Roboflow

Best Image Tagging Models

Compare the best 89 image tagging models and try 83 of them on your own images, free in the Roboflow Playground. 31 are open-weight, so you can self-host them for free under their licenses.

89 models · 31 open-weight · 83 free to try · prices synced Nov 5, 2026

We haven't benchmarked image tagging yet; scores and rankings appear on this site only where we've measured them. Until then, test the models below side by side, and for fixed categories in production expect a model fine-tuned on your own data to win.

Open-Source Image Tagging Models

31 models with downloadable weights you can self-host under their licenses (Custom, MIT, and Apache 2.0). 27 run live in the Playground through hosted APIs, so self-hosting is optional.

Actions
816B total / 42B activeCustomOct 2026
Try
Mistral
1.05T total, 49B activeCustomOct 2026
Try
309B total, 15B activeMITSep 2026
Try
1.02T total, 42B activeMITSep 2026
Try
320B total, 18B activeMITAug 2026
Try
Qwen
27.78BApache 2.0Aug 2026
Try
29.6BApache 2.0Aug 2026
Try
MoonshotAI
Kimi K3Moonshot AI
2.8TModified MITJul 2026
Try

Image Tagging Models via API

58 proprietary models where the weights aren't downloadable: access is through each provider's API and billed by them. Try 56 of them free in the Playground.

Actions
OpenAINEW
$1.00$5.001.1MOct 2026
Try
Google
$2.00$10.001MOct 2026
Try
Meta
$4.00$20.001.0MOct 2026
Try
Anthropic
$0.10$0.501MOct 2026
Try
OpenAI
$2.00$10.001.1MSep 2026
Try
Anthropic
$2.00$10.001MSep 2026
Try
Anthropic
$4.00$20.001MSep 2026
Try
OpenAI
$0.10$0.501.1MSep 2026
Try

How to Choose an Image Tagging Model

Tagging assigns many keywords per image, and the choice hinges on one question: is your tag vocabulary open-ended or fixed?

CLIP-style models score any tag text against an image, so your vocabulary can be whatever you want it to be at query time, and VLMs will simply write a keyword list from a prompt. This suits digital asset management and photo search, where the useful tags differ per library and evolve over time.

When tags come from a controlled vocabulary (product attributes, content categories), a trained multi-label classifier with tuned per-tag thresholds is more consistent, far cheaper per image, and easier to evaluate: precision and recall per tag against a fixed list. Zero-shot approaches drift on synonyms and borderline cases that a trained model learns to call consistently.

The bottom line: Open, evolving vocabularies favor CLIP-style or VLM tagging; a fixed taxonomy at volume favors a trained multi-label classifier with calibrated thresholds.

Frequently Asked Questions About Image Tagging Models

Yes. 31 of the 89 image tagging models here are open-weight (for example Muse Spark Open, Mistral Large 4, and MiMo V2.6 Flash), free to self-host under their licenses (Custom, MIT, and Apache 2.0).

Yes. You can run 83 of them in the Roboflow Playground for free. Upload an image and compare the models' output side by side, no setup required.

Explore other vision task pages, compare models head to head, or browse the full model catalog.