Open Source AI Models You Can Use for Free

The best open source AI models you can use for free in 2026 are OpenAI’s gpt-oss, Google’s Gemma 4, Alibaba’s Qwen 3.x family, DeepSeek V4 and Meta’s Llama models. You can download their weights at no cost and run them on your own computer with a free tool like Ollama, so there is no subscription and no per-token bill. The trade-off is hardware: small models run on an ordinary laptop, while the largest need a serious GPU or a paid cloud host.

This guide explains which open models are worth your time, what each license actually lets you do, how much hardware you need, and when a free open model really saves money compared with a cheap paid API.

Open source vs open weights: what “free” really means

Most AI models called “open source” are technically open weights. The weights are the trained numbers that make the model work. When a company publishes them, anyone can download and run the model. True open source would also include the training data and code, which almost no major lab releases.

For most readers this distinction matters less than the license, because the license decides what you can legally do:

• Apache 2.0 and MIT: permissive. You can use the model commercially, modify it, fine-tune it and ship it in a product, as long as you keep the license notice.
• Custom community licenses (such as Llama’s): free to use, but with conditions such as acceptable use rules and limits for very large companies. The Open Source Initiative disputes calling these “open source”.

The best open source AI models in 2026

Here is a quick comparison of the main open model families you can download today.

1. gpt-oss: best all-rounder from OpenAI

OpenAI released gpt-oss on 5 August 2025, its first open-weight language models since GPT-2. There are two sizes. gpt-oss-120b has 117 billion parameters but only activates 5.1 billion per token, and it runs on a single 80 GB GPU. gpt-oss-20b has 21 billion parameters with 3.6 billion active and runs on devices with 16 GB of memory. Both support a 128K token context window and use the Apache 2.0 license.

The “active parameters” detail matters. These are mixture-of-experts (MoE) models: only a slice of the network works on each word, which makes them faster and cheaper to run than their total size suggests. On Ollama, gpt-oss:20b is a 14 GB download and gpt-oss:120b is 65 GB.

Pick it if you want a capable general assistant that runs well on a good laptop or desktop with 16 GB or more of memory.

2. Gemma 4: best for laptops and small devices

Google DeepMind released Gemma 4 on 2 April 2026 and, for the first time, switched Gemma to the standard Apache 2.0 license. The family includes small “edge” models (E2B and E4B) built for phones and small boards, a 26B mixture-of-experts model with about 3.8 billion active parameters, and a 31B dense model. The larger models support a 256K context window, the edge models 128K.

All Gemma 4 variants accept text and images, and the small edge models also accept audio. On Ollama, the default gemma4 tag is roughly a 6.6 to 9.5 GB download, which makes it one of the easiest strong models to try. Run ollama run gemma4 and you are chatting in minutes.

Pick it if you have an ordinary laptop, want image understanding, or need a clean commercial license from a major company.

3. Qwen 3.x: best for coding and many languages

Alibaba’s Qwen family is the widest range of open models available. Ollama’s library lists Qwen3 from 0.6B (523 MB) up to 235B (142 GB), Qwen3.5 multimodal models, Qwen3.6 models built for agentic coding, and dedicated Qwen coder models. The first open Qwen3.6 model, Qwen3.6-35B-A3B, was released on 17 April 2026 under Apache 2.0. Note that Alibaba keeps its top models, Qwen3.6-Plus and Max, as hosted services only.

Pick it if you need a coding helper, strong multilingual output, or a very small model for a low-end machine.

4. DeepSeek V4: best quality if you have server hardware

DeepSeek published its V4 models with open weights under the MIT license, one of the most permissive licenses there is. They are huge: V4-Flash has 284 billion parameters and V4-Pro about 1.6 trillion. That puts them out of reach for laptops; you need a multi-GPU server or cloud cluster. Most individuals use DeepSeek through its very cheap API instead, which we cover in our DeepSeek API pricing guide. For privacy trade-offs of the hosted app, read DeepSeek vs ChatGPT.

For a smaller DeepSeek option, Ollama still lists the deepseek-r1 reasoning models from 1.5B up to 671B.

Pick it if you are a company with GPU servers and want near-frontier quality with no license fees.

5. Llama: popular, but read the license

Meta’s Llama 4 (Scout and Maverick) arrived in April 2025 under the Llama 4 Community License. Llama has huge community support and many fine-tuned versions, and smaller Llama 3.x models such as llama3.2:3b (a 2 GB download) remain popular for light local use. However, the Llama license carries acceptable use rules and has historically restricted the very largest companies, and Meta launched a separate model, Muse Spark, in April 2026 to power its own chatbots. If you are building a product, Apache 2.0 or MIT models are simpler choices.

How to run open source AI models for free

You have three main ways to use these models without paying a subscription.

1. Run locally with Ollama. Install Ollama from its website, open a terminal and type ollama run followed by a model name, such as gemma4 or gpt-oss:20b. Local use is free and open source, and Ollama states it does not see your prompts or data when you run locally. Our step-by-step guide to running an LLM locally with Ollama walks through every command.
2. Use a desktop app. Tools like LM Studio give you a point-and-click interface for downloading and chatting with open models, if you prefer not to use a terminal.
3. Use a free cloud tier. Ollama’s free cloud plan includes starter credits and starter models with one concurrent request, useful when your laptop is too small for a big model. If you need more, Ollama Pro costs $20 a month (or $200 a year) with $60 of usage credits.

How much hardware do you need?

The main constraint is memory: RAM on a regular computer, or unified memory on a Mac, or VRAM on a graphics card. As a rough guide, you need more free memory than the model’s download size, plus extra room for longer conversations. Here are real download sizes from the Ollama library:

Ollama supports NVIDIA GPUs (compute capability 5.0 and newer), AMD GPUs through ROCm on Linux, Apple Silicon through Metal, and Vulkan on Windows and Linux. Without a supported GPU, models still run on the CPU, just slower. Also note that Ollama’s default context window is 4,096 tokens; raise it if you paste long documents. Our explainer on context windows covers why.

Are free open models actually cheaper than paid AI?

Not always. This is the part most “free AI” articles skip. Budget API models have become extremely cheap, so the savings from going open depend on your volume.

Worked example: a small business assistant

Imagine you process 2 million input tokens and 500,000 output tokens a month, roughly a few hundred customer emails summarized and answered. Compare the costs:

• GPT-6 Luna API ($0.10 input, $0.50 output per 1M): $0.20 + $0.25 = $0.45 a month.
• Gemini 3.8 Flash API ($0.75 input, $3.75 output per 1M, through 2026): $1.50 + $1.88 = about $3.38 a month.
• Claude Sonnet 5.5 API ($2 input, $10 output per 1M): $4 + $5 = $9 a month.
• Local gpt-oss-20b or Gemma 4: $0 in fees on a computer you already own, plus electricity.

At this volume, buying a new GPU just to save $0.45 a month makes no sense. Open models start to win when volume is very high (tens or hundreds of millions of tokens), when data must never leave your premises, or when you need the model to work offline. Our guide to picking the cheapest AI model for each task and the OpenAI API pricing rate card help you run this math for your own workload.

Who should use open source AI models (and who should not)

If you simply want a free chatbot with no setup, the free tiers of ChatGPT, Gemini and Claude are easier. See our roundups of the best free AI tools and free alternatives to ChatGPT Plus. If privacy is your main reason for going open, read our business guide to AI data privacy too.

Frequently asked questions

Next steps: try one open model this week

Start small. Install Ollama, run Gemma 4 or gpt-oss-20b, and give it a real task from your work, such as summarizing a report or drafting replies. If it handles the job well, you have a free, private assistant. If it struggles, keep it for simple tasks and use a paid model for the rest. For more ways to cut your AI spend, see our list of 12 ways to save money on AI subscriptions. Model releases, licenses and cloud pricing change often, so check each vendor’s official page, such as the gpt-oss announcement and Ollama pricing, before you commit.