10 Best Open-Source LLM Models (2025 Updated): Llama 4, Qwen 3 and DeepSeek R1

The world of open source large language models is moving at an incredible pace, with new contenders emerging seemingly every month. For developers, researchers, and businesses, choosing the right model is a critical decision that dictates the performance, cost, and scalability of next-generation AI applications. This guide cuts through the noise to provide a comprehensive roundup of the top open-source LLM models you need to know for 2025.

What “Open” Really Means: A Quick Guide

The term "open" isn't a single standard. It exists on a spectrum from truly open-source to more restrictive "source-available" models. Understanding the license is crucial, especially for commercial use.

How We Picked Our Top 10

This list is based on a blend of quantitative and qualitative signals to reflect real-world utility:

• Task Versatility: Strong performance across core tasks like reasoning, coding, and multilingual understanding.
• License Permissiveness: Preference for models with business-friendly licenses like Apache 2.0 and MIT.
• Local Deployment: Ease of running the model locally, with clear VRAM requirements for quantized versions.
• Context Window: Models offering large context windows for tasks like RAG on long documents.
• Benchmark Signals: Strong, consistent performance on trusted leaderboards like the Hugging Face Open LLM Leaderboard and the human-preference-based LMSys Chatbot Arena. (Note: Benchmarks are useful signals but can be gamed; real-world testing is essential).
• Community Adoption: Active community support, fine-tuning recipes, and integration with popular tools.

Quick Comparison of the Top 10 Open-Source LLM Models

The 10 Best Open-Source LLMs in Detail

Here's a closer look at each model, its strengths, and how to get started.

1. Qwen3 (235B-A22B)

• What it is: Qwen3’s flagship MoE model. 235B total, ~22B active per token. Strong on reasoning, code, and multilingual. Native 32,768 context, 131,072 with YaRN.
• Best for: High-end local or server deployment where MoE efficiency matters.
• License: Apache-2.0.
• Quick start: ollama run qwen3:235b (Q4_K_M variants available).
• Gotchas: GGUF/Q4 runs exist but speed depends on offload and sequence length.
• Official link: HF model card and Qwen3 announcement.

2. Mixtral 8x22B

• What it is: A powerful Mixture-of-Experts (MoE) model from Mistral AI. It uses 8 distinct "expert" networks and activates two of them for each token, providing massive parameter scale with manageable compute. This makes it incredibly fast and efficient for its size.
• Best for: High-quality reasoning and general chat that rivals top-tier proprietary models, all under a fully permissive license.
• License: Apache 2.0 (Fully permissive for commercial use).
• Quick Start: Due to its size, vLLM is recommended: python -m vllm.entrypoints.openai.api_server --model mistralai/Mixtral-8x22B-Instruct-v0.1
• Gotchas: Its large total parameter count means it requires significant VRAM (~80GB for 4-bit quantization), putting it out of reach for most consumer hardware.
• Official Link: Mixtral 8x22B Blog Post

3. Llama 4 (Scout / Maverick)

• What it is: Meta’s Llama 4 family. Scout and Maverick are instruction-tuned variants with 128k context and strong general performance.
• Best for: General chat, agents, broad ecosystem support.
• License: Llama 4 Community License.
• Quick start: ollama run llama4:scout or ollama run llama4:maverick.
• Gotchas: Community license has acceptable-use terms for some deployments.
• Official link: Meta Llama 4 overview.

4. DeepSeek-V3 (R1-distilled capable)

• What it is: 671B MoE with 37B active per token. 128k context. Strong on reasoning, cost-efficient training, supports FP8, BF16, INT4/8 inference across modern stacks.
• Best for: High-end servers needing top open performance, R1-style reasoning signals via distillation.
• License: Model license permits commercial use; code MIT.
• Quick start: Use vLLM, SGLang, LMDeploy, TRT-LLM per the model card. ollama run deepseek-v3 exists for GGUF quant runs.
• Gotchas: Transformers support is evolving; follow recommended runners.
• Official link: HF model card.

5. DeepSeek Coder V2

• What it is: A specialist model from the DeepSeek-V2 family, specifically fine-tuned for code generation, completion, and reasoning. It supports over 300 programming languages and has demonstrated state-of-the-art performance on coding benchmarks.
• Best for: Any application centered on code, from developer assistants to automated code generation and refactoring pipelines.
• License: DeepSeek Model License 2.0 (Permissive for commercial and research use).
• Quick Start: ollama run deepseek-coder-v2
• Gotchas: While exceptional at coding, its general reasoning capabilities are not as strong as the base DeepSeek-V2 model.
• Official Link: DeepSeek Coder V2 Blog Post

6. Grok-1

• What it is: The massive 314B parameter open-weight model released by xAI. It's a dense model (not MoE for the base release) that provides a powerful foundation for research and fine-tuning.
• Best for: Research and large-scale fine-tuning projects where having access to a massive, permissively licensed base model is key.
• License: Apache 2.0 (Fully permissive for commercial use).
• Quick Start: Not available on Ollama due to size. Requires a multi-GPU setup using a framework like vLLM or Hugging Face TGI.
• Gotchas: This is a base model, not an instruction-tuned one, so it requires significant fine-tuning to be useful for chat applications. Its immense size makes it impractical for all but the most well-resourced teams.
• Official Link: xAI Grok-1 Blog Post

7. Llama 3.3 (70B Instruct)

• What it is: An improved 70B instruct model in the Llama 3.x line with 128k context in community builds. Broad tool and community support.
• Best for: High-quality general assistants on a single 80 GB GPU or quantized locally.
• License: Llama Community License (3.x).
• Quick start: ollama run llama3.3:70b.
• Gotchas: Community license has use restrictions.
• Official link: Ollama library entry.

8. Command R+

• What it is: A 104B parameter model from Cohere, designed specifically for enterprise use cases like Retrieval-Augmented Generation (RAG) and tool use. It is highly optimized for multilingual performance across 10 key business languages.
• Best for: Enterprise RAG systems, complex agentic workflows with tool calling, and multilingual customer support applications.
• License: CC-BY-NC 4.0 (Non-commercial. A commercial license is available through Cohere).
• Quick Start: ollama run command-r-plus
• Gotchas: The default license is non-commercial, which is a critical limitation for many businesses. You must engage with Cohere for commercial deployment.
• Official Link: Command R+ on Hugging Face

9. Gemma 2 (27B)

• What it is: Google's second-generation open-weight model, available in 9B and 27B sizes. The 27B variant offers a compelling mix of performance and efficiency, making it suitable for deployment on a single GPU or even on-device in some scenarios.
• Best for: General-purpose tasks on resource-constrained hardware, offering a great performance-per-watt ratio. A strong choice for on-premise or on-device applications.
• License: Gemma License (Permissive for commercial use, with an acceptable use policy).
• Quick Start: ollama run gemma2:27b
• Gotchas: The 8k context window is smaller than many modern alternatives, which can be a limitation for long-document RAG.
• Official Link: Gemma 2 Announcement

10. Qwen2 (72B)

• What it is: A powerful model from Alibaba's Qwen2 family, known for its extremely long context window capabilities and strong multilingual performance. It's a formidable competitor to Llama 3.1 70B.
• Best for: Applications requiring analysis of very long documents (e.g., financial reports, legal contracts) and tasks needing robust multilingual support.
• License: Tongyi Qianwen License 2.0 (Permissive, commercial use allowed).
• Quick Start: ollama run qwen2:72b
• Gotchas: While powerful, the community and tooling ecosystem around Qwen is not as mature as that for Llama, which may mean fewer third-party integrations and tutorials.
• Official Link: Qwen2 on Hugging Face

How to Choose the Right Open-Source LLM

Selecting the best model involves a trade-off between performance, cost, and practicality. Follow this 3-step decision flow.

1. Assess Your Budget and Hardware: This is the first filter.

• Consumer GPU (e.g., 24GB VRAM): You are limited to models like DeepSeek-V2, Gemma 2 (27B), or quantized versions of 30-40B models.
• Pro/Enterprise GPU (e.g., 48-80GB VRAM): You can comfortably run 70B models like Llama 3.1 and Qwen2 72B.
• Cloud/API: If you have no local hardware, your choice is determined by your budget for API calls or managed hosting.

3. Define Your Primary Task:

• General Chat/Reasoning: Llama 3.1, Mixtral 8x22B, and DeepSeek-V2 are top contenders.
• Coding: DeepSeek Coder V2 is the specialist. Qwen2.5 is also a strong choice.
• Long-Context RAG: Qwen2 (72B) or Llama 3.1 (with its 128k window) are excellent.
• Multilingual: Qwen models have a distinct advantage.
• On-Device/Edge: Gemma 2 (9B) or smaller quantized models are the way to go.

5. Check Your License Needs:

• Maximum Flexibility (Commercial Use): Prioritize Apache 2.0 (Mixtral, Grok-1) or MIT licenses.
• Standard Commercial Use: Llama 3.1, DeepSeek, and Gemma licenses are generally safe but require reviewing their acceptable use policies.
• Research/Non-Commercial: Models with licenses like CC-BY-NC (Command R+) are only suitable for non-commercial projects.

Deployment Quick Starts

Getting started is easier than ever. Here are a few one-liners.

Ollama (Local Deployment)

Ollama is the easiest way to run models on your local machine.

# Run a powerful coding model

ollama run deepseek-coder-v2

# Run a balanced general-purpose model

ollama run llama3.1:70b

# Run a lightweight, multilingual model

ollama run qwen2.5:7b

vLLM or TGI (Server Deployment)

For production servers, frameworks like vLLM and Hugging Face's Text Generation Inference (TGI) offer high-throughput performance.

# Serve Llama 3.1 70B with vLLM (requires ~40GB+ VRAM)

pip install vllm

python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3.1-70B-Instruct

# Serve a 7B model with TGI using Docker

docker run -p 8080:80 -v $PWD/data:/data ghcr.io/huggingface/text-generation-inference:latest --model-id meta-llama/Meta-Llama-3.1-8B-Instruct

Licensing and Compliance Matrix

Understanding licenses is critical for any serious project. This is not legal advice; always consult a legal professional.

Benchmarks You Can Trust (and How to Read Them)

While no single benchmark is perfect, looking at a few in combination provides a strong signal.

• Hugging Face Open LLM Leaderboard: A widely cited benchmark that tests reasoning, common sense, and knowledge across multiple standardized tests.
• LMSys Chatbot Arena: A human-preference leaderboard where models are ranked based on anonymous, head-to-head user votes. It's excellent for gauging conversational ability.
• Artificial Analysis: Provides detailed performance and cost comparisons for models served via major API providers.
• Hallucination & Factuality Checks: Look for consensus across TruthfulQA, a hallucination leaderboard, and FActScore for long-form. For RAG, require Ragas faithfulness to pass before shipping.

How to Interpret Scores Be a critical consumer of benchmarks.

1. Look for Consensus: A model that ranks highly on both the Open LLM Leaderboard and the Chatbot Arena is likely a top performer.
2. Beware of Contamination: Some models may have been inadvertently trained on benchmark questions ("test leakage"), inflating their scores.
3. Human Preference Matters: For user-facing chatbots, the Chatbot Arena is often a better predictor of real-world performance than academic benchmarks.

FAQ

What is the difference between open weights and open source?

"Open source" typically means both the model weights and the source code (training, inference) are available under a permissive license like Apache 2.0 or MIT. "Open weights" means the model weights are public, but they might be governed by a more restrictive custom license that dictates how they can be used, modified, or monetized.

Which models run on a single 24 GB or 48 GB GPU?

• 24 GB GPU (e.g., RTX 3090/4090): You can comfortably run 4-bit quantized versions of models up to ~40B parameters. DeepSeek-V2 (21B active), Qwen2.5 (32B), and Gemma 2 (27B) are excellent choices.
• 48 GB GPU (e.g., A100 40GB, RTX 6000 Ada): You can run 4-bit quantized versions of 70B-72B models like Llama 3.1 70B and Qwen2 72B.

Which models are best for 1M context or more?

While models like Llama 3.1 now have 128k context, Qwen models have historically been leaders in very long context. Some fine-tuned versions of Qwen models have demonstrated capabilities up to 1-2 million tokens, but these often require specific inference techniques. For most use cases, the 128k context offered by top models is more than sufficient.

Can I fine-tune commercially on each license?

For permissive licenses like Apache 2.0 and MIT, yes, without issue. For custom licenses like Llama 3.1, Gemma, and DeepSeek, the answer is also yes. The main restriction is that you cannot use models with a "Non-Commercial" (NC) license, such as Command R+'s default license, to create a commercial product. Always read the full license text.