AI

LFM2.5 24B A2B (on-device MoE)

Global Rank
#27
Intelligence Index
28.8%
Blended Price / 1M
$0.2125
Throughput Speed
220 tps
Latency (TTFT)
480 ms
Context Window
131.072K
Price Efficiency
—
VERIFIED TELEMETRY

Benchmark Evaluations

View Benchmark Suites →
GPQA Diamond (default) 67.0%
SciCode (default) 32.0%
SWE-bench Verified (default) 29.0%
Terminal-Bench 2.0 (default) 28.5%
LiveCodeBench (default) 27.5%

Model Overview & Capabilities

LFM2.5-24B-A2B is Liquid AI’s revolutionary on-device reasoning Mixture-of-Experts model released on October 13, 2026, securing Rank #28 on our global leaderboard with an index score of 28.8. Activating just 2.45 billion parameters per token from its 24-billion parameter base, it achieves verified scores of 29.0% on SWE-bench Verified, 28.5% on Terminal-Bench 2.0, 29.5% on Aider Polyglot, and 66.0% on GPQA Diamond.

The model is freely downloadable for private on-device execution on consumer hardware, with cloud endpoints available at $0.05 per million input tokens and $0.70 per million output tokens ($0.21 blended rate under 3:1 weighting). It delivers an astonishing inference velocity of 220 tokens per second with an ultra-responsive 0.48-second initial latency.

With a 131,072-token context window, LFM2.5-24B is purpose-built for local-first developer workstations, offline coding assistants, embedded systems, automotive edge computing, and privacy-sensitive corporate environments where proprietary source code cannot leave the local developer workstation. Its compact memory footprint allows software engineers to execute sophisticated reasoning loops entirely offline without any network connectivity.

Pricing Trend History

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Abdullah Zulfiqar
Abdullah ZulfiqarFounder & Technical EditorLast updated 21 October 2026

Abdullah Zulfiqar is the founder and editor of VibeCoderJournal, an independent publication covering AI coding tools, model benchmarks and developer workflows. His coverage includes Claude Code, Codex CLI, local model setup and benchmark methodology. See the About and Editorial Standards page for scoring definitions, source limitations and correction contacts.

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