# Is LFM3-1.7B SOTA?

Pending benchmarks. Announced Oct 27, 2026; no independent results yet.

As of Nov 3, 2026, LFM3-1.7B is pending benchmarks on isitsota: announced Oct 27, 2026, with no independent results on the boards we track yet.

Liquid AI's LFM3 text model for local agents and specialized applications, on a new tri-hybrid architecture of gated short convolutions, sliding-window attention and global attention, with a 256K-token context. Open weights under the LFM Open License v1.0, with the pre-trained LFM3-1.7B-Base released alongside.

Canonical: https://isitsota.com/m/lfm3-1-7b
Last updated: 2026-10-27
Tracking since: 2026-10-27

## Announced facts

| Fact | Value |
| --- | --- |
| Lab | Liquid AI |
| Announced | Oct 27, 2026 |
| API | Live · Liquid AI API and Playground, per Liquid's blog |
| Open weights | Published |
| Parameters | 1.73B |
| Architecture | 24 layers: 13 gated short convolutions, 5 sliding-window attention (256-token window), 6 global attention |
| Context | 256K tokens (input + output) |
| Training | 22T tokens of pre-training (last 3.5T curated mid-training with agentic traces); SFT, mixed RL, multi-domain on-policy distillation and agentic RL |
| Languages | English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish |
| Memory | About 1 GB at 4-bit precision, per Liquid |
| Checkpoints | Post-trained LFM3-1.7B and pre-trained LFM3-1.7B-Base on Hugging Face (plus GGUF, MLX and ONNX builds) |
| License | LFM Open License v1.0 |

Tags marked unsure are not stated on the lab's own pages.

## What would let us call it

Verdicts use only independent boards we already track. Two consecutive snapshots are required before any crown.

- [x] Publicly announced with a source
- [x] Usable by the public (API or weights)
- [ ] Listed on an independent board we track
- [ ] Enough results for a rank in a category
- [ ] Holds across two snapshots (crowns only)

## What the lab claims

Self-reported. Shown for context; not used in any verdict.

- Lab claim: outperforms LFM2.5-2.6B with 35% fewer parameters, averaging 49.37 on Liquid's agent, tool-use, coding, instruction-following, knowledge and long-context table vs 47.24 for Qwen3.5-9B and 39.27 for Qwen3.5-4B.
- Lab claim: at 32K context (llama.cpp, Q4_0) it prefills at least 2.5x and decodes at least 1.8x faster than MiniCPM5-2B, Qwen3.5-4B, Qwen3.5-9B and Gemma 4 E4B on three devices, using the least memory.

## Sources

- LAB: [Liquid AI: Introducing LFM3](https://www.liquid.ai/blog/lfm3) (2026-10-27)
- LAB: [Liquid AI announcement on X](https://x.com/liquidai/status/2115128329003303107) (2026-10-27)
- REPO: [LiquidAI/LFM3-1.7B on Hugging Face](https://huggingface.co/LiquidAI/LFM3-1.7B)
- REPO: [LiquidAI/LFM3-1.7B-Base on Hugging Face](https://huggingface.co/LiquidAI/LFM3-1.7B-Base)

Tracking continues until an independent board lists it.

## Related

- Lab: [Liquid AI](https://isitsota.com/labs/liquid.md)
- All pending releases: https://isitsota.com/releases.md
- JSON: https://api.isitsota.com/v1/models/lfm3-1-7b