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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.

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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
LiquidAI $0.00 $0.00 — ~180ms ~40 tps ~99.9%
OpenAI (GPT-4o-mini-equivalent) ~$0.15 ~$0.60 — ~220ms ~60 tps 99.9%
Anthropic (Claude 3 Haiku-equivalent) ~$0.20 ~$0.80 — ~250ms ~50 tps 99.9%
Google (Gemini 1.5 Flash-equivalent) ~$0.12 ~$0.48 — ~210ms ~70 tps 99.9%

Try this model

Test LFM2.5-1.2B-Instruct (free) right here — free to start.

LFM2.5-1.2B-Instruct (free)
Hi! Want to test the model?

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Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        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

  • Conversational Chat

    Instruction-tuned chat model supporting multi-turn dialogue, general assistance, and natural conversation with strong instruction-following behavior.

  • Text Generation

    Generates coherent, context-aware text for prompts, explanations, and open-ended tasks using a 1.2B-parameter on-device-optimized architecture.

  • Multilingual Support

    Understands and generates text in multiple languages, including English, Arabic, Chinese, and several others, for diverse global use cases.

  • Tool and Function Use

    Supports structured outputs, function calling, and tool use, enabling integration into agentic pipelines and automation workflows.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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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