Inference.net API
Access Inference.net instantly with Puter.js, and add AI to any app in a few lines of code without backend or API keys.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain AI like I'm five!", {
model: "inference-net/cliptagger-12b"
}).then(response => {
console.log(response);
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain AI like I'm five!", {
model: "inference-net/cliptagger-12b"
}).then(response => {
console.log(response);
});
</script>
</body>
</html>
List of Inference.net Models
ClipTagger 12B
inference-net/cliptagger-12b
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.
ChatSchematron V2 Small
inference-net/schematron-v2-small
Schematron V2 Small is a 3-billion-parameter HTML-to-JSON extraction model from Inference.net, fine-tuned from Meta's Llama 3.2 3B. It takes a JSON schema and raw HTML as input and returns schema-conformant structured output; extraction instructions go through the schema itself rather than a system or user prompt. Inference.net built it as the quality-focused tier of its V2 lineup, approaching 8B-level accuracy at roughly 3B-level speed, alongside a faster Turbo variant. On Inference.net's internal LLM-as-judge benchmark it scores 4.06 out of 5, close to the original Schematron 8B's 4.07 and ahead of the first-generation 3B's 3.91. On SimpleQA it scores 83.1, and Inference.net reports both V2 models beating DeepSeek V3.2 and GPT-5.4 Nano on that benchmark despite being much smaller. It targets web scraping, product data ingestion, financial document parsing, and search-augmented pipelines needing reliable structured output over long pages, with a 128K context window and 4,096-token output limit.
ChatSchematron V2 Turbo
inference-net/schematron-v2-turbo
Schematron V2 Turbo is a 3-billion-parameter model from Inference.net built to turn messy HTML into JSON matching a schema you supply, using schema-constrained decoding so the output is valid JSON by construction rather than by retry. It's the throughput-optimized sibling to Schematron V2 Small, handling 4.14 requests per second on a single H100 versus Small's 2.47, a 2.5x gain over the original Schematron 3B. It shares Small's 128,000-token context but doubles its output limit, to 8,192 tokens. On Inference.net's quality benchmark it scores 4.039 out of 5, close to Small's 4.060, and both beat DeepSeek V3.2 and GPT-5.4 Nano on extraction accuracy. On SimpleQA, a web-search factuality benchmark, it reaches 79.42 against Small's 83.10. It suits web scraping and data extraction pipelines that prioritize speed and cost; the endpoint doesn't support tool calling.
Frequently Asked Questions
The Inference.net API gives you access to models for AI chat. Through Puter.js, you can start using Inference.net models instantly with zero setup or configuration.
Puter.js supports a variety of Inference.net models, including ClipTagger 12B, Schematron V2 Small, and Schematron V2 Turbo. Find all AI models supported by Puter.js in the AI model list.
With the User-Pays model, users cover their own AI costs through their Puter account. This means you can build apps without worrying about infrastructure expenses.
Puter.js is a JavaScript library that provides access to AI, storage, and other cloud services directly from a single API. It handles authentication, infrastructure, and scaling so you can focus on building your app.
Yes — the Inference.net API through Puter.js works with any JavaScript framework, Node.js, or plain HTML. Just include the library and start building. See the documentation for more details.