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LFM3-1.7B is a compact 1.7B-parameter text model from Liquid AI with a 256K-token context, post-trained for agents, tool calling and structured outputs. It reasons before it answers.

料金: 入力:100 万トークンあたり 19.2 クレジット(約 $0.10)、出力:100 万トークンあたり 48 クレジット(約 $0.24)。

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LFM3-1.7B の使い方ガイド

LFM3-1.7B API on ModelFold

LFM3-1.7B is a compact 1.7B-parameter text model from Liquid AI with a 256K-token context, post-trained for agents, tool calling and structured outputs. It reasons before it answers. Try LFM3-1.7B in the ModelFold Playground, then call it from your own code through an OpenAI-compatible chat completions API.

What Is the LFM3-1.7B API

LFM3-1.7B at a glance

LFM3-1.7B is a chat model from Liquid AI. It accepts text as input and returns text, with a context window of up to 262K tokens.

Use it without hosting anything

On ModelFold you call LFM3-1.7B with a single API key — no model weights to download and no inference servers to run. The same key also works for the other chat, image, video and audio models on ModelFold.

Core Capabilities of LFM3-1.7B

Up to 262K tokens of context

Keep long conversations, large documents and sizeable code in a single request, so the model can reason over the full material instead of fragments.

Function calling

LFM3-1.7B supports tool (function) calling: describe your functions in the request and the model returns structured calls your code can run — the basis for agents and integrations.

Reasoning mode

LFM3-1.7B can produce reasoning before its final answer; in the Playground you can switch reasoning on or off to compare results.

Structured outputs

Ask for JSON that follows a schema (response_format), which makes extraction, classification and system integrations more reliable.

How to Use LFM3-1.7B on ModelFold

  1. 1

    Step 1: Try it in the Playground

    Open the Playground at the top of this page, type a prompt and see how LFM3-1.7B responds before writing any code.

  2. 2

    Step 2: Create an API key

    Sign in to ModelFold and create an API key in the console. Keep it secret and send it as a Bearer token.

  3. 3

    Step 3: Send a request

    Call POST https://modelfold.ai/v1/chat/completions with "model": "lfm-3-1-7b" and your messages. Existing OpenAI-compatible SDKs work by changing the base URL and key.

  4. ✓

    Step 4: Ship and monitor

    Integrate the response into your product and check token usage and spend for every call in the console logs.

What You Can Build with LFM3-1.7B

Tool-calling backends

Route requests, call functions and chain API calls as the worker model inside larger agent systems.

High-volume pipelines

Run classification, extraction, rewriting and batch jobs at scale where cost per request matters.

Low-latency experiences

Power chat interfaces, autocomplete and interactive tools where users expect fast responses.

Autonomous agents

Build agents that plan multi-step tasks, call your tools and APIs, and keep working toward a goal across long sessions.

Frequently Asked Questions

What is LFM3-1.7B?⌄

LFM3-1.7B is a compact 1.7B-parameter text model from Liquid AI with a 256K-token context, post-trained for agents, tool calling and structured outputs. It reasons before it answers.

Which inputs does LFM3-1.7B support?⌄

LFM3-1.7B accepts text and returns text. Through ModelFold you send text messages; documents attached in the Playground are converted to text.

How long is the context window of LFM3-1.7B?⌄

Up to 262K tokens, shared between your input and the model’s output.

Does LFM3-1.7B support function calling?⌄

Yes. LFM3-1.7B supports tools / function calling through the standard tools parameter of the chat completions API.

How is LFM3-1.7B billed on ModelFold?⌄

Pay as you go by tokens: input and output tokens are charged at the per-million-token prices shown on this page. There is no monthly fee, and failed requests are not charged.

How do I call LFM3-1.7B through the API?⌄

Send a POST request to https://modelfold.ai/v1/chat/completions with "model": "lfm-3-1-7b", using your ModelFold API key as the Bearer token.

料金表
入力19.2 クレジット 1M tokens≈ $0.10
出力48 クレジット 1M tokens≈ $0.24

トークン単位で課金し、入力と出力は別料金です。思考トークンは出力に含まれます。Playground でも同じ計算を使います。

API ドキュメントを見る →AI Agents · Claude Code / Cursor / Trae →
クイックスタート
POST/v1/chat/completions
curl https://modelfold.ai/v1/chat/completions \
  -H "Authorization: Bearer $MODELFOLD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"lfm-3-1-7b","messages":[{"role":"user","content":"Hello"}]}'