Compare / EXAONE 4.0 1.2B vs LFM2.5 1.2B Thinking

EXAONE 4.0 1.2B vs LFM2.5 1.2B Thinking

Updated November 2026

Verdict

EXAONE 4.0 1.2B scores higher on the Small LM Index: 36.1 vs 30.1, although their likely ranges overlap, so the lead is not certain. On the 5 benchmarks both labs report, EXAONE 4.0 1.2B wins 3 and LFM2.5 1.2B Thinking wins 2. Both need about 1.8 GB of memory at 4-bit quantization. Neither model has a hosted API on OpenRouter, so you would run both yourself.

Pick EXAONE 4.0 1.2B if…

  • you want the strongest model overall
  • you need math and reasoning
  • you work with long documents

Pick LFM2.5 1.2B Thinking if…

  • you need precise instruction following
  • you build agents that call tools

Specifications

EXAONE 4.0 1.2BLFM2.5 1.2B Thinking
Small LM Index36.1 (likely 33–40)30.1 (likely 27–34)
LabLG AI Research (EXAONE) · Rest of AsiaLiquid AI · United States
Released2025-07-152026-01-20
Parameters1.3B (dense)1.2B (dense)
Memory at 4-bit~1.8 GB~1.3 GB
Context window64K tokens32K tokens
Inputtexttext
ReasoningHybrid (thinking on/off)Always thinks
LicenseEXAONE License (non-commercial)LFM Open License
API price / 1M tokensSelf-host onlySelf-host only
Hugging Face downloads (30 days)13.3K8.1K

Benchmarks

Higher is better. Bold marks the winner where both labs report a score. ≈ marks the index model's estimate for a score that wasn't reported, ✓ an independent measurement and * a score measured by another lab.

BenchmarkEXAONE 4.0 1.2BLFM2.5 1.2B Thinking
MMLU-Pro Knowledge59.349.7
GPQA Diamond Knowledge52.037.9
AIME Math45.231.7
LiveCodeBench Coding45.3≈ 32
IFBench Instruction following—44.9
MMLU Knowledge71.5—
Multilingual MMLU Multilingual62.4—
IFEval Instruction following67.888.4
BFCL Agentic52.957.0
MATH-500 Math—88.0
HMMT Math34.0—

Scores are mostly reported by each lab with its own settings, so small gaps are noise. See the methodology. Open in the interactive comparison to add more models.

Questions

Which is better, EXAONE 4.0 1.2B or LFM2.5 1.2B Thinking?

EXAONE 4.0 1.2B scores higher on the Small LM Index: 36.1 vs 30.1, although their likely ranges overlap, so the lead is not certain. On the 5 benchmarks both labs report, EXAONE 4.0 1.2B wins 3 and LFM2.5 1.2B Thinking wins 2.

Which is cheaper to run?

Neither model has a hosted API on OpenRouter, so you would run both yourself. Both need about 1.8 GB of memory at 4-bit quantization.

Which has the longer context window?

EXAONE 4.0 1.2B has the longer context window: 64K tokens against 32K.

Can I use EXAONE 4.0 1.2B and LFM2.5 1.2B Thinking commercially?

EXAONE 4.0 1.2B is released under EXAONE License (non-commercial) and LFM2.5 1.2B Thinking under LFM Open License. Apache 2.0 and MIT allow commercial use; other licenses can add conditions, so read the license before shipping a product.

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