Back at the start of 2026, open neural networks were seen as "almost as good, but worse" compared to ChatGPT and Claude. By October the situation had changed: open models from China are entering the global top five rankings, being released under free licenses, and made freely available in web chats. In this updated guide we break down what an "open model" means, which ones are the best today, and in which cases it makes sense to run them on your own hardware versus using them through the cloud.
Open-source and open-weight: what's the difference
The term "open-source" is used broadly in AI, but strictly speaking most open models are open-weight: the weights (the trained model itself) are published, and you can download it, run it, and fine-tune it. The training data and training code are usually closed. Fully open models, where everything is available, are few and far between.
For the user, the license matters more than the terminology:
- MIT and Apache 2.0 — you can do anything, including commercial use without restrictions. This is how GLM-5.2, the Qwen and DeepSeek model families, and Mistral 7B are released.
- Custom licenses with conditions — for example, Llama's: free for almost everyone, but with restrictions for very large companies and some usage prohibitions.
- Research only — some image and video generation models are released with a ban on commercial use.
Before integrating into a product, read the license of the specific version: conditions can differ within the same family. For more on the term, see the glossary.
Why open models matter
Privacy. The model runs on your machine, and data never leaves it. For medicine, law, finance, and any code under NDA, this is often the only option.
Cost. The weights are free. You only pay for hardware or GPU rental, and under heavy load this is many times cheaper than the APIs of closed models.
Independence. No limits, regional blocks, changing terms, or service shutdowns. The model you downloaded stays with you forever.
Control and fine-tuning. An open model can be fine-tuned on your own data via LoRA and fine-tuning, stripped of unwanted restrictions, and embedded into any process.
Access without a VPN. The web chats of DeepSeek, Qwen, Kimi, and Z.ai work from Russia without restrictions — unlike ChatGPT, Claude, and Gemini.
The best open models: October 2026
Language models
| Model | Developer | License | Best for |
|---|---|---|---|
| DeepSeek V4 | DeepSeek | open | general purpose, math, code |
| Qwen 3.7 | Alibaba | Apache 2.0 | Russian language, a wide range of sizes |
| GLM-5.2 / GLM-5.3 | Zhipu AI | MIT | code, agents, minimal refusals |
| Kimi K2.6 | Moonshot AI | open | long documents, agentic tasks |
| Llama 4 Maverick | Meta | Llama | ecosystem, local deployment |
| MiniMax M3 | MiniMax | open | general tasks |
| Mistral 7B | Mistral AI | Apache 2.0 | weak hardware |
The big picture: the flagships of the open world are models with hundreds of billions of parameters that are comparable in quality to closed models of the middle and upper tier. For a home computer, most families have compact versions with 7–30 billion parameters.
For Russian, Qwen performs best. For code — GLM and DeepSeek. For long context — Kimi. See current versions and specs in the language model catalog.
Image generation
| Model | Developer | Features |
|---|---|---|
| FLUX 2 Dev | Black Forest Labs | the best quality among open models, 12–24 GB VRAM |
| Qwen-Image 2.0 | Alibaba | strong at text in images, free web chat |
| HunyuanImage 3.0 | Tencent | photorealism |
| SDXL | Stability AI | a huge ecosystem of fine-tunes and extensions |
To run them, use ComfyUI or Automatic1111, not tools for language models. An overview of all options is in the image model catalog.
Video
Open video generation models lag behind closed ones (Veo 3.1, Kling 3.0) more than in other categories. Compact options like Motif-Video 2B are suitable for experiments, but for work tasks cloud services are still the more sensible choice for now.
Speech
Whisper Large from OpenAI remains the standard for speech recognition: open, accurate, supports Russian, and runs even on weak graphics cards. For speech synthesis, open models are catching up to commercial ones, but leaders like ElevenLabs are still noticeably ahead in naturalness.
How to try for free without installing anything
An open model doesn't obligate you to run it. The simplest options:
- Official web chats. DeepSeek, Qwen Chat, Kimi, and Z.ai offer free access to their flagships without limits — they are also the best free alternatives to ChatGPT.
- API aggregators. Services like OpenRouter provide access to dozens of open models through a single key, some of them free. Convenient for developers to compare models in their own app.
- Weight hosting. On Hugging Face, many models have demo pages where you can talk to the model right in the browser.
When to run it yourself
Local deployment is justified if at least one condition is met: data cannot be sent outside; the load is so heavy that the API costs more than hardware; you need to work without internet; fine-tuning or non-standard settings are required. In all other cases, the cloud is simpler and often better quality — the largest versions of the models are available there.
If you've decided to run it, we have a detailed guide: how to run a neural network on your computer — Ollama, choosing a model for your video memory size, quantization, and common problems. In short: 8 GB of VRAM is enough for 7–8B models, 16 GB opens up the 14B class, 24 GB — 30B; flagships need server cards. You can calculate more precisely in the VRAM calculator, and compare the economics of local versus cloud in the article local vs cloud models.
Limitations of open models
In fairness — where open models still fall short:
- The most complex tasks. On tasks requiring long reasoning and rare knowledge, closed flagships (Claude Opus 5.5, GPT-6.1 Sol, Gemini 4 Argon) are still ahead.
- Multimodality. Video analysis and video generation are noticeably weaker.
- Support. There's no support service or guarantees; when problems arise, you figure them out yourself or with the community.
- Hardware. To get flagship quality, you need a flagship server. Compact versions on a home PC are at the level of closed models from a year ago.
Conclusion
Open neural networks in 2026 are not a compromise but a full-fledged choice. For most users they're interesting even without local deployment: the free chats of DeepSeek and Qwen handle everyday tasks no worse than paid subscriptions and work without a VPN. For companies, open models are a path to privacy and cost control. And for enthusiasts — the opportunity to have their own neural network that isn't going anywhere.
New open models come out every two to three weeks, and many of them become the new standard within a month. We cover every significant release in our channel: what the model is, how it's better than the previous one, and what hardware it runs on.