Free, Unlimited Arcee AI API
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In this tutorial, you will learn how to add Arcee AI models into your application for free using Puter.js. You can gain access to open-source models such as such as Trinity Mini, Spotlight, Maestro Reasoning, Virtuoso large, and Coder Large without having to set up the AI server yourself.
Puter.js uses the User-Pays model, where users of your application cover their own AI costs. This means you as a developer don't pay anything for your users' usage, making your app practically free to run. You can scale to unlimited users and pay nothing for the AI or server usage.
Getting Started
Add Puter.js to your project with a single line:
<script src="https://js.puter.com/v2/"></script>
That's it, you're ready to start integrating Arcee AI into your application.
Example 1: Basic Chat
Virtuoso Large is Arcee AI's general purpose LLM for every day use such as Q&A and writing, while still maintaining strong reasoning performance.puter.ai.chat("Explain the concept of quantum computing in simple terms", { model: "openrouter:arcee-ai/virtuoso-large" })
.then(response => {
puter.print(response);
});
Full code example:
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain the concept of quantum computing in simple terms", { model: "openrouter:arcee-ai/virtuoso-large" })
.then(response => {
puter.print(response);
});
</script>
</body>
</html>
Example 2: Complex Reasoning
Maestro Reaoning excels at complex analysis, and is the flagship reasoning model by Arcee AI.<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
async function streamResponse() {
const response = await puter.ai.chat(
"Debug this logic: If I have 3 apples and give away 5, how many do I have?",
{ model: "openrouter:arcee-ai/maestro-reasoning", stream: true }
);
for await (const part of response) {
if (part?.reasoning) puter.print(part?.reasoning);
else puter.print(part?.text);
}
}
streamResponse();
</script>
</body>
</html>
Example 3: Efficient Reasoning
Trinity Mini is engineered for efficient reasoning through its smaller parameter size (26B).<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
async function streamResponse() {
const response = await puter.ai.chat(
"Debug this logic: If I have 3 apples and give away 5, how many do I have?",
{ model: "openrouter:arcee-ai/trinity-mini", stream: true }
);
for await (const part of response) {
if (part?.reasoning) puter.print(part?.reasoning);
else puter.print(part?.text);
}
}
streamResponse();
</script>
</body>
</html>
Example 4: Image Analysis
Spotlight is optimized for visual tasks such as image to text analysis and multimodal conversation.<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat(
"What do you see in this image?",
"https://assets.puter.site/doge.jpeg",
{ model: 'openrouter:arcee-ai/spotlight' }
).then(response => {
puter.print(response);
});
</script>
</body>
</html>
Example 5: Code Generation
Coder Large is Arcee AI's coding language model with 32K context window. It understands more than 30 programming languages.<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat(
"Write a Python function that implements binary search on a sorted array",
{ model: "openrouter:arcee-ai/coder-large" }
)
.then(response => {
puter.print(response, {code: true});
});
</script>
</body>
</html>
List of Arcee AI models
You can use the following Arcee AI models with Puter.js:
openrouter:arcee-ai/trinity-mini:free
openrouter:arcee-ai/trinity-mini
openrouter:arcee-ai/spotlight
openrouter:arcee-ai/maestro-reasoning
openrouter:arcee-ai/virtuoso-large
openrouter:arcee-ai/coder-large
Conclusion
Using Puter.js, you can gain access to Arcee AI models without having to set up the AI server yourself. And thanks to the User-Pays model, your users cover their own AI usage, not you as the developer. This means you can build powerful applications without worrying about AI usage costs.
You can find all AI features supported by Puter.js in the documentation.
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