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Available now on ModelsLab · Language Model

Arcee AI: Trinity MiniEfficient MoE Reasoning

Run Agents Seamlessly

Sparse MoE

3B Active Params

26B model activates 3B per token from 128 experts for low-latency inference.

Long Context

131K Token Window

Handles extended inputs with strong utilization for grounded multi-turn responses.

Tool Calling

Reliable Function Use

Delivers schema-true JSON and agent recovery in Arcee AI: Trinity Mini API.

Examples

See what Arcee AI: Trinity Mini can create

Copy any prompt below and try it yourself in the playground.

Code Review

Review this Python function for bugs and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)

JSON Schema

Generate valid JSON for user profile schema with name, email, age over 18, and preferences array.

Agent Workflow

Plan multi-step task: fetch weather API for NYC, compare to Tokyo, output summary in table format.

Document Summary

Summarize key points from this 10K token RAG document on quantum computing advancements.

For Developers

A few lines of code.
Reasoning. Few lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
import requests
response = requests.post(
"https://modelslab.com/api/v7/llm/chat/completions",
json={
"key": "YOUR_API_KEY",
"prompt": "",
"model_id": ""
}
)
print(response.json())

FAQ

Common questions about Arcee AI: Trinity Mini

Read the docs

Ready to create?

Start generating with Arcee AI: Trinity Mini on ModelsLab.