Korean LLMs Compared: Benchmarking 6 Lightweight AI Models

Korean LLM Spotlight: Performance, Language Understanding, Coding & Reasoning Compared – In-depth Ellice Benchmark Report

South Korea’s AI landscape has entered a marketing boom of “large language models (LLMs).” While various companies roll out domestic models highlighting unique strengths, it remains challenging for users and developers to objectively identify which model suits which task.

So how should one choose the model that fits both our service and technical needs?

The problem lies in inconsistent evaluation methods and datasets across models—a simple numerical comparison fails to reflect real performance differences. This creates a potential risk for businesses and developers implementing AI solutions without sufficient insight.

To eliminate this information imbalance and enable fair comparison of domestic translation models, Ellice releases a benchmark of six Korea-made lightweight LLMs evaluated under identical conditions.

Benchmark Setup & Evaluation Criteria

Ellice built the benchmarking environment using the open-source framework lm-evaluation-harness, ensuring consistency and reproducibility.


We tested across four real-world usage areas:

• Korean Understanding (KOBEST)
• Coding Performance (HumanEval+ & MBPP)
• Logical Reasoning (GSM8K)
• Instruction Compliance & Safety (IFEval)

What Are We Actually Measuring?

1. Korean Proficiency Benchmark: KOBEST

• What this measures:
  Can the model accurately comprehend Korean sentences—understanding vocabulary, grammar, meaning, and inference?
• In simple terms:
  The model is taking the Korean-language SAT. Can it deduce implied meaning or summarize key themes from text?
• Why does this matter?
  High scores translate to better performance in chat, summarization, translation, and analysis using Korean input.

2. AI Coding Interview: HumanEval+ & MBPP

• What this measures:
  Can the model correctly write functioning code from natural language prompts?
• In simple terms:
  It answers “Write a program that does X”—just like in a developer job interview.
• Why does this matter?
  Good performance signals that the model can serve as an effective coding assistant in real development workflows.
  

3. Elementary Math Competition: GSM8K

• What this measures:
  Can the model solve word problems step by step and logically?
• In simple terms:
  It’s like the model is competing in an elementary school math contest.
• Why does this matter?
  This reflects its suitability for data analysis, automated reporting, and any task requiring logical reasoning.

4. “Follow Instructions Precisely” Test: IFEval

• What this measures:
  Can the model accurately interpret complex instructions and enforce constraints?
• In simple terms:
  It tests the ability of the model as a “do-what-you’re-told AI assistant.”
• Why does this matter?
  Essential for areas like automated content creation, business document assistance, and legal/financial compliance.

Models Included in the Benchmark

Performance Comparison Summary

Key Insights

1. Helpy Edu C – Precision-Driven Education Model

Top scores in both coding (HumanEval 0.872) and math reasoning (GSM8K 0.824), proving it’s ideal for educational assistance.

2. EXAONE 7.8B – Balanced, Enterprise-Grade LLM

Consistently top-tier results across all categories—coding, reasoning, and instruction compliance—making it a strong choice for production use.

3. Trillion 7B – Korean-Savvy Specialist

Scores highest in KOBEST (0.795), showcasing clear strength in Korean text processing and summarization tasks.

4. HyperCLOVA Series – Performance Scaling with Model Size

Increasing parameter count from 0.5B to 1.5B shows clear performance gains in coding and reasoning benchmarks.

Conclusion

This benchmark isn’t about declaring a single “best” model.
Ellice conducted a fair, standardized comparison of domestic LLMs to provide transparent guidance for model selection.

The real question isn’t who performs best—but which model best aligns with your service goals.
That’s why Ellice designed this benchmark—to empower informed decision-making.

Try Them Yourself: Elice ML API

Most of the benchmarked models are available through the Elice ML API for hands-on testing in your own environment.
Discover the strengths that numbers alone can’t reveal—run them, test them, compare them.

Why Use Elice ML API?

✅ Fully compatible with OpenAI API—just swap endpoints
✅ Up to 90% cost reduction vs. major vendors
✅ Korean billing & SLA support for enterprises
✅ Deploy anywhere—serverless to dedicated environments

Curious which model works best for your service?
Want to see how each performs with your own tasks?

👉 Try them today via Elice ML API.
The best way to compare models is to use them.