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

Updated: April 2026 – GitHub stars and model versions are regularly updated. New: Muse Spark analysis, Gemma 4, Nemotron Cascade 2, and Kimi K2.5.

Why Open Source Matters for LLMs Now

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

This article gives you a complete 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, only 14 GB, 85 tokens per second on consumer hardware. The model that finally erases the line between cloud and local intelligence. → Our deep-dive on Gemma 4

Strengths:

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

Weaknesses:

• Gemma License (not pure Apache 2.0)
• MoE architecture – not all frameworks support it natively yet
• 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) 🆕

Moonshot AI's flagship from Beijing: 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 at math reasoning
• Modified MIT – commercially free under 100M MAU

Weaknesses:

• Very large – local use only with high-end hardware (128 GB+ RAM)
• Chinese provider – compliance concern
• Modified MIT requires attribution above certain 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 brings two variants: 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 on 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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⚠️ Muse Spark (Meta) – Proprietary Reference

Important: Muse Spark is not an open-source model. We include it here as a proprietary reference because it marks Meta's strategic departure from open source – and directly shows where open alternatives already outperform it.

Muse Spark is the first model from Meta's new Superintelligence Labs (MSL), available for free to all Meta users since April 2026. → Our critical analysis of Muse Spark

Where Muse Spark excels (and open source doesn't yet):

• HealthBench Hard: 42.8 – beats GPT-5.4 (40.1), no open-source model comes close
• Humanity's Last Exam: 50.2% – the Contemplating mode orchestrates multiple agents in parallel
• Free for 3+ billion Meta users

Where open source beats Muse Spark:

• Coding: Terminal-Bench 59.0 – even Qwen2.5-Coder-32B delivers better results for local code tasks
• Abstract Reasoning: ARC-AGI-2 42.5 vs. GPT-5.4 76.1 – a massive gap
• Agentic Tasks: Kimi K2.5 with Agent Swarm is the better choice for autonomous multi-step workflows
• Data Control: Muse Spark runs in Meta's cloud – no self-hosting option

The strategic break: Meta championed open source for years. Muse Spark is closed-source with no timeline for weight release. For the open-source community, this means: Llama remains Meta's open offering, but the company's best model is no longer freely available.

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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 deep-dive on Qwen3.5

Strengths:

• Beats GPT-5-mini in 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 enterprises

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

Weaknesses:

• Very large – locally 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. The European counterweight to US and Chinese models.

Strengths:

• European provider (Paris) – easier GDPR narrative
• Strong multilingual support
• 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 guidelines (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 on reasoning tasks.

Strengths:

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

Weaknesses:

• No multimodal in 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 when 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 whether the usage terms fit your case.

How to Run Open-Source LLMs Locally

The easiest ways to get an open-source model running 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 quality is now laptop-sized. And Meta's Muse Spark proves: even the loudest open-source advocate goes closed-source when frontier performance is at stake. 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
• Muse Spark as a warning sign: When proprietary models become free but without data control – open source isn't a nice-to-have, it's insurance

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

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→ Our AI Services → Meta Muse Spark: Critical Analysis → Gemma 4: Frontier Intelligence Goes Laptop-Sized → Project KNUT: Local AI Infrastructure with 52 GB VRAM → Hunter Alpha: The 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