Open source LLMs are one of the most important AI trends of 2026.
And for good reason:
Open source models were long significantly weaker than proprietary models. But by spring 2026 they have caught up, especially out of Chinese labs:
DeepSeek V4 Pro (released April 24, 2026), GLM-5.1 from Z.ai, Kimi K2.6 from Moonshot AI, and Qwen3.5 from Alibaba can compete with the best proprietary LLMs like Claude Opus 4.7, GPT-5.5, or Gemini 3.1 Pro, and even beat them on specific benchmarks like SWE-Bench Pro and HumanEval.
In this article, you'll find an overview of the 50 best open source LLMs as of April 2026 with their key benchmark scores and licenses.
Additionally, I'll show you how to easily and freely use open LLMs on your own computer (without needing to program or use the terminal).
- DeepSeek V4 Pro (1.6T MoE, MIT, April 2026), Kimi K2.6 (1T MoE), and GLM-5.1 from Z.ai lead the April 2026 rankings, with GLM-5.1 topping SWE-Bench Pro at 58.4%
- 50 open source LLMs available with various licenses, from MIT and Apache 2.0 through to restricted research-only licenses
- Chinese labs (DeepSeek, Moonshot AI, Z.ai, Alibaba) hold most top positions; the 2025 leaders (GPT-OSS-120B, DeepSeek R1, Qwen3-235B, Llama 4) are still solid but no longer at the top
- Local usage possible with tools like Ollama, LM Studio, or GPT4All, but the new top models need serious hardware (multi-GPU or quantized variants for consumer rigs)
Open Source LLMs Compared
Benchmark score color coding:
1. Key Benchmarks Explained
To objectively compare open source LLMs, I use three central benchmark categories:
MMLU / MMLU-Pro: The Massive Multitask Language Understanding Benchmark tests general knowledge across 57 subjects (STEM, social sciences, humanities). MMLU-Pro is the more challenging variant with less contamination. Top models score 85-90% here.
MATH / GPQA: These benchmarks test mathematical and scientific reasoning. MATH-500 contains challenging math problems, while GPQA (Graduate-Level Physics Questions Answers) tests expert knowledge in biology, physics, and chemistry. Top models score 70-97% here.
HumanEval / LiveCodeBench: These benchmarks test code generation. HumanEval contains Python programming tasks, LiveCodeBench tests code performance with current, uncontaminated tasks. Top models score 60-90% here.
The table shows three benchmark scores for each model, which vary depending on the model's strengths (e.g., code-focused models have higher HumanEval scores).
2. Top Models of April 2026
DeepSeek V4 Pro (released April 24, 2026) is the new leader. The 1.6 trillion parameter MoE activates only 49B per token, scores 87.5% on MMLU-Pro, 90.1% on GPQA Diamond, and 93.5% on LiveCodeBench. Same MIT license as the rest of the DeepSeek lineup, and it ships with native 1M-token context at roughly 27% of the inference FLOPs of V3.2.
Kimi K2.6 from Moonshot AI is the second-best open weight overall: 92% on HumanEval, 90.5% on GPQA Diamond, 96.4% on AIME 2026, with a 256K context window and native video input. Modified MIT license, 1T parameters MoE.
GLM-5.1 from Z.ai (formerly Zhipu) tops SWE-Bench Pro with 58.4%, beating GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%). The 754B-parameter MoE was trained entirely on Huawei Ascend chips and ships under the MIT license. The reasoning sibling, GLM-5, hits 96% on MMLU and 94% on GPQA, the highest knowledge scores in the open-source space.
Kimi K2.5 still posts the highest HumanEval score on any leaderboard (99.0) and leads on MATH-500 (98.0). It is the best open weight purely for code generation when latency matters less than peak quality.
DeepSeek V4 Flash (284B / 13B active) is the cost-efficient sibling of V4 Pro and the most practical choice when you want frontier-class quality on a single high-end GPU.
The previous generation is still very usable: GPT-OSS-120B (OpenAI's first open-weight model since GPT-2), DeepSeek R1, Qwen3-235B-A22B-Thinking, and Llama 4 Maverick all remain strong, just no longer state-of-the-art.
3. LLM Licenses Explained
Here's an overview of the most commonly used licenses for open source LLMs.
MIT License
A very permissive open source license, similar to Apache 2.0. It allows unrestricted use, modification, and distribution of the LLM, including in proprietary programs, as long as the copyright notice is retained. DeepSeek V3 uses MIT with some restrictions for military use.
Llama 2 Community / Llama 3 Community
Meta released Llama 2 and Llama 3 under these licenses. They allow free use of the LLMs for research and commercial applications with up to 700 million monthly active users. The source code and model weights are freely available.
Qwen License / Qianwen LICENSE
Qwen models are released under various licenses. While smaller models are often licensed under Apache 2.0, larger models like Qwen2.5-72B have special license terms that allow commercial use with certain restrictions.
Apache 2.0
A very permissive open source license with minimal restrictions. It allows use, modification, and distribution of the LLM, including in proprietary programs, as long as the copyright notice is retained. It contains no copyleft clause.
CC BY-NC-4.0
A Creative Commons license that allows editing and sharing the LLM in any form, but not for commercial purposes. The author's name must be credited.
CC BY-NC-SA-4.0
Similar to CC BY-NC-4.0, but with the additional Share-Alike condition. This means forks or modified versions of an LLM must be distributed under the same conditions.
Non-Commercial
Here, using the LLM for commercial purposes is prohibited. However, what exactly counts as "commercial" is not always clearly defined or delimited.
Usually, "non-commercial" models are only released for research purposes or private use.
4. Using Open Source LLMs Locally on Your Own Computer
Using open source LLMs locally on your own computer is easier than you might think:
1. Download LM Studio
Download LM Studio from the website. It's free and available for Mac, Windows, and Linux:
2. Install and Open LM Studio
Next, install LM Studio on your computer and open it.
3. Download Your Desired Open Source LLMs
Now you need to download the open source LLMs you want to use in LM Studio.
Many popular LLMs are already on the home screen. To download an LLM, simply click the blue download button:
To find specific open source LLMs, you can also use the search function:
4. Important: Check System Requirements Before Downloading
Before downloading an LLM, you should check the system requirements.
Llama 3, for example, requires more than 8 GB RAM and 4.92 GB of free storage:
5. Chat with the Open Source LLM
After downloading an open source LLM, you can use it directly in LM Studio.
Simply click on the speech bubble icon (?) in the left sidebar.
The user interface and settings options are reminiscent of the OpenAI Playground:
