleOCR VL takes, and how good the crowd says it is at that kind of work.
| THE JOB | BARE MINIMUMNvidia GeForce GTX 1650 (or any 4-core modern CPU laptop) | SWEET SPOTNvidia GeForce RTX 3060 | FULL POWERNvidia GeForce RTX 4070 Ti Super |
|---|---|---|---|
| Write an emaila paragraph or two, drafted from a one-line brief | ~20 S | ~6 S | ~4 S |
| Summarize a documenta long report boiled down to the points that matter | ~60 S★★★★★ | ~18 S★★★★★ | ~11 S★★★★★ |
| Build a websitea small landing page, markup and styles together | ~5.3 MIN★★★★★ | ~1.6 MIN★★★★★ | ~57 S★★★★★ |
| Build a backendan API with routes, storage and tests | ~17 MIN★★★★★ | ~4.9 MIN★★★★★ | ~3 MIN★★★★★ |
| Build a gamea playable browser game in one file | ~10 MIN★★★★★ | ~2.9 MIN★★★★★ | ~1.8 MIN★★★★★ |
STARS ARE THE COMMUNITY SCORE FOR THAT KIND OF WORK, DOCKED FOR HOW SQUASHED THE WEIGHTS GET AT EACH TIER. TIMES ARE COMPUTED FROM RESEARCHED SPEEDS. BALLPARK, NOT GOSPEL.
People are mostly frustrated with PaddleOCR VL right now.
65 POSTS · 6 COMMENTS · REDDIT · DEV.TO · GITHUB · HACKER NEWS
1 thumbs up · 4 thumbs down
Other models the crowd has fully reviewed, starting with multimodal models like this one.
I compared even more parsers on 14 PDF-parsing capabilities using different types
Turbo-OCR for high-volume image and PDF processing
Xberg v1 is out
OvisOCR2: a promising 0.8B local document parser
Xberg v1 is out
CJK Manga/Manhwa/Manhua 150M OCR model (hayai-ocr-v2) outperforming PaddleOCR-VL-For-Manga
I graded 4 open-source PDF parsers on 12 capabilities for RAG ingestion. The failures are exactly the chunks your retriever needs
What are the best alternatives to AWS Textract for OCR?
Open-source OCR actually tested by document type (invoices vs contracts vs scans)
Architecting OW as the primary AI surface for a 2-person team — multi-model + custom KB + write-back. Looking for similar production setups.
Run GLM-OCR, DeepSeek-OCR-2, and dots.mocr with an OpenAI Compatible API
I tried to pick an OCR model by benchmark score and learned the numbers do not compare