Model Cost Profile

LiquidAI: LFM2.5-1.2B-Thinking (free)

Developer: liquid

Pricing updated Mar 11, 2026

Input rank: #9Output rank: #9

Live Pricing

Input: $0.0000

Output: $0.0000

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

LiquidAI's LFM2.5-1.2B-Thinking model offers a substantial context window of 32,768 tokens, making it ideal for applications requiring deep contextual understanding, such as document summarization and complex dialogue systems. As a free API model, it allows teams to experiment and integrate advanced AI capabilities without incurring input or output costs, facilitating budget-friendly development and deployment. This model is particularly advantageous for startups and research teams looking to leverage large-scale language processing without financial barriers.

๐Ÿง  Reasoning

Context Window

32,768

Tokens

Input Price / 1M

$0.0000

Prompt tokens

Output Price / 1M

$0.0000

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

LiquidAI: LFM2.5-1.2B-Thinking (free) Pricing Trend

Input / 1M tokensOutput / 1M tokens
Mar 7 โ€” Mar 11
$0.000000$0.000000$0.000000Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.000000

Current Output / 1M

$0.000000

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FAQ

Common pricing and benchmark questions for LiquidAI: LFM2.5-1.2B-Thinking (free).

How much does LiquidAI: LFM2.5-1.2B-Thinking (free) cost per 1M input tokens?

LiquidAI: LFM2.5-1.2B-Thinking (free) input pricing is $0.0000 per 1M tokens based on the latest synced provider data.

How much does LiquidAI: LFM2.5-1.2B-Thinking (free) cost per 1M output tokens?

LiquidAI: LFM2.5-1.2B-Thinking (free) output pricing is $0.0000 per 1M tokens based on the latest synced provider data.

What context window does LiquidAI: LFM2.5-1.2B-Thinking (free) support?

LiquidAI: LFM2.5-1.2B-Thinking (free) supports a context window of 32,768 tokens.

How can I compare LiquidAI: LFM2.5-1.2B-Thinking (free) with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.