AI 模型情報

LFM2-24B-A2B

openrouter/lfm-2-24b-a2b

出品方: openrouter · 系列: liquid · 發布 2026-02-25

$0.030
輸入 / 1M token
$0.120
輸出 / 1M token
33K
上下文長度
33K
最大輸出

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

能力清單

工具呼叫推理結構化輸出附件開放權重溫度可調
支援模態: 輸入 text · 輸出 text

Model fit scores

0–100 · higher is better

These scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.

Coding1
  • Tool calling0/40
  • Structured output0/20
  • Reasoning0/10
  • Context window (100K → 1M)0/20
  • Provider availability1/10
Agents16
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output30
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control10/10
  • Price-friendly for high-volume20/20
Cost efficiency82
  • Headline price (log-scaled)82/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Production-readiness65
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K15/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)15/15

Cost Efficiency Index

Open full calculator →

Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.

ScenarioCostAssumption
RAG answer
per 1,000 RAG answers
$0.21
< $0.01 per request
5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot.
Support ticket triage
per 10,000 tickets
$0.42
< $0.01 per request
1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support.
Data extraction
per 1,000 documents
$0.12
< $0.01 per request
2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines.
Code review
per 1,000 PRs
$0.36
< $0.01 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$0.43
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

定價詳情

推薦定價來自 openrouter · liquid/lfm-2-24b-a2b

$0.030
輸入
$0.120
輸出

於 1 家供應商可用

服務商服務商模型 ID輸入 / 1M輸出 / 1M上下文發布日期
OpenRouter
openrouter
liquid/lfm-2-24b-a2b$0.030$0.12033K2026-02-25

Frequently asked questions

How much does LFM2-24B-A2B cost?

LFM2-24B-A2B costs $0.030 per 1M input tokens and $0.120 per 1M output tokens, sourced from openrouter. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of LFM2-24B-A2B?

LFM2-24B-A2B has a context window of 33K tokens, with a max output of 33K tokens per reply. This is the total combined size of prompt + completion.

Does LFM2-24B-A2B support tool calling?

No. LFM2-24B-A2B does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does LFM2-24B-A2B support structured output / JSON mode?

No. LFM2-24B-A2B does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.

Can LFM2-24B-A2B accept image input?

No. LFM2-24B-A2B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is LFM2-24B-A2B open-weight?

Yes. LFM2-24B-A2B's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.

What are the best alternatives to LFM2-24B-A2B?

If LFM2-24B-A2B doesn't fit, consider INTELLECT-3, LFM2.5-1.2B-Thinking (free), LFM2.5-1.2B-Instruct (free). Each one targets the same use case — see the Related links below for direct head-to-head pages.

Where does this data come from?

All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.

More openrouter models

Capability lists this model is in

最近更新:

Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.