7 best open weight AI models I've tested in 2026
What is an open weight AI model?
An open weight AI model is one whose weights are public. The model weights are the billions of trained parameters that make a large language model work, and anyone can download them.
This means you can host the model on your own infrastructure, fine-tune it however you like, and run it completely offline. What you don't get is the recipe for how the model was created. This can include the training data and the training code. And also the methods that produced those weights stay private.
Without the recipe, you can't rebuild the model from scratch or verify how it was made. Researchers call this a reproducibility problem. This is what makes open weight different from open source models, which I'll break down more in a second.
Fun fact, even OpenAI ships open weight models now. Their flagship models like GPT-5.5 are closed, but they released gpt-oss as a downloadable model in 2025. So the "open" in their name finally means something again.
Open weight LLMs vs open source LLMs
Most open source models are not actually open source. And people like to use "open weight" and "open source" interchangeably. But they are two different things.
The Open Source Initiative, the nonprofit that defines what open source means in software, put out an official definition for AI models in late 2024. For a model to count as open source, the creators have to release everything someone would need to rebuild it from scratch. That means the training code, details about the training data, and the full methodology. Not just the finished weights.
Almost no popular model clears that bar. GLM, DeepSeek, Qwen, Llama, and basically every big name you have heard of only release the weights. The handful of true open source models, like OLMo and BLOOM, come from research projects rather than companies with something to protect.
Does the difference matter for you? Honestly, probably not. If you can download a model, run it on your own hardware, and fine-tune it for your use case, you have the freedom that matters for actually building things.
7 best open weight AI models in 2026
Here are the top open weight AI models:
1. GLM-5.2
- Best for: Agentic workflows and long-horizon coding tasks
GLM-5.2 is an open weight model created by the AI lab Z.ai. It's their flagship model that is on par with frontier models like Opus 4.8, GPT-5.5, and Gemini 3.1 Pro.
It's rated as one of the top rated "open source" models and is known for its ability to tool call and reason on long-horizon tasks.
Pros and cons
Pros:
- It currently sits at the top of the Artificial Analysis Intelligence Index for open weight models, which puts it ahead of DeepSeek V4 Pro, MiniMax M3, and Kimi K2.6.
- The context window is 1 million tokens. That's a big jump from the 200k window in GLM-5.1 and enough to hold entire codebases or long research runs in a single session.
- It was built specifically for agentic work, so tool calling and multi-step tasks feel native.
- The MIT license has no regional limits or usage restrictions, so you can fine-tune it or run it on your own infrastructure without asking anyone for permission.
- API pricing is a fraction of what comparable frontier models cost.
Cons:
- The full weights are around 1.5TB, so running it locally is out of reach unless you have serious hardware. Most people will use it through an API or a platform like Gumloop.
- It's text only right now. If your workflows need image or file inputs analyzed by the model itself, you will need a multimodal option.
2. DeepSeek V4 Pro
- Best for: Complex reasoning tasks
DeepSeek V4 Pro is a highly rated open weight LLM model created by DeepSeek. A while ago, they were known as an open source model with limited capabilities.
But today, DeepSeek V4 Pro is right up there with some older frontier models like Claude Sonnet 4.6.
Pros and cons
Pros:
- It ranks first among open weight models on the GDPval-AA benchmark for real-world agentic work, ahead of Kimi K2.6 and GLM-5.1.
- The 1 million token context window handles long documents and multi-step agent runs without breaking a sweat.
- Per-token API pricing is low compared to frontier models with similar capability.
- The MIT license means you can use it commercially, fine-tune it, and self-host it with no restrictions.
Cons:
- All that step-by-step reasoning burns through output tokens, so your actual cost per task can end up higher than the cheap per-token price suggests.
- It tends to answer even when it does not know something, so I would double check its outputs on factual tasks.
- It can drop requirements on complex prompts with lots of constraints, which is where closed frontier models still hold an edge.
3. Kimi K2.7 Code
- Best for: Agentic coding and software engineering tasks
Kimi K2.7 Code is an open weight LLM model created by the team at Moonshot AI. What makes this model different is that it is a specialized mode.
Pros and cons
Pros:
- It's purpose-built for coding agents, with big reported gains over K2.6.
- It uses around 30% fewer reasoning tokens than K2.6, so agent runs cost less without giving up quality.
- Vision input is supported, so it can read screenshots, diagrams, and UI mockups inside a workflow.
Cons:
- The context window is 262k tokens, which is solid but well short of the 1 million you get with GLM-5.2 or DeepSeek V4 Pro.
- Thinking mode is always on, so quick simple tasks still pay the reasoning cost.
4. Qwen3.5 397B
- Best for: Multilingual workflows
Qwen3.5 397B is an open weight LLM created by Alibaba Group.
Pros and cons
Pros:
- It covers 201 languages and dialects, which makes it the strongest multilingual option on this list.
- Vision is built in natively, so it handles screenshots, charts, and documents without a separate model.
Cons:
- The context window is 262k tokens in the open weight version.
- Coding is good but slightly behind the top closed models and the coding-focused picks on this list.
5. DeepSeek V4 Flash
- Best for: High-volume workflows where speed matters
DeepSeek V4 Flash is an open weight model focused on speed.
Pros and cons
Pros:
- It's one of the fastest open weight models available.
- Pricing is extremely cheap, at a fraction of what V4 Pro costs per token.
Cons:
- Knowledge is where the size reduction shows most.
- Like V4 Pro, it's text only, so no images or screenshots.
6. MiniMax M3
- Best for: Multimodal workflows with images and video
MiniMax M3 is an open weight model created by MiniMax.
Pros and cons
Pros:
- It's the only model on this list that handles video input, and one of the few with native image understanding.
- Coding performance is near the frontier.
Cons:
- The hardest coding tasks still favor frontier closed models like Opus 4.8.
7. Kimi K2.6
- Best for: Multi-agent orchestration
Kimi K2.6 is an open weight model created by Moonshot AI.
Pros and cons
Pros:
- It's built for multi-agent orchestration.
- Moonshot's published results show it beating GPT-5.4 and Gemini 3.1 Pro on agentic search benchmarks.
Cons:
- The context window is 262k tokens, which is behind the 1 million you get with GLM-5.2 or the DeepSeek models.
Which open weight model should you use?
After two months of testing, here's how I would break it down:
- Go with GLM-5.2 if you want the best overall option for agentic tasks.
- DeepSeek V4 Pro is the pick for heavy reasoning workflows.
- Kimi K2.7 Code was built specifically for coding agents, so use it there.
- Qwen3.5 397B makes the most sense if your workflows touch multiple languages.
- DeepSeek V4 Flash will save you the most money on simple, high-volume automations.
- MiniMax M3 is the strongest choice for image and video understanding.
- Kimi K2.6 is the one for multi-agent workflows and generating UIs.