Inference.net: ClipTagger 12B
inference-net/cliptagger-12b
Access ClipTagger 12B from Inference.net using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "inference-net/cliptagger-12b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "inference-net/cliptagger-12b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="inference-net/cliptagger-12b",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "inference-net/cliptagger-12b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
ClipTagger 12B is a 12-billion-parameter vision-language model built by Inference.net in collaboration with Grass, based on the Gemma-12B architecture and trained through knowledge distillation from a frontier VLM on a million curated video frames.
It is purpose-built for frame-by-frame video captioning at scale, returning a fixed JSON schema per frame with description, detected objects, actions, environment, content type, production quality, and logos.
Using Gemini-2.5-Pro as judge, it scored 3.53 on caption accuracy against GPT-4.1's 3.64 and Claude 4 Sonnet's 3.16, with ROUGE-1 of 67.4%, ROUGE-L of 52.0%, and BLEU of 26.7% against its teacher model. At roughly $335 per million generations, it costs about 15x less than GPT-4.1 and 17x less than Claude 4 Sonnet.
It suits teams building searchable video databases, content moderation pipelines, or ad verification and analytics tools, and is released under Apache-2.0.
Context Window 125K
tokens
Max Output 2K
tokens
Input Cost $0.3
per million tokens
Output Cost $0.5
per million tokens
Release Date N/A
Model Playground
Try ClipTagger 12B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Frequently Asked Questions
You can access ClipTagger 12B by Inference.net through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add ClipTagger 12B to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.3 |
| Output | $0.5 |
ClipTagger 12B supports a context window of 125K tokens. For reference, that is roughly equivalent to 250 pages of text.
ClipTagger 12B can generate up to 2K tokens in a single response.
Yes — the ClipTagger 12B API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add ClipTagger 12B to your app without worrying about API keys or setup.
Read the Docs View Tutorials