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Liquid AI releases LFM2.5-24B-A2B for efficient agent tasks

By PulseAugur Editorial · [1 sources] ·

Liquid AI has released LFM2.5-24B-A2B, a large open-weight model designed for agentic tasks. This model utilizes a mixture-of-experts approach, activating approximately 2 billion parameters per token out of its total 24 billion, aiming to balance capability with cost efficiency. Key upgrades include a significantly expanded context window of 131,072 tokens, a doubled vocabulary for improved multilingual support, and enhanced tuning for agent frameworks like OpenClaw and Hermes Agent. While excelling in tool use and instruction following on its own benchmarks, larger models still outperform it in knowledge-intensive and coding tasks. AI

IMPACT Enables more cost-effective local agent deployment and private data processing.

RANK_REASON Frontier-lab model release with system card. [lever_c_demoted from frontier_release: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Liquid AI releases LFM2.5-24B-A2B for efficient agent tasks

How we ranked this

Signal score
56 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Significant
Frontier-lab model release with system card. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, product
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High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 (CA) · NeurAITool ·

    Liquid AI LFM2.5: Small Model, Big Agent Power

    <p>Most agent models ask you to choose between capability and cost. Liquid AI's LFM2.5-24B-A2B tries to remove that trade-off: it is a large open-weight model that only wakes up a small slice of itself for each token.</p> <h2> The idea in plain terms </h2> <p>The headline is simp…

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