Open Weights vs Closed Models — Choosing Where AI Lives
Status: 🟩 COMPLETE 🟦 LIVING Tags: open-weights, open-source, closed-source, proprietary, llama, gpt, self-hosting, privacy
What it is
When people talk about AI models being “open” or “closed,” they’re describing who can access the model itself — the underlying mathematical weights that encode everything the model has learned.
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Open-weights models: The trained model is released publicly. Anyone can download the weights, run the model on their own hardware, modify it, fine-tune it, or build products with it. You can use it without sending data to anyone.
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Closed models / proprietary models: The model is kept private. You can only access it by sending your data to the company’s API (via the internet), paying for access, and trusting them with your data. Examples: GPT-4o (OpenAI), Claude (Anthropic), Gemini (Google).
This distinction has profound implications for privacy, cost, control, and capability.
A concrete analogy
Closed model = visiting a restaurant. You go to their location, they cook with their kitchen and their recipes (which are secret), you eat, you pay, they keep the recipe.
Open-weights model = getting the recipe. You receive the actual recipe (the model weights). You can cook it at home, modify it to your taste, serve it to others, or build a business around it. The restaurant no longer controls what you do.
Major open-weights models (mid-2026)
| Model | Creator | Country | Best for |
|---|---|---|---|
| Llama 3.3 70B / Llama 4 | Meta | 🇺🇸 | General purpose; most popular |
| Mistral 7B / Mixtral 8x7B / Mistral Large 2 | Mistral | 🇫🇷 | Quality + efficiency; European |
| Gemma 2 / Gemma 3 | 🇺🇸 | Small, efficient; on-device | |
| Phi-3 / Phi-4 | Microsoft | 🇺🇸 | Tiny but capable; mobile |
| Qwen 2.5 | Alibaba | 🇨🇳 ⛔ | Good quality — but Chinese; avoid |
| DeepSeek R2 | DeepSeek | 🇨🇳 ⛔ | Good quality — but Chinese; avoid |
| Command R+ | Cohere | 🇨🇦 | RAG-optimised; enterprise |
| Falcon 2 | TII | 🇦🇪 | Research use; UAE |
Major closed models (mid-2026)
| Model | Creator | Country | Access |
|---|---|---|---|
| GPT-4o / o3 / o4 | OpenAI | 🇺🇸 | ChatGPT; OpenAI API |
| Claude 3.5 / Claude 4 | Anthropic | 🇺🇸 | Claude.ai; Anthropic API |
| Gemini 2.5 Pro / Flash | 🇺🇸 | Gemini; Google API | |
| Grok-3 / Grok-4 | xAI | 🇺🇸 | X Premium; xAI API |
| Mistral Large 2 | Mistral | 🇫🇷 | Mistral API (also available via cloud) |
The key differences
| Dimension | Open-weights | Closed |
|---|---|---|
| Privacy | Maximum — data never leaves your machine | Data sent to provider’s servers |
| Cost | Free (you pay compute) | API usage fees |
| Capability | Catching up; approaching frontier | Currently top performance |
| Customisation | Fine-tune, modify anything | Limited to fine-tuning on provider |
| Speed | Depends on your hardware | Generally fast (optimised cloud) |
| Reliability | Your infrastructure’s reliability | Provider SLA |
| Vendor lock-in | None — you own the weights | Dependent on provider |
| Updates | You choose when to update | Provider updates (may change behaviour) |
| Chinese models | Available but risky (DeepSeek, Qwen) | Assessed per vendor |
When open-weights is clearly better
1. Maximum privacy requirements
You’re processing medical records, legal documents, financial data, or anything deeply sensitive. The information cannot leave your infrastructure under any circumstances.
Open-weights means: the AI runs on your servers, your laptop, or your cloud account. Data never touches the model company’s servers.
2. Air-gapped environments
Government, defence, or critical infrastructure environments with no internet access. The only AI you can use is the one you’ve downloaded.
3. Fine-tuning for a specific domain
You want to train the model further on your company’s documents, customer conversations, or specialised knowledge. Open-weights gives you full control over the training process.
4. Cost at very high volume
For very high-volume applications (millions of daily queries), running your own open-weights model can be significantly cheaper than paying API fees, if you have the technical expertise.
5. Regulatory requirements
Some regulations (healthcare, finance, EU GDPR in certain interpretations) may require demonstrating that data doesn’t leave specific jurisdictions or infrastructure. Self-hosted open-weights satisfies this.
When closed is clearly better
1. Capability at the frontier
For the most demanding tasks — advanced reasoning, complex creative work, cutting-edge multimodal tasks — frontier closed models (GPT-4o, Claude 3.5, Gemini Ultra) currently outperform available open-weights models, though the gap is closing.
2. Zero technical setup
Closed models via APIs are available immediately, with no infrastructure to set up. Open-weights requires servers, GPUs (or a strong CPU), and technical knowledge to deploy.
3. Small usage volume
If you’re sending a few hundred queries per day, API costs are minimal and self-hosting is overkill.
4. Specialised capabilities
Some closed models have unique features (DALL·E 3 image generation tightly integrated with GPT, Claude Artifacts, Gemini’s multimodal capabilities) that don’t have direct open equivalents.
Running open-weights models locally: the tooling
The main tools for running open-weights models locally (no GPU required for smaller models):
| Tool | What it does | Difficulty |
|---|---|---|
| Ollama | Easiest way to run models locally; one command | ⭐ Beginner |
| LM Studio | GUI for running models locally; very accessible | ⭐ Beginner |
| GPT4All | Desktop app; simple; private | ⭐ Beginner |
| vLLM | Production-grade server; high throughput | ⭐⭐⭐ Advanced |
| llama.cpp | Efficient inference; broad hardware support | ⭐⭐ Intermediate |
For Australian users with a modern laptop (8GB+ RAM): Ollama + Llama 3.2 3B or Phi-3 Mini can run locally with acceptable speed for testing.
The “open-weights” vs “open-source” distinction
These terms are often confused:
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Open-source (true): Full access to training code, training data, and model weights. Anyone can reproduce and verify everything. Very rare in practice for frontier models.
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Open-weights: Only the trained model weights are released. The training code and data may or may not be released. You can run and modify the model but can’t reproduce the training.
Most models called “open” are open-weights, not truly open-source. Meta’s Llama models, for example, release weights but not training data. This matters because:
- You can’t independently verify what the model was trained on
- You can’t retrain from scratch
- There may be biases or backdoors in the training you can’t audit
Australian government and regulatory angle
The Australian Government’s AI frameworks increasingly consider data sovereignty:
- The Digital Transformation Agency’s cloud strategy encourages keeping sensitive data in Australian or Five Eyes-nation infrastructure
- Open-weights models deployed on Australian cloud infrastructure (AWS Sydney, Azure Australia East) satisfy many data residency requirements
- Chinese open-weights models (DeepSeek, Qwen) should be treated with the same caution as Chinese closed models — see vendors-chinese-avoid
Gotchas
- “Open” doesn’t mean safe by default. Open-weights models can contain biases, produce harmful content, and require the same careful deployment practices as closed models.
- Local models require hardware. Running a 70B parameter model (Llama 3.3 70B) requires roughly 40GB of GPU VRAM or specialised CPU memory. Not every computer can do this.
- Smaller local models are less capable. A Phi-3 Mini (3.8B parameters) running on your laptop is significantly less capable than GPT-4o. Don’t expect frontier-model quality from laptop-size models.
- Fine-tuning is technical. Customising an open-weights model requires significant ML engineering expertise — not a no-code experience.
- Open weights ≠ no risk. You’re still responsible for AI outputs. Running your own open-weights model doesn’t eliminate hallucinations, bias, or harmful output risks.
See also
- llama — Meta’s flagship open-weights models
- mistral-company — European open-weights models
- vendors-chinese-avoid — why Chinese open-weights models require extra caution
- ai-energy-footprint — local models use your energy; cloud models use theirs
- privacy-and-data-training — privacy considerations for AI tools
Sources
- Touvron et al., “Llama 2: Open Foundation and Fine-Tuned Chat Models” (Meta, 2023)
- Jiang et al., “Mistral 7B” (2023)
- OSI (Open Source Initiative) definition of open source vs open weights
- Australian Digital Transformation Agency — cloud and data sovereignty policy
- Bommasani et al., “On the Opportunities and Risks of Foundation Models” (Stanford, 2021)
- IEEE Spectrum — “The Open vs Closed AI Debate” (2024)