Prism ML: Ternary Bonsai 27B
prism-ml/ternary-bonsai-27b
Access Ternary Bonsai 27B from Prism ML 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: "prism-ml/ternary-bonsai-27b"
}).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: "prism-ml/ternary-bonsai-27b"
}).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="prism-ml/ternary-bonsai-27b",
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": "prism-ml/ternary-bonsai-27b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Ternary Bonsai 27B is PrismML's ternary-weight build of Qwen3.6 27B, compressing the model to roughly 1.71 bits per weight while retaining about 95% of full-precision quality.
Across a 15-benchmark suite covering knowledge, reasoning, math, coding, instruction following, tool calling, and vision, it averages 80.49 in thinking mode, including 99.20 on MATH-500, 93.90 on HumanEval+, 82.75 on LiveCodeBench, and 74.41 on BFCL v3 for tool calling. It also handles vision input, scoring 68.96 on MMMU-Pro.
The model supports a 262K-token context window and structured JSON output, making it suited for long-document analysis, coding agents, and tool-calling workflows. PrismML also offers a 1-bit variant of Bonsai 27B with a smaller footprint; the ternary build trades some of that compression for higher accuracy across the same benchmark suite.
Context Window 262K
tokens
Max Output 262K
tokens
Input Cost $0
per million tokens
Output Cost $0
per million tokens
Release Date Jul 16, 2026
Model Playground
Try Ternary Bonsai 27B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Frequently Asked Questions
You can access Ternary Bonsai 27B by Prism ML 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 Ternary Bonsai 27B 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 |
| Output | $0 |
Ternary Bonsai 27B was created by Prism ML and released on Jul 16, 2026.
Ternary Bonsai 27B supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Ternary Bonsai 27B can generate up to 262K tokens in a single response.
Yes — the Ternary Bonsai 27B 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 Ternary Bonsai 27B to your app without worrying about API keys or setup.
Read the Docs View Tutorials