8 Best Open Source LLM in 2026

The best open source LLM in 2026 is DeepSeek V4 for overall capability under a full MIT license, with Qwen3.8-27B as the best pick for a single workstation. I compared 8 open-weight models on license terms, size, context window, and BenchLM’s October 2026 leaderboard.

My short answer: DeepSeek V4 for the strongest permissive license, GLM-5.3 for the highest verified benchmark score, Qwen3.8-27B and Gemma 4 for local use, and Kimi K3 for cluster-scale agents. If you want a hosted assistant instead of model weights, start with our best AI chatbot guide.

How I Chose the Best Open Source LLMs

I ranked 8 models on 5 criteria: license freedom, benchmark score, parameter size, context window, and how easy the weights are to run. License came first because a model you cannot legally ship in a product is not useful to most teams.

Benchmark scores come from the BenchLM open-source leaderboard, last updated October 16, 2026. License, size, and context data come from the vendors’ release notes and an October 2026 comparison by Wavect.

This is a review of published specs and a public leaderboard, not my own hands-on benchmark. Rankings shift monthly, so treat every score as a snapshot.

Quick Comparison of the Best Open Source LLMs

This table maps each model to its developer, size, license, and context window so you can filter by the constraint that matters most.

Model	Developer	Size	License	Context window	Best for
DeepSeek V4 Pro / Flash	DeepSeek	About 1.7T / 304B	MIT	1M tokens	Best overall
GLM-5.3	Z.ai	753B	GLM-5.3 License	1M tokens	Highest verified score (67.9)
Qwen3.8-27B	Alibaba	27B dense	Apache 2.0	262,144 tokens	Single workstation
Gemma 4 31B / 26B MoE	Google	31B dense / 26B (3.8B active)	Apache 2.0	256,000 tokens	Small and permissive
Kimi K3	Moonshot	2.8T (104B active)	Kimi K3 License	1M tokens	Cluster-scale agents
gpt-oss-120b / 20b	OpenAI	117B / 21B	Apache 2.0	128,000 tokens	Reasoning on modest hardware
Muse Glimmer-30B	Meta	About 29.6B dense	Apache 2.0	131,072+ tokens	Local agent workflows
Llama 4 Scout / Maverick	Meta	109B / 400B (17B active)	Llama 4 Community License	10M / 1M tokens	Very long context

1. DeepSeek V4: Best Open Source LLM Overall

DeepSeek V4 is the best open source LLM overall because it pairs frontier-class scores with a plain MIT license and a 1M-token context window. The family ships as V4-Flash-0731 at about 304B parameters and V4-Pro-0813 at about 1.7T.

BenchLM scores V4 Pro 0813 at 63.2, and the MIT license places no extra conditions on commercial use. Sources list the Pro size between 1.6T and 1.7T, so confirm the exact count on the model card.

The catch is hardware. Both tiers target multi-GPU servers, not a laptop. See our DeepSeek alternatives guide if you want something lighter.

License: MIT.

2. GLM-5.3: Highest Benchmark Score With a Published License

GLM-5.3 from Z.ai is the top-scoring open model I could pair with a published license, at 67.9 on BenchLM. It has 753B parameters and a 1M-token context window.

It is permissive, but it ships under its own GLM-5.3 License, not MIT. Earlier GLM releases were described as MIT, so check the license text before you build on it.

BenchLM also ranks GLM-5.2 at 60.5 and GLM-5.3-Flash at 58.5 if you want a smaller step down.

License: GLM-5.3 License (custom).

3. Qwen3.8-27B: Best for a Single Workstation

Qwen3.8-27B from Alibaba is the best open source LLM for one machine, because a 27B dense model with an Apache 2.0 license scores 59.6 on BenchLM. Its native context window is 262,144 tokens.

Weights alone take about 14 GB at 4-bit quantization (27B parameters at roughly half a byte each). Add memory for the context cache, which grows with prompt length.

The 1M-token extension through YaRN is possible, but a longer window is not proof of reliable accuracy at that length. The much larger Qwen3.8-2.4T-A95B uses a separate Qwen3.8-Max License with conditions for very large products.

License: Apache 2.0.

4. Gemma 4: Best Small Open Source LLM From Google

Gemma 4 is the best small open source LLM family because Google released it on April 2, 2026 under Apache 2.0 for the first time. Earlier Gemma versions used a custom license with usage restrictions.

The family has 4 sizes: E2B, E4B, a 26B mixture-of-experts model with 3.8B active parameters, and a 31B dense model. The 26B and 31B variants have a 256,000-token context window, and the 31B fits on a single 80GB H100 unquantized.

BenchLM scores the 26B MoE at 45.6 and the 31B at 39.9, so Gemma 4 trails Qwen3.8-27B on that board. It wins on license clarity and size options.

License: Apache 2.0.

5. Kimi K3: Best for Cluster-Scale Agent Work

Kimi K3 from Moonshot is the best open model for agentic coding and tool use if you run a GPU cluster. It has 2.8T total parameters, 104B active, and a 1M-token window.

Its Kimi K3 License adds conditions for large model-as-a-service businesses and very large commercial products. Internal use is exempt.

I did not find K3 on BenchLM’s board; its predecessor Kimi K2.6 scores 58.4. Treat K3 as a vendor-reported option until independent scores appear.

License: Kimi K3 License (custom).

6. gpt-oss: Best Open Source LLM From OpenAI

gpt-oss is the best pick for reasoning on modest hardware from a US lab, because both sizes use Apache 2.0. gpt-oss-120b has about 117B parameters and gpt-oss-20b about 21B, per OpenAI’s release.

BenchLM scores gpt-oss-120b at 37.3 and gpt-oss-20b at 33.3. Newer Chinese and Google releases now score higher, so this is a license-and-compatibility choice, not a performance one.

License: Apache 2.0.

7. Muse Glimmer-30B: Best Apache 2.0 Model From Meta

Meta’s Muse Glimmer-30B is the best Meta model for local agents because it is a dense 29.6B model under Apache 2.0. It launched in August 2026 with a context window of 131,072 tokens or more.

It is separate from Llama 4 and carries a cleaner license. BenchLM scores it at 41.2.

Plan the quantization, vision encoder, and KV cache memory together, because they all compete for the same VRAM.

License: Apache 2.0.

8. Llama 4: Best for Very Long Context

Llama 4 from Meta is the best open model for very long context, since Llama 4 Scout supports a 10M-token window. Scout has 109B total and 17B active parameters, and Maverick has 400B total and 17B active.

Both use the Llama 4 Community License, which restricts multimodal use for EU-domiciled developers (end users are excepted). BenchLM scores Scout at 28.2 and Maverick at 21.6, well behind this year’s newer releases.

Pick it for context length and ecosystem support, not raw score. Compare it in Llama 4 vs GPT-4o or browse Llama alternatives.

License: Llama 4 Community License.

Open Source vs Open Weight: What the License Lets You Do

Most “open source” LLMs are open-weight: you can download and run the weights, but the training data and full recipe stay private. The license decides whether you can use them commercially.

Licenses fall into 3 tiers:

1. Permissive (MIT, Apache 2.0): DeepSeek V4, Qwen3.8-27B, Gemma 4, gpt-oss, and Muse Glimmer-30B. Use them in products with no usage conditions beyond attribution.
2. Custom with conditions: Kimi K3, GLM-5.3, and Qwen3.8-Max. Read the text if you run a large commercial service.
3. Community license with restrictions: Llama 4, with its EU multimodal clause.

Mistral’s open models are another family worth checking; see Llama vs Mistral for how the two compare.

How to Run an Open Source LLM on Your Own Hardware

Run an open source LLM in 3 steps: pick a model that fits your memory, download it through a runner, and chat locally. Ollama and LM Studio are the two common runners, and both are free.

Ollama is MIT-licensed, works from the command line and a local API, and runs on macOS, Windows, and Linux. LM Studio offers a graphical model browser and a chat window, but it is proprietary software that is free to use.

Download weights from Hugging Face or the runner’s built-in library. Match model size to memory: a 27B model at 4-bit needs about 14 GB for weights, and a 120B model needs a server-class GPU.

Which Open Source LLM Fits Which Job?

Match the model to the constraint: license for products, size for hardware, and context for long documents. Here are 5 common jobs and the pick for each:

1. Commercial product with no license worry: DeepSeek V4 or Qwen3.8-27B (MIT or Apache 2.0).
2. One consumer or workstation GPU: Qwen3.8-27B or Gemma 4 26B MoE.
3. Highest verified benchmark score: GLM-5.3, after reading its license.
4. Million-token documents: DeepSeek V4 (1M) or Llama 4 Scout (10M).
5. Agentic coding on a cluster: Kimi K3, and see our best AI chatbot for coding roundup for hosted options.

BenchLM’s overall leader in October 2026 is Xiaomi’s MiMo-V2.6-Pro at 73.6. I could not verify its license, so it is not in my picks.

Frequently Asked Questions (FAQ)

What is the best open source LLM?

DeepSeek V4 is the best open source LLM overall in 2026 because of its MIT license, 1M-token window, and frontier-class scores. Qwen3.8-27B is the best for a single machine.

What is the best open source LLM to run locally?

Qwen3.8-27B and Gemma 4 are the best local options. Both use Apache 2.0, and a 27B model needs about 14 GB for weights at 4-bit.

Is Llama still the best open source LLM?

No. BenchLM scores Llama 4 Scout at 28.2, below Qwen3.8-27B (59.6) and GLM-5.3 (67.9). Llama 4 still leads on context length with a 10M-token window.

Which open source LLM has the most permissive license?

DeepSeek V4 uses MIT, and Qwen3.8-27B, Gemma 4, gpt-oss, and Muse Glimmer-30B use Apache 2.0. All five allow commercial use without usage-based conditions.

Is an open source LLM free to use?

The weights are free to download, but running them costs hardware or cloud compute. Hosted APIs for the same models charge per token.

Can open source LLMs match ChatGPT?

The top open models now score in the same range as many hosted models on public leaderboards, but results vary by task. Compare them directly with your own prompts, and read DeepSeek vs ChatGPT for one example.

Final Verdict

DeepSeek V4 is the best open source LLM in 2026 because it combines frontier scores, a 1M-token window, and an MIT license. Qwen3.8-27B and Gemma 4 are the best choices when the model must fit on one machine.

Check the license first, then match size to your hardware, and re-check the BenchLM board before you commit. For hosted assistants, return to our best AI chatbot guide or the AI Comparison homepage.