Liquid AIOpen weights model1.2B parameters

LFM2 1.2B benchmarks

All models
  • General rank
    Too few tasks1 non-coding tasks ranked5 needed for a rank
  • Cost
    Self-hostedSmall GPU, ~1.3 GBNo hosted price
  • LangWatch policy#30 / 42 tied
    90.4%Best task · PII left invs PII detectors

Comparison summary

Strongest
Safety and guardrails
Leads 0 of 1 (0%)

A language model (LLM): it reads a prompt and writes text back, so the prompt sets the task. Strongest at safety.

Specifications

Lab
Liquid AI
Weights
Open weights
Parameters
1.2B
Licence
LFM Open License 1.0
Type
small LLM

Safety and guardrails

Leads 0 of 1, too few tasks to compare here.

See the 5 tasks

Traces

Not ranked

PII left in, % · Lower is better

  1. Amazon Comprehend10.6, interval 9.4 to 11.9, ahead alone
  2. GLiNER Multi PII v115.3, interval 13.2 to 17.3, tier 2
  3. NVIDIA GLiNER PII19.7, interval 17.4 to 22.0, tier 3
  4. Gemma 4 26B A4B19.8, interval 15.8 to 24.2, tier 3
  5. Qwen3.8 27B20.3, interval 16.5 to 24.3, tier 3
  6. GLiNER2 Large20.4, interval 18.0 to 22.8, tier 3
  7. Gemma 4 12B23.3, interval 19.0 to 27.6, tier 3
  8. LFM2 1.2B
    97.8, interval 96.3 to 99.0, not ranked

Business

Not ranked

PII left in, % · Lower is better

  1. Perplexity PII Tracer2.1, interval 1.2 to 3.0, tied for the lead
  2. Qwen3.8 27B2.4, interval 1.7 to 3.3, tied for the lead
  3. Gemma 4 31B2.9, interval 1.9 to 4.0, tied for the lead
  4. GLiNER Multi PII v14.1, interval 3.1 to 5.3, tier 2
  5. Ministral 3 14B5.4, interval 4.0 to 6.9, tier 2
  6. Gemma 4 12B5.8, interval 4.4 to 7.3, tier 3
  7. Amazon Comprehend5.9, interval 4.6 to 7.2, tier 3
  8. LFM2 1.2B
    91.6, interval 87.1 to 95.2, not ranked

Multilingual

Not ranked

PII left in, % · Lower is better

  1. Perplexity PII Tracer2.9, interval 2.5 to 3.4, ahead alone
  2. Qwen3.8 27B4.2, interval 3.7 to 4.8, tier 2
  3. Gemma 4 31B4.5, interval 3.9 to 5.1, tier 2
  4. GLiNER Multi PII v15.7, interval 5.2 to 6.3, tier 3
  5. bardsai EU PII Multilang6.9, interval 6.3 to 7.5, tier 4
  6. Qwen3.6 35B A3B7.7, interval 7.0 to 8.4, tier 4
  7. Gemma 4 12B8.9, interval 8.1 to 9.7, tier 5
  8. LFM2 1.2B
    98.7, interval 97.7 to 99.6, not ranked

Look-alikes

Not ranked

Look-alikes redacted, % · Lower is better

  1. Gemma 4 31B3.5, interval 2.1 to 5.1, ahead alone
  2. LFM2 1.2B
    4.3, interval 2.1 to 7.0, not ranked
  3. Qwen3.8 27B10.3, interval 7.9 to 12.9, tier 2
  4. NuExtract 319.8, interval 15.9 to 23.6, tier 3
  5. Qwen3.5 9B20.5, interval 16.8 to 24.3, tier 3
  6. Ministral 3 14B30.5, interval 26.5 to 34.8, tier 4
  7. NVIDIA GLiNER PII42.0, interval 38.2 to 46.1, tier 5
  8. Gemma 3 4B IT45.0, interval 41.0 to 49.0, tier 5

LangWatch policy

Tier 12 of 13#30 / 42

PII left in, % · Lower is better

  1. Gemma 4 31B6.9, interval 2.8 to 11.3, ahead alone
  2. Ministral 3 14B10.2, interval 5.8 to 15.4, tier 2
  3. Gemma 4 12B16.5, interval 10.3 to 23.2, tier 3
  4. Qwen3.8 27B16.7, interval 9.8 to 25.0, tier 3
  5. Gemma 4 26B A4B20.5, interval 13.3 to 28.8, tier 4
  6. Qwen3.5 9B21.5, interval 13.4 to 30.6, tier 4
  7. Gemma 3 4B IT22.4, interval 14.3 to 32.1, tier 4
  8. LFM2 1.2B
    90.4, interval 84.9 to 95.4, tier 12

Cost

Self-hosted · GPU memory needed, among small LLMs · lower is better

How we count

A model leads a task when it is in the task's leading tie tier. Each task counts inside its own benchmark, against that benchmark's models; no score is averaged. An area chart shows the models ranked on at least 2 of the area's tasks in a benchmark this model is in too. The overall standing counts every non-coding tasks and needs 5 for a rank. Latency is compared only on one hardware tier.

LFM2 1.2BOther modelsWhisker: 95% interval