Best Open Source LLMs: 25+ Models Compared for 2026

Last updated: January 2026

The best open source LLMs in 2026 are DeepSeek-V3.2 (best overall), Llama 4 Scout (best for long context), Qwen 3 (best multilingual), and Mistral Large 2 (best for European deployment). Open source models now match or exceed proprietary alternatives on most benchmarks while offering full control over weights, privacy, and deployment costs.

The gap between open source and closed source LLMs has nearly closed. DeepSeek-V3.2 achieves 94.2% on MMLU, competitive with GPT-4o. Llama 4 Scout handles 10 million token contexts. Qwen 3 supports 29+ languages with native fluency. And you can run capable 7B models on consumer hardware.

This guide covers 25+ open source models, organized by use case, with benchmarks, hardware requirements, and licensing details.

What Makes an LLM "Open Source"?

Not all "open" models are truly open source. The spectrum:

For this guide, we include models where weights are publicly available and commercial use is permitted (with or without restrictions).

Why it matters: True open source means you can fine-tune, deploy privately, and modify without restrictions. Open weights may have usage limits (e.g., Llama's 700M monthly user cap).

Best Open Source LLMs by Category

Quick Comparison Table

Best Overall: DeepSeek-V3.2

Parameters: 671B total, 37B active (MoE) Context: 128K tokens License: MIT (fully open source) Training cost: ~$5.6M (remarkably efficient)

DeepSeek-V3.2 represents a breakthrough in open source LLMs. It matches GPT-4o on most benchmarks while being fully open source under MIT license.

Benchmarks

Key Features

• DeepSeek Sparse Attention (DSA): Reduces inference cost by 70% for long inputs
• Mixture of Experts: Only 37B parameters active per token despite 671B total
• Think/Non-Think modes: Toggle reasoning depth based on task
• Multi-token prediction: Improved generation quality

Hardware Requirements

• Full precision: 350GB+ VRAM (8x A100 80GB)
• 4-bit quantized: 170GB+ VRAM
• API: Available via DeepSeek API ($0.14/M input, $0.28/M output)

When to Use

• Complex reasoning and analysis
• General-purpose assistant applications
• Research and development
• When you need GPT-4 quality with full control

Best for Long Context: Llama 4 Scout

Parameters: 109B total, 17B active (16 experts) Context: 10 million tokens License: Meta License (commercial with restrictions) Released: April 2025

Llama 4 Scout handles context windows that were previously impossible. 10 million tokens means entire codebases, book series, or years of documents in a single prompt.

Key Features

• 10M context window: Process entire repositories or document collections
• Natively multimodal: Text and image input, text output
• Multilingual: 200 languages, 10x more multilingual tokens than Llama 3
• Efficient MoE: Only 17B active parameters

Benchmarks

Hardware Requirements

• Full precision: 220GB+ VRAM
• 4-bit quantized: 55GB+ VRAM
• API: Available via Meta, together.ai, others

Licensing Note

Llama 4's license allows commercial use but includes restrictions:

• Cannot train other LLMs on outputs
• 700M monthly active user cap (above requires Meta agreement)
• Must include attribution

Best for Reasoning: DeepSeek-R1

Parameters: 671B total Context: 128K tokens License: MIT Training cost: ~$294K (on top of V3 base)

DeepSeek-R1 was trained specifically for reasoning tasks using reinforcement learning. It shows its "thinking" process, similar to OpenAI's o1.

Key Features

• Transparent reasoning: Shows step-by-step thought process
• RL-trained: Developed reasoning through reinforcement learning, not just SFT
• Distillable: Can distill reasoning capability into smaller models
• MIT license: Full commercial freedom

Benchmarks

When to Use

• Mathematical problem solving
• Complex logical reasoning
• Code debugging and algorithm design
• Tasks requiring verifiable step-by-step thinking

Best Multilingual: Qwen 3

Parameters: 0.6B to 235B (dense and MoE variants) Context: 128K tokens License: Apache 2.0 Developed by: Alibaba Cloud

Qwen 3 is the most capable multilingual open source model. It handles 29+ languages with native fluency, not just translation-level quality.

Model Variants

Key Features

• 29+ languages: Chinese, English, French, Spanish, German, Japanese, Korean, Arabic, and more
• Apache 2.0: Fully permissive commercial use
• Full size range: From edge (0.6B) to maximum capability (235B)
• Strong coding: Competitive with dedicated code models

Benchmarks (Qwen3-235B)

Best for EU Deployment: Mistral Large 2

Parameters: 123B Context: 128K tokens License: Apache 2.0 Developed by: Mistral AI (France)

Mistral Large 2 is the strongest model from a European company. Important for organizations with EU data residency requirements or preferences for non-US AI.

Key Features

• European origin: Developed in France, important for compliance
• Apache 2.0: Fully permissive license
• Strong reasoning: Competitive with GPT-4 on most tasks
• 128K context: Handles long documents well

Benchmarks

Other Mistral Models

Best for Code: Qwen 2.5-Coder

Parameters: 1.5B to 32B Context: 128K tokens License: Apache 2.0

Qwen 2.5-Coder leads open source code generation. The 32B variant matches GPT-4 on coding benchmarks.

Benchmarks

Size Options

Other Top Code Models

Best Small Models (Under 10B)

Small models run on consumer hardware while remaining highly capable.

Top Picks

Phi-4 (Microsoft)

Microsoft's Phi-4 punches far above its weight. At 14B parameters, it matches models 5-10x its size on reasoning tasks.

Key features:

• Trained on synthetic data and curated high-quality sources
• Strong reasoning despite small size
• MIT license
• Runs on consumer GPUs (RTX 3090, 4090)

Gemma 2 (Google)

Google's Gemma 2 offers strong performance in a small package.

License note: Gemma uses Google's custom license. Commercial use allowed but with some restrictions on certain applications.

Best for Local/Private Deployment

When you need to run models entirely on your own infrastructure:

Recommended Stack

Running Models Locally

Popular tools:

• Ollama: Simplest setup, great for beginners
• llama.cpp: Maximum performance, C++ based
• vLLM: Production serving with batching
• Text Generation Inference: HuggingFace's serving solution
• LM Studio: GUI for local models

Quantization Trade-offs

For most use cases, 4-bit quantization offers the best balance. Quality loss is typically 1-3% on benchmarks but often unnoticeable in practice.

Complete Model Directory

General Purpose Models

Reasoning Models

Code Models

Multimodal Models (Vision + Text)

Hardware Requirements Guide

Consumer Hardware

Professional/Server

Apple Silicon

Licensing Comparison

Understanding licenses is critical for commercial use.

Fully Permissive (MIT/Apache 2.0)

• DeepSeek (all models)
• Qwen (all models)
• Mistral (most models)
• Microsoft Phi series
• StarCoder

Commercial with Restrictions

• Llama 4: Cannot use outputs to train other LLMs. 700M monthly user cap.
• Gemma: Some restrictions on specific use cases. Check terms.

Choosing the Right Model

Decision Framework

Start here:

1. What's your hardware?

   • Consumer GPU (12-24GB): Phi-4, Gemma 2 9B, Mistral 7B
   • Workstation (48-64GB): Qwen3-32B, Mixtral 8x22B
   • Server cluster: DeepSeek-V3.2, Llama 4

2. What's your primary use case?

   • General assistant: DeepSeek-V3.2, Qwen3
   • Coding: Qwen2.5-Coder, DeepSeek-Coder
   • Reasoning/math: DeepSeek-R1
   • Long documents: Llama 4 Scout
   • Multilingual: Qwen3

3. What are your licensing needs?

   • Maximum freedom: DeepSeek, Qwen (MIT/Apache)
   • Enterprise with restrictions OK: Llama 4

Recommendations by Use Case

Running Open Source LLMs with Miniloop

Miniloop makes it easy to incorporate open source LLMs into data workflows. Instead of managing infrastructure, describe what you want and Miniloop generates the code.

Example workflows:

• "For each document in this folder, use DeepSeek to extract key information and save to a spreadsheet"
• "Process these customer reviews with Qwen, classify sentiment, and flag urgent issues"
• "Use Llama to summarize each article in this RSS feed and post daily digest to Slack"

Benefits:

• Use any open source model via API or local deployment
• Generated Python code you can inspect and modify
• No lock-in to specific model providers

Pricing: Free, $29/mo+

When to skip Miniloop:

• You only need to run a single model for simple inference
• You're building a custom application with full control over infrastructure
• You prefer direct API integration without an orchestration layer

For a broader view of the open source AI ecosystem, see our guide to open source AI.

The Future of Open Source LLMs

Trends to Watch

1. MoE becoming standard Mixture of Experts architectures (DeepSeek, Llama 4, Mixtral) offer better performance per compute. Expect most large models to use MoE.

2. Reasoning models Following DeepSeek-R1, more models will include explicit reasoning capabilities with transparent thought processes.

3. Longer contexts Llama 4 Scout's 10M context is just the beginning. Expect open source models to match or exceed proprietary context lengths.

4. Smaller and better Phi-4 proves small models can punch above their weight. More research into efficient training and architecture.

5. Multimodal by default Text-only models are becoming rare. Expect vision, audio, and video capabilities in most new releases.

FAQs About Open Source LLMs

What is the best open source LLM in 2026?

DeepSeek-V3.2 is the best overall open source LLM in 2026. It achieves 94.2% on MMLU (matching GPT-4o), uses an efficient MoE architecture (671B total, 37B active), and is fully open source under MIT license. For specific use cases: Llama 4 Scout for long context (10M tokens), Qwen3 for multilingual (29+ languages), DeepSeek-R1 for reasoning, and Qwen2.5-Coder for code generation.

What is the difference between open source and open weight LLMs?

Open source LLMs include weights, training code, and datasets under permissive licenses (MIT, Apache 2.0). Open weight models only release weights with usage restrictions. Examples: DeepSeek and Qwen are fully open source. Llama 4 is open weight but restricts using outputs to train other LLMs and caps commercial use at 700M monthly users. For maximum flexibility, choose truly open source models.

Can I use open source LLMs commercially?

Yes, but check the license. MIT and Apache 2.0 licenses (DeepSeek, Qwen, Mistral) allow unrestricted commercial use. Meta's Llama license allows commercial use but with restrictions: you cannot train other LLMs on outputs, and there's a 700M monthly active user cap. Google's Gemma license has some use-case restrictions. Always review the specific license for your chosen model.

What hardware do I need to run open source LLMs?

It depends on model size and quantization. Small models (7-14B) run on consumer GPUs: RTX 3060 (12GB) handles Mistral 7B, RTX 4090 (24GB) handles Qwen3-14B with 4-bit quantization. Medium models (30-70B) need workstation hardware: 48-64GB VRAM. Large models (400B+) need server clusters: DeepSeek-V3.2 requires 8x A100 80GB for full precision. Apple Silicon M1/M2 Ultra with 64-128GB unified memory can run 70B models.

What is the best open source LLM for coding?

Qwen2.5-Coder-32B is the best open source coding LLM. It achieves 92.7% on HumanEval, exceeding GPT-4o (90.2%). It's Apache 2.0 licensed and available in sizes from 1.5B to 32B. Alternatives: DeepSeek-Coder-V2 (236B MoE, MIT license), CodeLlama (7-70B, strong Python), StarCoder 2 (multi-language support). For local development, Qwen2.5-Coder-14B fits on an RTX 4090 with excellent quality.

What is the best small open source LLM?

Phi-4 (14B) and Gemma 2 9B are the best small open source LLMs. Phi-4 matches models 5-10x its size on reasoning tasks and runs on consumer GPUs with 4-bit quantization (8GB VRAM). Gemma 2 9B offers excellent efficiency at 6GB VRAM. For multilingual needs, Qwen3-8B is strong at 5GB VRAM. For absolute minimum size, Qwen3-0.6B and Phi-3-mini work on mobile devices.

How do open source LLMs compare to GPT-4 and Claude?

Top open source LLMs now match or exceed proprietary models on most benchmarks. DeepSeek-V3.2 achieves 94.2% on MMLU vs GPT-4o's 92.0%. DeepSeek-R1 matches OpenAI o1 on math and reasoning. The gap has effectively closed for most tasks. Proprietary models may still have edges in specific areas, safety tuning, or API convenience, but raw capability is now comparable. The main trade-off is deployment complexity vs. API simplicity.

What is DeepSeek and why is it important?

DeepSeek is a Chinese AI lab that released the most capable open source LLMs under MIT license. DeepSeek-V3 achieved GPT-4 level performance at 1/10th the training cost ($5.6M vs ~$100M). DeepSeek-R1 added transparent reasoning capabilities. All models are fully open source with no usage restrictions. DeepSeek proved that frontier AI capabilities can be achieved efficiently and shared openly, changing the competitive dynamics of the AI industry.

Should I use open source or proprietary LLMs?

Choose open source if you need: full control, privacy, cost efficiency at scale, or no vendor lock-in. Choose proprietary if you need: simplest setup, guaranteed availability, or prefer managed services. Open source advantages: deploy anywhere, fine-tune freely, no per-token costs at scale, data stays private. Proprietary advantages: no infrastructure management, consistent APIs, often better documentation. Many teams use both: proprietary for prototyping, open source for production scale.

Orchestrate LLMs in Your Workflows

Deploying an LLM is step one. Making it useful for your business is step two.

With Miniloop, you can build workflows that orchestrate LLMs to:

• Process documents and extract structured data
• Classify and route incoming requests
• Generate personalized content at scale
• Chain multiple AI steps together

Works with any LLM API. Describe what you want, and Miniloop generates the pipeline. Try it free or browse templates.

Related Reading

• Best AI Chatbots in 2026
• What Is AI Orchestration?
• What Is LLM Orchestration?

Related Resources

• AI Automation Tools – Connect your apps and automate with AI
• AI Agent Platform – Build and deploy autonomous AI agents
• Agentic Workflows – Workflows that combine AI reasoning with automated execution
• AI Orchestration – How to coordinate multiple AI tools
• Browse Templates – Pre-built workflow templates to get started