Testing Liquid AI's LFM2.5 2.6B: 90t/s Speed but Fails at Tool Calling
curiousily_ · reddit · 2026-08-06
A developer tested Liquid AI's LFM2.5 (2.6B dense model) using llama.cpp on an M5 Pro. Under Q8 quantization, the model achieved impressive speeds of 90 tokens/s and consumed only about 4GB of memory, highlighting its efficiency for local deployment.
However, the model underperformed significantly in agentic tasks. Compared to a competitor from its official benchmarks (Qwen3.5 4B at Q4), LFM2.5 struggled notably with tool calling. In the OpenCode environment, it had severe issues making actual tool calls and was genuinely confused about the working directory.
Related event: Liquid AI 2.6B Model Matches DeepSeek in Tool Calling, 3.7x Faster(3 posts)→
More from coding & agent
- ERP•AI launches headless SaaS with $0 seats that bills machines: $1 buys 4,000 record writes — rohanpaul_ai · 2026-10-16
- FDE-Gym benchmark shows huge gap on real AI delivery tasks: top model scores 0.563 — DeepLethe · 2026-10-16
- ModelClock launches free read-only MCP server to track model deprecations — zomenaro · 2026-10-16
- Vibe-coded dashboard leaked franchise financials before anyone logged in — shauntrennery · 2026-10-16
- Fintech HR built a sourcing agent on 60,000 past applicants — 30 qualified candidates in a month — G0NZ0BOY · 2026-10-16
- Orchestrating a 100-image portrait of humanity for aliens with ChatGPT, Argon and p5.brush — dx8xb · 2026-10-16