Arcee AI: Maestro Reasoning
arcee-ai/maestro-reasoning
Access Maestro Reasoning from Arcee AI 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: "arcee-ai/maestro-reasoning"
}).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: "arcee-ai/maestro-reasoning"
}).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="arcee-ai/maestro-reasoning",
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": "arcee-ai/maestro-reasoning",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Arcee Maestro Reasoning is a 32-billion-parameter analytical reasoning model from Arcee AI, derived from Qwen2.5-32B and post-trained with DPO and chain-of-thought reinforcement learning to produce step-by-step logical reasoning traces.
It targets complex problem-solving, abstract reasoning, multi-step scenario modeling, and tasks requiring transparent, auditable inference chains — a natural fit for legal, financial, and scientific applications. The 128k context window allows reasoning over long documents in a single call.
On Yupp's high-reasoning benchmark, Maestro Reasoning ranks among the top five models overall, competing with significantly larger frontier models. It delivers strong reasoning quality at a mid-tier parameter count.
Context Window 131K
tokens
Max Output 32K
tokens
Input Cost $0.9
per million tokens
Output Cost $3.3
per million tokens
Release Date May 5, 2025
Model Playground
Try Maestro Reasoning instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Arcee AI
Trinity Large Thinking
Trinity Large Thinking is a 398-billion-parameter sparse Mixture-of-Experts reasoning model from Arcee AI, with approximately 13B active parameters per token, post-trained with extended chain-of-thought and agentic reinforcement learning. It generates explicit reasoning traces in thinking blocks before final responses, and its 262K context window accommodates long agentic reasoning chains. Benchmark results include 94.7% on τ²-Bench and 98.2% on LiveCodeBench, placing it at #2 on PinchBench behind only Claude Opus 4.6. Released under Apache 2.0, Trinity Large Thinking is the strongest option in the Trinity family for agentic pipelines, long-horizon planning, complex multi-step coding, and tasks that benefit from transparent reasoning traces.
ChatTrinity Mini
Trinity Mini is a 26-billion-parameter sparse Mixture-of-Experts model from Arcee AI, with approximately 3B active parameters per token. It uses 128 experts with 8 active per token, blending global sparsity with gated attention techniques. Specifically tuned for multi-turn agent workflows, tool orchestration, function calling, and structured outputs, it scores 84.95 on MMLU and 59.67 on BFCL V3, with throughput exceeding 200 tokens per second. Released under Apache 2.0, the 128k context window and strong function-calling performance make Trinity Mini a practical choice for agentic systems, backend automation, and tool-use pipelines where inference speed and cost efficiency matter.
ChatCoder Large
Arcee Coder Large is a 32-billion-parameter code-generation model from Arcee AI, fine-tuned from Qwen2.5-Instruct on permissively-licensed GitHub data, CodeSearchNet, and synthetic bug-fix corpora. It generates compilable code, explains implementations, reviews diffs, and fixes bugs across 30+ programming languages, with particular strength in TypeScript, Go, and Terraform. A reinforcement learning stage specifically rewards compilable outputs, making it more reliable than general-purpose models on real developer prompts. The 32k context window supports multi-file refactoring and long diff review in a single API call. A strong choice for code-heavy pipelines where output correctness and structured explanations matter.
Frequently Asked Questions
You can access Maestro Reasoning by Arcee AI 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 Maestro Reasoning 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 | $0.9 |
| Output | $3.3 |
Maestro Reasoning was created by Arcee AI and released on May 5, 2025.
Maestro Reasoning supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Maestro Reasoning can generate up to 32K tokens in a single response.
Yes — the Maestro Reasoning 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 Maestro Reasoning to your app without worrying about API keys or setup.
Read the Docs View Tutorials