---
title: LFM2.5-1.2B-Thinking — Reasoning LLM | ModelsLab
description: Run LiquidAI LFM2.5-1.2B-Thinking free model for math, logic, and agent tasks with 32K context. Try on-device reasoning via API now.
url: https://modelslab.com/liquidai-lfm25-12b-thinking-free
canonical: https://modelslab.com/liquidai-lfm25-12b-thinking-free
type: website
component: Seo/ModelPage
generated_at: 2026-04-15T01:59:01.490230Z
---

Available now on ModelsLab · Language Model

LiquidAI: LFM2.5-1.2B-Thinking (free)
Reason On-Device Free
---

[Try LiquidAI: LFM2.5-1.2B-Thinking (free)](/models/open_router/liquid-lfm-2.5-1.2b-thinking-free) [API Documentation](https://docs.modelslab.com)

Reason Smarter Faster
---

Chain-of-Thought

### Generates Thinking Traces

Produces step-by-step reasoning before answers for math, logic, multi-step tasks.

32K Context

### Handles Long Inputs

Sustains 46 tok/s at full 32K context for extended documents and workflows.

Agentic Power

### Tool Use Optimized

Excels in planning tool calls, data extraction, RAG on LiquidAI LFM2.5-1.2B-Thinking free.

Examples

See what LiquidAI: LFM2.5-1.2B-Thinking (free) can create
---

Copy any prompt below and try it yourself in the [playground](/models/open_router/liquid-lfm-2.5-1.2b-thinking-free).

Math Proof

“Solve this equation step-by-step: prove that for all positive integers n, the sum of the first n odd numbers equals n squared. Show full reasoning chain.”

Logic Puzzle

“Three houses in a row, labeled A B C. A has red door, B blue, C green. Owners: Alice baker, Bob coder, Charlie engineer. Baker hates green. Coder in middle. Who lives where? Reason fully.”

Code Debug

“Debug this Python function that fails on large inputs: def factorial(n): if n == 0: return 1 else: return n \* factorial(n-1). Identify recursion issue and fix with reasoning.”

Data Analysis

“Analyze sales data: Q1:100 units $10k, Q2:150 $15k, Q3:120 $12k. Predict Q4 trend, suggest actions. Use chain-of-thought for agentic planning.”

For Developers

A few lines of code.
Reasoning API. One Call.
---

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

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about LiquidAI: LFM2.5-1.2B-Thinking (free)
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[Read the docs ](https://docs.modelslab.com)

### What is LiquidAI: LFM2.5-1.2B-Thinking (free)?

### How does liquidai lfm2 5 1.2 b thinking free compare to others?

### What is context length for LiquidAI: LFM2.5-1.2B-Thinking (free) API?

### Best use cases for LiquidAI: LFM2.5-1.2B-Thinking (free) model?

### Is LiquidAI: LFM2.5-1.2B-Thinking (free) LLM fine-tunable?

### Why choose LiquidAI: LFM2.5-1.2B-Thinking (free) alternative?

Ready to create?
---

Start generating with LiquidAI: LFM2.5-1.2B-Thinking (free) on ModelsLab.

[Try LiquidAI: LFM2.5-1.2B-Thinking (free)](/models/open_router/liquid-lfm-2.5-1.2b-thinking-free) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-04-15*