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Open-Source LLM Comparison 2026 - LLaMA, Mistral, Qwen, Gemma, DeepSeek

A comprehensive comparison of major open-source LLMs in 2026. Covers LLaMA 3, Mistral, Qwen 2.5, Gemma 2, DeepSeek-V3 performance, licensing, Korean capabilities, and selection guide.

Data DynamicsApril 16, 20264 min read

As open-source LLM performance approaches commercial models, more enterprises are choosing open-source for cost savings and data security. This post comprehensively compares the major open-source LLMs as of 2026.

1. Why Open-Source?

BenefitDescription
Cost savingsUnlimited inference on own servers without API charges
Data securityData never leaves premises
CustomizationFree Fine-Tuning, quantization, domain adaptation
TransparencyModel architecture, training data disclosed
Vendor independenceNo dependency on specific API services

2. Comprehensive Comparison

Model Specifications

ModelDeveloperSizesContextLicense
LLaMA 3.1Meta8B/70B/405B128KLlama License
Qwen 2.5Alibaba0.5B~72B128KApache 2.0
DeepSeek-V3DeepSeek671B (MoE)128KMIT
Mixtral 8x22BMistral AI141B (MoE)64KApache 2.0
Gemma 2Google2B/9B/27B8KGemma License
Phi-4Microsoft14B16KMIT

Benchmark Comparison

ModelMMLUHumanEvalGSM8KMT-Bench
LLaMA 3.1 405B88.689.096.88.8
LLaMA 3.1 70B86.080.595.18.6
Qwen 2.5 72B86.186.695.88.7
DeepSeek-V387.182.691.68.5
Phi-4 14B84.882.694.98.5
Gemma 2 27B75.268.082.38.1

3. Model Analysis

LLaMA 3.1 (Meta)

  • Strengths: Top general performance, long context (128K), largest community
  • Weaknesses: License restrictions (separate agreement above 700M MAU)
  • Best for: General enterprise use, Fine-Tuning base

Qwen 2.5 (Alibaba)

  • Strengths: Diverse sizes (0.5B~72B), coding/math excellence, multilingual (Korean included), Apache 2.0
  • Best for: Asian multilingual services, coding assistants

DeepSeek-V3

  • Strengths: MoE for cost-efficient inference, top-tier performance, MIT license
  • Best for: Cost-conscious deployments with large infrastructure

Phi-4 (Microsoft)

  • Strengths: 14B with 70B-level performance, math/reasoning excellence, MIT
  • Best for: Small high-performance model needs, math/science tasks

4. Korean Language Performance

ModelKorean UnderstandingKorean GenerationKorean-Specific Training
Qwen 2.5 72BExcellentExcellentYes (CJK enhanced)
LLaMA 3.1 70BGoodGoodNo (general multilingual)
DeepSeek-V3GoodGoodYes (CJK enhanced)
Phi-4 14BFairFairNo (English-focused)

Note: For Korean services, Qwen 2.5 or LLaMA 3.1 70B are recommended. Qwen is specifically trained on CJK languages with superior Korean performance.

5. License Comparison

ModelLicenseCommercial UseKey Restrictions
LLaMA 3.1Llama LicenseYesSeparate agreement above 700M MAU
Qwen 2.5Apache 2.0YesNo restrictions
DeepSeek-V3MITYesNo restrictions
Mixtral 8x22BApache 2.0YesNo restrictions
Phi-4MITYesNo restrictions

6. Selection Guide

ScenarioRecommendedReason
General enterprise chatbotLLaMA 3.1 70BBest general performance, large community
Korean serviceQwen 2.5 72BBest Korean performance
Coding assistantQwen 2.5 72B-CoderTop coding benchmarks
Math/science reasoningPhi-4 14BBest reasoning for size
Cost-efficient servingDeepSeek-V3 (MoE)Few active parameters
Edge/mobile deploymentGemma 2 2B / Qwen 2.5 0.5BUltra-lightweight
Fine-Tuning baseLLaMA 3.1 8BLargest ecosystem

References

  • Meta. "Llama 3.1 Model Card" — https://github.com/meta-llama/llama-models
  • Alibaba. "Qwen 2.5 Technical Report." arXiv
  • DeepSeek. "DeepSeek-V3 Technical Report." arXiv
  • Google. "Gemma 2: Improving Open Language Models." arXiv
  • Microsoft. "Phi-4 Technical Report." arXiv

— Data Dynamics Engineering Team