Open-Source LLMs Compared 2026 – 25+ Models You Should Know

Last updated: April 2026 – GitHub stars and model versions are updated regularly. New: Gemma 4, Nemotron Cascade 2, and Kimi K2.5 added.

Why Open-Source LLMs Matter Now

2025 was the year open-source LLMs closed the gap with proprietary models. In 2026, they're on par in many areas – or better. For businesses, that means more control, less vendor lock-in, and better GDPR compliance.

This article gives you a comprehensive overview of the most important open-source LLMs – with real GitHub data, hardware requirements, and clear recommendations.

The Big Comparison Table

Top Models in Detail

🔥 Gemma 4 (Google) 🆕

Google's new MoE flagship: 26B parameters, just 14 GB, 85 tokens per second on consumer hardware. The model that definitively blurs the line between cloud and local intelligence. → Our Gemma 4 deep dive

Strengths:

• 85 t/s on an AMD Ryzen AI MAX+ with 128 GB RAM
• 256K context window for long document analysis
• Function calling that actually works
• GPT-4-quality complex reasoning – locally, no cloud required

Weaknesses:

• Gemma License (not pure Apache 2.0)
• MoE architecture – not all frameworks support it natively
• No video input yet

GitHub: github.com/google/gemma.cpp · 8,500+ ⭐

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🐉 Hunter Alpha → Xiaomi MiMo-V2-Pro (formerly "likely DeepSeek V4")

⚡ Update April 2026: Hunter Alpha was confirmed on March 18, 2026 as Xiaomi's MiMo-V2-Pro – it was never DeepSeek V4. The team is led by Luo Fuli, a former DeepSeek engineer. → The full story · → China's AI Offensive: The Analysis

The largest AI model available on OpenRouter: >1 trillion parameters, with ~42B active parameters per token. Originally launched anonymously on March 11, 2026, now commercially available under Xiaomi's MiMo brand.

Strengths:

• 1T parameters with ~42B active (MoE) – largest available model

• 1M token context window
• ClawEval 61.5 – strong agentic performance
• Known provider (Xiaomi, publicly listed)
• Open source planned after stabilization

Weaknesses:

• No longer free ($1–2 / MTok input, $3–6 / MTok output)
• Not locally runnable (OpenRouter API only for now)
• Privacy: OpenRouter logging policies still apply

Access: openrouter.ai/xiaomi/mimo-v2-pro

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🌙 Kimi K2.5 (Moonshot AI)

Beijing-based Moonshot AI's flagship: 1 trillion parameters with MoE (32B active), 384 experts, and a unique Agent Swarm architecture. → The Cursor controversy: Why Composer 2 runs on Kimi K2.5

Strengths:

• Agent Swarm: coordinates up to 100 sub-agents for complex tasks
• Multimodal (text + image + video)
• AIME 2025: 96.1% – beats all frontier models on math reasoning
• Modified MIT license – commercial use free below 100M MAU

Weaknesses:

• Very large – local deployment requires high-end hardware (128 GB+ RAM)
• Chinese provider – compliance considerations
• Modified MIT adds attribution requirements above thresholds

GitHub: github.com/MoonshotAI/Kimi-K2.5 · 5,000+ ⭐

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⚡ Nemotron Cascade 2 (NVIDIA) 🆕

NVIDIA's new inference-optimized model: 30B parameters, runs at ~54 t/s on Project KNUT (RTX 4060 Ti + RTX 3060). Specifically designed for fast local inference. → Project KNUT: Local AI Infrastructure

Strengths:

• 54 t/s on consumer GPUs – 15x faster than human speech
• Quality comparable to GPT-4o mini
• Optimized for NVIDIA hardware (CUDA)

Weaknesses:

• NVIDIA license (not Apache 2.0)
• Primarily designed for NVIDIA GPUs
• Still relatively small community

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🦙 Llama 4 (Meta)

Meta's latest generation comes in two flavors: Scout (109B, 10M context) and Maverick (400B, for quality). Both use Mixture-of-Experts – only 17B parameters are active per query.

Strengths:

• Largest context window of any open-source model (10M tokens with Scout)
• Strong community and ecosystem
• Multimodal (text + image)

Weaknesses:

• Llama License isn't "true" open source (commercial restrictions above 700M MAU)
• Large models require significant hardware

GitHub: github.com/meta-llama/llama-models · 7,500+ ⭐

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🌐 Qwen3.5 (Alibaba)

Currently the strongest open-source MoE model. 122B parameters, only 10B active – runs on a MacBook with 64 GB RAM. → Our Qwen3.5 deep dive

Strengths:

• Beats GPT-5-mini on most benchmarks
• Apache 2.0 – true open source
• 262k context window (expandable to 1M)

Weaknesses:

• No multimodal (text only)
• Chinese provider – compliance concern for some organizations

GitHub: github.com/QwenLM/Qwen3 · 27,000+ ⭐

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🔬 DeepSeek-R1

The model that shook the AI world in early 2025. 671B parameters with MoE (37B active), specialized in chain-of-thought reasoning.

Strengths:

• Reasoning quality on GPT-o1 level
• MIT license – maximum freedom
• "Thinking" mode shows the reasoning process

Weaknesses:

• Very large – local use only with high-end hardware
• Chinese provider

GitHub: github.com/deepseek-ai/DeepSeek-V3 · 102,000+ ⭐

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🌊 Mistral Large 2

Mistral's flagship: 123B parameters, 128k context, 80+ languages. Europe's counterweight to the US and Chinese models.

Strengths:

• European provider (Paris) – easier GDPR narrative
• Strong multilingual capabilities
• Apache 2.0

Weaknesses:

• Smaller community than Llama or Qwen
• Fewer specialized variants

GitHub: github.com/mistralai/mistral-inference · 10,700+ ⭐

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💎 Gemma 3 (Google)

Google's open model family from 1B to 27B – optimized for on-device use. Multimodal from 4B. Now the predecessor to Gemma 4, but still relevant for edge deployments.

Strengths:

• Multimodal (text + image) even in small variants
• Runs on smartphones and Raspberry Pi
• ShieldGemma for safety

Weaknesses:

• Gemma License has usage policies (not pure Apache 2.0)
• Maximum size only 27B

GitHub: github.com/google/gemma.cpp · 6,800+ ⭐

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🧠 Phi-4 (Microsoft)

Microsoft's "Small Language Model" with 14B parameters that beats larger models at reasoning tasks.

Strengths:

• Outstanding quality per parameter
• MIT license
• Runs on consumer hardware

Weaknesses:

• No multimodal in the base variant
• Small context window (16k)

GitHub: github.com/microsoft/phi-4 · 12,000+ ⭐

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Coding LLMs Compared

For developers, there are specialized code models:

Our recommendation: Qwen2.5-Coder-32B for maximum quality, StarCoder 2-3B if it needs to run locally on a laptop.

Decision Matrix: Which Model for Which Use Case?

Hardware Guide: What Do You Actually Need?

Licenses: The Devil in the Details

Not every "open-source" model is equally open:

Tip: For commercial projects, prefer Apache 2.0 or MIT. With Llama, check carefully whether the usage terms fit your case.

How to Run Open-Source LLMs Locally

The easiest ways to start an open-source model on your machine:

1. Ollama – One command: ollama run gemma4 – done
2. LM Studio – GUI for non-developers, drag & drop GGUF models
3. vLLM – For production deployments with high throughput
4. llama.cpp – C++ runtime, maximum CPU performance

→ More about GGUF, GGML, and Safetensors

Our Take

The question is no longer "cloud or local?" – it's "which model for which task?". With Gemma 4, the answer has shifted again: frontier intelligence is now laptop-sized. Our recommendation:

• Gemma 4 locally as the new default for most tasks
• Cloud APIs for customer chatbots and creative tasks (Claude, GPT-5)
• Open source locally for sensitive data, bulk processing, and prototyping
• Hybrid architecture as the goal: the best model for every job, regardless of provider

The future doesn't belong to one model – it belongs to the architecture that's flexible enough to use any model.

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→ Our AI services → Gemma 4: Frontier intelligence goes laptop-sized → Project KNUT: Local AI infrastructure with 52 GB VRAM → Hunter Alpha: The world's largest free AI model → Kimi K2.5: The model behind Cursor's Composer 2 → Qwen3.5 deep dive: 122B parameters on your laptop → AI agents compared