Liquid AI: LFM2.5-2.6B
liquid/lfm-2.5-2.6b:free
Access LFM2.5-2.6B from Liquid 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: "liquid/lfm-2.5-2.6b:free"
}).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: "liquid/lfm-2.5-2.6b:free"
}).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="liquid/lfm-2.5-2.6b:free",
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": "liquid/lfm-2.5-2.6b:free",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
LFM2.5-2.6B is a dense on-device agentic model from Liquid AI, pre-trained on roughly 34 trillion tokens with a 128K context window and native tool calling built for running inside agent harnesses.
It leads on instruction-following and tool-use benchmarks among models its size, scoring 59.17 on IFBench and 77.83 on ToolSandbox versus Qwen3.5-9B's 56.47 and 76.44. It trails Qwen3.5-9B on BFCLv4 (56.88 vs. 60.13) and AIME25 math reasoning (51.87 vs. 56.07), despite Qwen3.5-9B being more than 3x larger.
Liquid AI recommends it for high-volume agentic workloads, tool use, data extraction, RAG, and long-context tasks on edge devices, and advises against using it for agentic coding or knowledge-heavy work. For API callers, it's a solid choice when fast tool-calling and instruction following matter more than raw coding or general knowledge depth.
Context Window 128K
tokens
Max Output N/A
tokens
Input Cost $0
per million tokens
Output Cost $0
per million tokens
Release Date Aug 11, 2026
Model Playground
Try LFM2.5-2.6B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Frequently Asked Questions
You can access LFM2.5-2.6B by Liquid 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 LFM2.5-2.6B 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 |
LFM2.5-2.6B was created by Liquid AI and released on Aug 11, 2026.
LFM2.5-2.6B supports a context window of 128K tokens. For reference, that is roughly equivalent to 256 pages of text.
Yes — the LFM2.5-2.6B 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 LFM2.5-2.6B to your app without worrying about API keys or setup.
Read the Docs View Tutorials