Arcee AI: Coder Large
arcee-ai/coder-large
Access Coder Large 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/coder-large"
}).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/coder-large"
}).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/coder-large",
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/coder-large",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
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.
Context Window 33K
tokens
Max Output N/A
tokens
Input Cost $0.5
per million tokens
Output Cost $0.8
per million tokens
Release Date Mar 1, 2025
Model Playground
Try Coder Large instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Arcee AI
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ChatTrinity Large Preview
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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.
Frequently Asked Questions
You can access Coder Large 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 Coder Large 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 | $0.5 |
| Output | $0.8 |
Coder Large was created by Arcee AI and released on Mar 1, 2025.
Coder Large supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.
Yes — the Coder Large 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 Coder Large to your app without worrying about API keys or setup.
Read the Docs View Tutorials