Llama vs DeepSeek

Two families of open-weight models

Llama and DeepSeek are both families of AI models whose weights you can download. Llama comes from Meta in the United States; DeepSeek comes from the Chinese company of the same name. Both publish model cards and weights on Hugging Face, both use mixture-of-experts designs in their newest models, and both are offered by many cloud providers. The differences are in size, age, licensing, reasoning modes and what each family is best at.

This comparison looks at the newest generation of each as of October 2026: Llama 4 Scout and Maverick against DeepSeek V4. Our site runs Llama only; DeepSeek models are not available in our chat.

Llama vs DeepSeek at a glance

Prices are OpenRouter list price, Oct 2026, for the original April releases of V4 Pro and V4 Flash.

Models and design

Both families use a mixture-of-experts design, in which only part of the model works on each token. That keeps answers fast while the total model stays large. Llama 4 Maverick and Scout each use 17 billion active parameters. DeepSeek's model card describes DeepSeek-V4-Pro with 1.6 trillion parameters and 49 billion activated, and DeepSeek-V4-Flash with 284 billion parameters and 13 billion activated, both supporting one million tokens of context.

DeepSeek V4 also offers three reasoning modes: a fast non-thinking mode for routine tasks, Think High for careful analysis, and Think Max, which pushes reasoning as far as it goes. Llama 4 answers directly without a separate thinking phase. For multi-step math, logic and complex code, a reasoning mode often helps, at the cost of slower and longer responses.

DeepSeek has since released further versions, including DeepSeek V4.1 Flash in September 2026, which accepts text and images.

Freshness and quality

DeepSeek V4 is about a year newer than Llama 4, and its later updates are newer still. Llama 4's training data ends in August 2024. For difficult reasoning and coding, DeepSeek V4 with thinking enabled is generally the stronger choice. Llama 4 is simpler to use for everyday chat, reads images natively in both main models, and has a long track record in apps and tools. Neither model searches the web by itself.

Running them yourself

Hardware is where the two differ most. Llama 4 Scout is the most accessible of these models: Meta notes it fits on a single H100 GPU with Int4 quantization, and Ollama's download is about 67 GB. Llama 4 Maverick is a 245 GB download in Ollama. DeepSeek V4 Pro, at 1.6 trillion parameters, is a data-center model. V4 Flash is smaller but still far larger than Scout in total parameters. DeepSeek's card also recommends a context window of at least 384K tokens for its Think Max mode, which needs a lot of memory.

For laptops, neither family's newest flagship is practical; smaller models such as Llama 3.1 8B are the usual choice. See run Llama locally.

Licensing

DeepSeek V4's repository and weights are released under the MIT License, one of the most permissive open-source licenses: few conditions beyond keeping the copyright notice. Llama 4 uses Meta's Llama 4 Community License, which allows commercial use but adds conditions: the acceptable use policy, "Built with Llama" attribution, the 700 million monthly active user threshold and a restriction on multimodal models for companies based in the EU. Our license guide explains these. If licensing simplicity matters to your company, DeepSeek has the edge.

Cost

Through APIs both are inexpensive compared with closed frontier models. Llama 4 Scout at $0.10 input and $0.30 output per million tokens and DeepSeek V4 Flash at $0.091 and $0.182 are both very low; Maverick at $0.1875 and $0.6525 sits below V4 Pro at $0.4427 and $0.8853 (OpenRouter list price, Oct 2026). Reasoning modes produce extra thinking tokens, so a DeepSeek answer with Think Max can cost more than its list price suggests.

How to decide in practice

Test both on a handful of your real tasks. For DeepSeek, try each task in non-thinking mode first and switch on thinking only where answers fall short, since reasoning adds time and tokens. For Llama, try Maverick for quality and Scout for cost. If you plan to self-host, check the hardware each model needs before anything else; that alone often decides the matter.

Choose Llama if…

• you want image understanding in the main models;
• you need a model you can self-host on a single high-end GPU, such as Scout;
• you prefer quick, direct answers for everyday tasks;
• you want a free online chat right now, without signing up.

Chat with Llama 4 Maverick

Choose DeepSeek if…

• you need step-by-step reasoning for hard math, logic or code;
• you want the simplest possible license (MIT);
• you want a newer model with more recent training data;
• you have data-center hardware or use DeepSeek through an API.

Also see Llama vs Qwen and Llama vs Mistral for other open-weight families, or Llama history for Meta's timeline.

FAQ

Is DeepSeek better than Llama?

For hard reasoning and coding, DeepSeek V4 is newer and generally stronger. Llama 4 reads images in both main models, is easier to self-host in Scout form, and is simpler for quick everyday answers.

Are both open source?

DeepSeek V4 uses the MIT License. Llama's weights are open, but its community license adds conditions, so it is usually called open-weight.

Can I use DeepSeek in the Llama.Online chat?

No. Our chat runs only Llama models. DeepSeek is not available here.

Which is cheaper through an API?

The smallest options are close: Llama 4 Scout and DeepSeek V4 Flash both cost a fraction of a dollar per million tokens. Maverick is listed below V4 Pro.