Best Open Source LLMs Ranked (Updated Monthly)
The definitive ranking of open-source large language models in 2026. We compare Llama, DeepSeek, Qwen, Mistral, and more with benchmarks and real-world testing.
Every few months, I find myself in the same situation: a new open-source model drops, benchmarks look incredible, and I have to figure out whether it’s actually better than what I’m already running. After doing this dance a dozen times, I decided to keep a running list.
This is that list—a monthly-updated ranking of the best open source LLMs you can actually run and use. No paywalled models. No cloud-only options. Just genuinely accessible AI that you can download, customize, and deploy however you want.
As of January 2026, the open-source AI landscape has never been more competitive. Let me show you which models are worth your time.
How We Rank Open Source LLMs
Before diving into the rankings, let me explain what I’m actually measuring. Benchmarks matter, but they’re not everything.
Our Evaluation Criteria
1. Benchmark Performance (40% weight)
- MMLU (general knowledge across 57 subjects)
- HumanEval (code generation quality)
- GPQA (graduate-level reasoning)
- MT-Bench (conversation quality)
2. Real-World Usability (30% weight)
- Response quality in actual use
- Instruction following accuracy
- Consistency across prompts
3. Accessibility (20% weight)
- Hardware requirements
- Download size
- Ease of running locally
- Quantization options available
4. Ecosystem & Support (10% weight)
- Documentation quality
- Community size
- Framework integration (Ollama, LangChain, etc.)
I update these rankings monthly as new models release and existing ones improve. The AI landscape moves fast—what’s best today might be second-best next month. See our AI benchmarks explained guide for details on how these evaluations work. You can also check the Hugging Face Open LLM Leaderboard for current benchmark scores.
Top 10 Open Source LLMs Ranked (January 2026)
#1: Llama 4 Maverick
The new king of open-source AI.
Meta’s Llama 4 Maverick, released in early 2026, represents a genuine leap forward. With 400 billion total parameters in a Mixture-of-Experts configuration, native multimodal capabilities, and a 1-million-token context window, it’s the most capable open-source model available.
| Spec | Details |
|---|---|
| Parameters | 400B (MoE) |
| Context Window | 1M tokens |
| Languages | 200+ |
| Multimodal | Yes (text + images) |
| MMLU Score | 89.5 |
| HumanEval | 91.2 |
Why it’s #1: Maverick matches or exceeds GPT-4 Turbo and Claude 3.5 Sonnet on most benchmarks while being completely open source. The multimodal capability—understanding both text and images—makes it incredibly versatile.
The catch: Running it locally requires serious hardware. You’ll need multiple high-end GPUs or cloud resources. For most personal use, Llama 3.3 70B is more practical.
Best for: Enterprise deployment, research, applications needing maximum capability.
#2: Llama 3.3 70B
The practical choice for serious work.
If Maverick is the supercar, Llama 3.3 70B is the reliable sports sedan everyone can actually drive. It delivers exceptional performance at a fraction of the resource requirements.
| Spec | Details |
|---|---|
| Parameters | 70B |
| Context Window | 128K tokens |
| Languages | 8+ |
| MMLU Score | 86.0 |
| HumanEval | 88.4 |
Why it’s #2: Llama 3.3 70B achieves performance comparable to the much larger 405B model while running on accessible hardware. With quantization, you can run it on a consumer GPU with 24GB VRAM.
Real-world experience: This is my daily driver. For writing, coding, and general assistance, it handles 95% of what I throw at it without needing cloud resources.
Best for: Developer workstations, serious hobbyists, production applications where cloud isn’t an option. Want to customize it further? Check our fine-tuning Llama tutorial.
#3: DeepSeek V3
The reasoning powerhouse.
DeepSeek V3, from the Chinese AI company, has been making waves since its late 2025 release. It punches well above its weight class, particularly on reasoning and coding tasks.
| Spec | Details |
|---|---|
| Parameters | 671B (MoE, 37B active) |
| Context Window | 128K tokens |
| MMLU Score | 88.5 |
| HumanEval | 89.0 |
Why it’s #3: The Mixture-of-Experts architecture means only 37B parameters are active at once, making it surprisingly efficient for its capability level. On reasoning benchmarks, it sometimes beats Llama 4.
What sets it apart: DeepSeek was trained with a fraction of the compute budget of its competitors, showing what’s possible with efficient training methods.
Best for: Reasoning-heavy tasks, code generation, mathematical problems.
#4: Qwen 3 72B
The multilingual powerhouse.
Alibaba’s Qwen 3 deserves more attention in Western markets. It’s exceptionally strong at multilingual tasks and offers excellent performance at a 72B parameter size.
| Spec | Details |
|---|---|
| Parameters | 72B |
| Context Window | 128K tokens |
| Languages | 12+ (excellent non-English support) |
| MMLU Score | 85.5 |
| HumanEval | 86.3 |
Why it’s #4: If you need strong performance in Chinese, Japanese, Korean, or other Asian languages, Qwen is often the best choice. It’s also excellent at structured data tasks and mathematical reasoning.
Best for: Multilingual applications, Asian language support, math-heavy use cases.
#5: Mistral Medium 3
Maximum efficiency.
Mistral AI’s latest release prioritizes efficiency without sacrificing too much capability. If you’re resource-constrained, this is your model.
| Spec | Details |
|---|---|
| Architecture | MoE |
| Context Window | 32K tokens |
| MMLU Score | 84.2 |
| HumanEval | 85.1 |
Why it’s #5: Mistral claims it delivers 90% of top-tier performance at significantly lower cost. In my testing, that’s roughly accurate. It’s noticeably faster than dense models of equivalent quality.
Best for: Real-time applications, chatbots, scenarios where latency matters.
#6: Mixtral 8x22B
The original Mixture-of-Experts success story.
Before everyone was doing MoE, Mixtral showed that it could work exceptionally well for open-source models.
| Spec | Details |
|---|---|
| Parameters | 176B (8x22B MoE) |
| Active | ~44B per forward pass |
| Context Window | 64K tokens |
| MMLU Score | 82.5 |
Best for: Balanced performance-to-resource ratio, long-context applications.
#7: Qwen 2.5 72B
The math specialist.
While Qwen 3 is newer, Qwen 2.5 still excels in specific areas, particularly mathematical reasoning.
| Spec | Details |
|---|---|
| Parameters | 72B |
| Context Window | 128K tokens |
| MATH Benchmark | 83.1 (exceptional) |
Best for: Mathematical applications, structured data, precise reasoning.
#8: Gemma 2 27B
Google’s efficient contribution.
Google’s Gemma 2 27B is smaller but remarkably capable for its size. It’s perfect when you need something that runs on limited hardware.
| Spec | Details |
|---|---|
| Parameters | 27B |
| Context Window | 8K tokens |
| Hardware | Runs on 16GB VRAM |
Best for: Laptop deployment, edge devices, resource-constrained environments.
#9: Phi-3 Medium
Microsoft’s efficient model.
Microsoft’s contribution to open-source AI focuses on doing more with less. Phi-3 Medium is surprisingly capable despite modest parameters.
| Spec | Details |
|---|---|
| Parameters | 14B |
| Context Window | 128K tokens |
Best for: Embedded applications, quick prototyping, scenarios where speed matters more than maximum quality.
#10: Yi-34B
Strong Chinese language support.
01.AI’s Yi-34B rounds out our top 10 with excellent Chinese language capabilities and solid English performance.
| Spec | Details |
|---|---|
| Parameters | 34B |
| Context Window | 200K tokens |
Best for: Chinese language applications, extended context needs.
Best Models by Use Case
Not sure which to pick? Here’s a quick guide based on what you’re trying to do:
Best for Coding
- Llama 4 Maverick - Maximum capability
- DeepSeek V3 - Excellent code generation
- Llama 3.3 70B - Practical and powerful
Look for high HumanEval scores. These models consistently generate working code on the first attempt.
Best for General Chat
- Llama 3.3 70B - Excellent conversation quality
- Qwen 3 - Great instruction following
- Mistral Medium 3 - Fast responses
For chat applications, instruction-tuned versions are essential. Look for “Instruct” in the model name.
Best for RAG Applications
- Llama 4 Scout - 10M context window (!)
- Qwen 3 - Strong retrieval integration
- Llama 3.3 - Reliable at long contexts
Context window size matters for RAG. Llama 4 Scout’s 10-million-token context is game-changing for document-heavy applications.
Best for Limited Resources
- Gemma 2 27B - Runs on consumer GPUs
- Phi-3 Medium - Tiny but capable
- Llama 3.2 8B - Smallest Llama with good quality
If you only have 16GB VRAM or less, these models deliver the best quality-to-resource ratio.
Best for Multilingual
- Qwen 3 - 12+ languages, excellent Asian support
- Llama 4 Maverick - 200+ languages
- Mistral - Strong European language support
Choose based on your language priorities. Qwen dominates Asian languages; Mistral is excellent for European languages.
How to Run These Models Locally
Getting started is easier than you might think. Here’s the quick path.
Using Ollama (Easiest Method)
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Run Llama 3.3 70B
ollama run llama3.3
# Run Qwen 3
ollama run qwen2.5:72b
# Run Mixtral
ollama run mixtralOllama handles downloading, quantization, and inference. It’s the easiest way to get started.
Hardware Requirements
| Model | RAM | VRAM | Storage |
|---|---|---|---|
| 8B models | 8GB | 4GB+ | 5GB |
| 27B models | 16GB | 16GB | 15GB |
| 70B models | 32GB | 24GB+ | 40GB |
| 400B+ models | 128GB+ | Multi-GPU | 200GB+ |
Pro tip: Quantized versions (like Q4_K_M) reduce these requirements by 50% with minimal quality loss.
For detailed setup instructions, see our complete Ollama guide.
Frequently Asked Questions
Which open source LLM is best overall?
For most users, Llama 3.3 70B offers the best balance of performance and accessibility. If you have the hardware, Llama 4 Maverick is technically superior.
Are open source LLMs as good as ChatGPT?
The gap has closed significantly. Llama 4 and DeepSeek V3 match or exceed GPT-4 on many benchmarks. For most tasks, you won’t notice a quality difference.
Can I use open source LLMs commercially?
Most yes, with conditions. Llama requires a license for apps with 700M+ users. Mistral and Qwen have Apache 2.0 licenses. Always check the specific license.
How often do you update these rankings?
Monthly, or when a significant new model releases. This page was last updated January 2026.
What’s coming next?
Llama 4 Behemoth (the largest Llama ever) is expected in 2026. Mistral Large 3 is in development. The open-source pace shows no signs of slowing.
Conclusion
Open-source AI has never been stronger. Whether you need maximum capability (Llama 4 Maverick), practical everyday use (Llama 3.3 70B), or specialized performance (DeepSeek, Qwen, Mistral), there’s an excellent option for you.
The models I’ve ranked here are all genuinely usable today. They’re not research projects—they’re production-ready AI you can run on your own hardware, customize for your needs, and deploy without cloud dependencies or per-token fees.
My recommendation for most people: start with Llama 3.3 70B. It’s the sweet spot of capability, accessibility, and ecosystem support. You can always scale up later.
Rankings updated: January 2026. Check back monthly for the latest.
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