What Are Open AI Models and Why Do They Matter?

Quick Answer

Open AI models are neural networks with publicly available weights that you can download, run on your own hardware, and modify as you like. Unlike proprietary services like ChatGPT or Claude API, open models give you privacy, cost savings at scale, and freedom from vendor lock-in. Here's what you actually need to know.

What Does "Open" Actually Mean in AI?

When people say a model is "open," they mean the trained weights—the numerical parameters that make it work—are publicly available. Anyone can download them, run them, inspect how they work, and modify them for their needs.

This is fundamentally different from proprietary models like ChatGPT or Claude API, where the weights stay locked behind Anthropic's or OpenAI's servers. You never see them. You only interact via API endpoints, following their rules and paying per token.

Open doesn't necessarily mean free or open-source code. Some open models like Meta's Llama have restricted licenses. But the key principle is the same: the weights are downloadable and runnable by anyone.

The Major Open Model Families

Llama (Meta)

Meta's Llama series is arguably the most popular open model family right now. Llama 3.1 comes in 8B, 70B, and 405B parameter sizes. The 8B version is small enough for most laptops; the 70B needs serious hardware or cloud inference. Widely used for fine-tuning and experimentation.

Qwen (Alibaba)

Alibaba's Qwen models, particularly Qwen2.5, compete directly with Llama on performance and are especially strong with non-English languages and coding tasks. Available in multiple sizes from 0.5B to 72B parameters.

DeepSeek

DeepSeek models are known for excellent reasoning and code capability at lower computational cost. Their MoE (mixture-of-experts) approach is efficient and gaining traction for deployment.

Mistral

Mistral's models (7B, Small, Medium, Large) are optimised for efficiency without sacrificing too much capability. Popular for edge deployment and organisations wanting lightweight options.

Gemma (Google)

Google's Gemma is smaller and lighter than some competitors but still competitive. Good choice for resource-constrained environments.

Open vs. Proprietary: The Real Differences

Privacy

With open models, your data never leaves your machine or your company's infrastructure. With ChatGPT or Claude API, you're sending prompts to servers you don't control. For sensitive work, open is the only honest choice.

Cost

APIs charge per token: Claude costs around £0.003 per 1K input tokens, GPT-4 is even higher. Run a 70B model on your own VPS or local hardware and you pay once for compute, not per query. At scale, this is dramatically cheaper.

Freedom and Control

With proprietary models, you're at the mercy of policy changes. OpenAI or Anthropic can change pricing, disable features, or alter terms. With open models, you control deployment, fine-tuning, and everything else.

Experimentation

Want to fine-tune a model on your domain-specific data? Quantize it to run on edge devices? Change the system prompt programmatically? Open models let you. Proprietary APIs typically don't.

Why Open Models Matter Right Now

For the past two years, proprietary models held a large capability lead. That gap has compressed dramatically. Llama 3.1, Qwen2.5, and others now compete with or beat closed models on many benchmarks. The infrastructure for running them has matured—tools like Ollama make it trivial.

Open models are no longer "good enough for hobbyists." They're genuinely production-ready for real work. That shifts the equation entirely.

Making Open Models Actually Practical: Ollama

The biggest friction point used to be setup. Getting a model downloaded, quantized, and running with an API endpoint could take hours of wrestling with PyTorch and GGUF formats.

Ollama solves this. Download the app, run ollama run llama2, and you have a local API endpoint five minutes later. It handles quantization, caching, and GPU acceleration automatically. For most people experimenting with open models, Ollama is the entry point.

The Tulip Approach

Many people and teams want the benefits of open models—privacy, control, no vendor lock-in—without managing hardware themselves. That's where Tulip comes in. Tulip runs all the leading open models on our infrastructure so you get the openness and control without the ops burden. You can run agents, handle inference, and scale without thinking about GPUs or cloud instances.

When Open Models Aren't the Right Call

Open models are powerful, but they're not universal. If you need the absolute cutting-edge reasoning or multimodal capability only GPT-4o or Claude 3.5 offers, closed models still win. And if your use case is pure API convenience with no privacy concerns, the simplicity of ChatGPT or similar might outweigh the cost.

But for teams building agents, handling sensitive data, or wanting to optimise costs, open is increasingly the pragmatic choice.

FAQ

Can I use open models commercially?

Yes, but check the license. Llama is free for commercial use under its license. Some other models have restrictions. Always read the specific license terms for the model you're using.

Do open models require technical expertise?

Tools like Ollama remove most of the technical barrier. If you can download an app and run a command, you can use open models. More advanced use cases (fine-tuning, custom deployment) do require more skills.

How much hardware do I need?

For small models like Llama 8B or Mistral 7B, a modern laptop with 16GB RAM works. For larger models like Llama 70B, you need a dedicated GPU with 24GB+ VRAM, or cloud inference. Tulip handles this complexity for you.

Are open models as good as ChatGPT?

On many tasks, yes—especially code, reasoning, and multilingual work. On others, like image generation or extremely advanced reasoning, closed models still lead. It depends on your specific use case.

What's the catch?

Open models need more computational resources to run locally than calling an API. They can require tuning for optimal results. And the ecosystem is more fragmented—more options to choose from, more to figure out yourself. But if you value privacy and control, the tradeoff is worth it.

Can I fine-tune open models?

Yes, and that's one of their biggest advantages. Fine-tune Llama or Qwen on your own data and get a model tailored to your domain. This is nearly impossible with closed models.