THE AI MODEL DIRECTORY Start exploring models here

Find your ideal AI model.

From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.

Explore AI models 2

Provider Input Output License

Discover models worth your attention

meiki.text.detect.v0

rtr46

--- **Official GitHub Repository:** meikiocr This model is a core component of the `meikiocr` pipeline. For the full implementation, command-line script, and documentation, please see the official GitHub repository. meiki.text.detect.v0.1 is an update to meiki.text.detect.v0 (see below): - meiki.text.detect.v0.1 is a new state-of-the-art, open weight text detection model for video games beating text detection models like PaddleOCR - while it is still based on D-FINE detector, it uses mobilenet v4 small as backbone instead of hgnet v2 - v0.1 comes in 2 variants: v0.1.960x544 and v0.1.320x192. unlike v0 both v0.1 variants share the same architecture, but are trained on different resolutions - v0.1 models increase focus on video game text detection and are limited to 64 detected boxes, increasing efficency for this use case (making them less suitable for manga text detection out of the box) - v0.1.960x544 and v0.1.320x192 have better accuracy and lower latency than small.v0 and tiny.v0 respectively

Image captioning

Input Image→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

meiki.txt.recognition.v0

rtr46

--- **Official GitHub Repository:** meikiocr This model is a core component of the `meikiocr` pipeline. For the full implementation, command-line script, and documentation, please see the official GitHub repository. meiki.text.recognition achieves state-of-the-art text recognition accuracy as well as latency by redefining "text recognition" as "character detection". the model is a fine-tune of https://github.com/Peterande/D-FINE object detecor combined with a mobilenetv4 CNN backbone. to my knowledge there is no existing, open weight text recognition model with a better accuracy/latency tradeoff for japanese text recognition.

Image captioning

Input Image→ Output Text

Price not verifiedCheck multimodal and channel pricing separately

View details ↗

CompareSource / docs ↗

COST EXPLORER

Great models. Clear costs.

Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

Understand before you choose

Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.