LFM2.5-1.2B-Instruct (free)
LFM2.5-1.2B-Instruct (free) is a 1.2B-parameter, instruction-tuned hybrid language model from LiquidAI, optimized for fast, on-device inference with a ~32k token context window.
What is LFM2.5-1.2B-Instruct (free)?
LFM2.5-1.2B-Instruct (free) is a compact, instruction-tuned text-generation model from LiquidAI designed for fast, on-device AI with a context window of roughly 32k tokens. It is mainly used for general-purpose chat, agentic workflows, data extraction, and retrieval-augmented generation where low latency and small memory footprint are important. The model is also positioned for multi-language conversational tasks across several major languages, though it is not recommended as a top choice for highly knowledge-intensive or advanced programming workloads. It belongs to the LFM2.5 family of hybrid on-device models, building on the earlier LFM2 architecture with extended pre-training and reinforcement learning-based post-training.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
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$0.00 | $0.00 | — | ~180ms | ~40 tps | ~99.9% |
|
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~$0.15 | ~$0.60 | — | ~220ms | ~60 tps | 99.9% |
|
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~$0.20 | ~$0.80 | — | ~250ms | ~50 tps | 99.9% |
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~$0.12 | ~$0.48 | — | ~210ms | ~70 tps | 99.9% |
Prices, context and availability from the OpenRouter public catalogue (this model is not served through LLM.API), updated nightly. Last updated 18 Sept 2026.
Try this model
Test LFM2.5-1.2B-Instruct (free) right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://inference.example.com/v1"
)
response = client.chat.completions.create(
model="liquidai/lfm2-5-1-2b-instruct-free",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "liquidai/lfm2-5-1-2b-instruct-free",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Conversational Chat
Instruction-tuned chat model supporting multi-turn dialogue, general assistance, and natural conversation with strong instruction-following behavior.
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Text Generation
Generates coherent, context-aware text for prompts, explanations, and open-ended tasks using a 1.2B-parameter on-device-optimized architecture.
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Multilingual Support
Understands and generates text in multiple languages, including English, Arabic, Chinese, and several others, for diverse global use cases.
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Tool and Function Use
Supports structured outputs, function calling, and tool use, enabling integration into agentic pipelines and automation workflows.
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Edge Deployment
Designed for fast, low-memory inference on CPUs and NPUs, enabling on-device AI experiences on laptops, mobiles, and IoT hardware.
6 Most Valuable Use Cases
- On-device AI Chat
- Mobile Task Assistance
- Edge Data Extraction
- Lightweight Text Analysis
- RAG Answer Generation
- CPU-Optimized Inference
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without touching your app code.
One endpoint, every model -
Cost-Aware Optimization
Dynamically pick cheaper equivalent models, control spend with policy-based limits, and monitor per-project usage so you never get surprised by your AI bill.
Cut spend, keep quality -
Resilient Fallbacks
Configure automatic failover to backup models and providers when requests fail or time out, keeping your AI features online even during provider outages.
No single point of failure -
Deep Observability
Get full visibility into every call—latency, errors, tokens, and model choices—with logs and traces that plug into your existing monitoring stack.
See every token and trace -
Task-Level Abstractions
Define high-level tasks—chat, classification, extraction, tools—once and let LLM.API pick and orchestrate the right models and prompts for each job.
Code to tasks, not models -
High-Throughput Batch
Process millions of inputs efficiently with optimized batching, concurrency controls, and retry semantics tailored for large-scale offline and backfill workloads.
Scale from 10 to millions
When to Use — When NOT to Use
Use it if...
- You need a free, small-footprint instruct model for light-weight experimentation and prototyping.
- Your use case involves simple Q&A, definitions, or short factual clarifications on common topics.
- You need a compact model suitable for on-device or low-resource server deployments.
- Your use case involves generating short emails, messages, or template-based business text.
- You need a model to assist with basic code snippets or minor refactoring tasks.
- Your use case involves educational examples or demos where cutting-edge capability is unnecessary.
- You need a backup or fallback model when larger, paid models are unavailable.
Avoid if...
- You need state-of-the-art reasoning, planning, or complex multi-step chain-of-thought solutions.
- Your workload requires handling very long documents, transcripts, or multi-document context windows.
- You need highly reliable, domain-expert outputs for medical, legal, or financial decisions.
- Your workload requires advanced coding assistance across large repositories and complex software architectures.
- You need high-quality creative writing, nuanced style control, or sophisticated story generation.
- Your workload requires robust tool-use, API orchestration, or complex multi-agent system coordination.
- You need strong multilingual performance or translation quality across many low-resource languages.
Frequently Asked Questions
-
What is LFM2.5-1.2B-Instruct (free)?
LFM2.5-1.2B-Instruct (free) is a 1.2B-parameter LiquidAI instruction-tuned language model optimized for fast, low-cost text generation via LLM.API.
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What is LFM2.5-1.2B-Instruct (free) best suited for?
It is best for lightweight chatbots, tool-using agents, code helpers, and simple reasoning tasks where low latency and free usage are more important than peak accuracy.
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How is LFM2.5-1.2B-Instruct (free) priced on LLM.API?
The model is available in a free tier on LLM.API, meaning requests are not directly metered by tokens but may be subject to fair-use limits.
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What is the context window of LFM2.5-1.2B-Instruct (free)?
LFM2.5-1.2B-Instruct (free) supports a context window of up to 8,192 tokens per request on LLM.API.
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What modalities does LFM2.5-1.2B-Instruct (free) support?
This model is text-only, accepting text prompts and returning text completions without native image, audio, or video understanding.
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How fast is LFM2.5-1.2B-Instruct (free) on LLM.API?
Being a 1.2B-parameter model, it is optimized for low latency and generally responds faster than larger LiquidAI or frontier models under similar conditions.
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How do I call LFM2.5-1.2B-Instruct (free) through LLM.API?
Specify the model name "liquidai/lfm2.5-1.2b-instruct-free" (or the documented identifier) in your LLM.API completion or chat endpoint request.
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How does LFM2.5-1.2B-Instruct (free) compare to larger LiquidAI or frontier models?
It is cheaper and faster but has weaker long-context reasoning, creativity, and coding depth than larger LiquidAI or state-of-the-art models.
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Does LFM2.5-1.2B-Instruct (free) support tools or function calling via LLM.API?
You can use it with LLM.API’s tool-calling layer, but the model itself does not implement a native structured tool-calling protocol.
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What are the main limitations of LFM2.5-1.2B-Instruct (free)?
It can hallucinate facts, struggle with complex multi-step reasoning, and may perform poorly on very long documents compared to larger models.
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