Liquid AI: LFM2-24B-A2B

Public pricingIntelligence 79/100Medium memory

LiquidAI: LFM2-24B-A2B is a text model for general chat, analysis, and production use. It combines low latency and efficient inference with a 33K tokens context window and a low-cost profile. Use it for general chat, analysis, and production workloads when latency, cost, and throughput matters.

Input

$0.03/1M

Output

$0.12/1M

Cached

$0.01/1M

Batch

$0.01/1M

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Technical specifications

LFM2-24B-A2B at a glance.

Memory

32,768

tokens

Max reply

8,192

tokens

Memory tier

Medium

a long report or a codebase file

Tokenizer

—

Released

Mar 2026

Training cutoff

Oct 2025

Availability

Public pricing

Status

active

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick LFM2-24B-A2B

  • High-volume workloads where unit cost matters.

When to look elsewhere

  • Your workload involves images — pick a vision-capable model instead.
  • You need tool-use / function calling for agent workflows.

Alternatives

Similar models to compare.

FAQ

LFM2-24B-A2B — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, LFM2-24B-A2B costs roughly $7 per month. Input is $0.03 /1M tokens and output is $0.12 /1M tokens.
LFM2-24B-A2B has a 32,768-token context window (medium memory — a long report or a codebase file). That means you can fit about 6,144 words of input and history in a single call.
Beyond text generation, LFM2-24B-A2B supports fine-tuning on your own data. It streams replies by default.
LFM2-24B-A2B was released in March 2026, with training data cut off around October 2025.
Models in a similar class include LFM2.5-1.2B-Instruct, LFM2.5-1.2B-Thinking, Llama 3 8B Instruct. The "Similar models" section below this FAQ links into each.

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