Open-Weight vs Closed LLMs: GPT-5, Gemini, Llama, DeepSeek
Open-weight vs closed LLMs explained simply — GPT-5, Gemini, Llama, and DeepSeek compared on cost, privacy, and control for Indian users.
Open-Weight vs Closed LLMs: GPT-5, Gemini, Llama, DeepSeek
If you're building anything with AI in India, this is the first fork in the road: use a closed model through an API, or run an open-weight model yourself? The choice affects your bill, your data privacy, and how much you can customise. Let's break it down with GPT-5, Gemini, Llama, and DeepSeek as the examples.
What does open-weight vs closed actually mean?
- Closed LLM — you can't download the model. You call it over an API and the provider runs it (e.g. GPT-5, Gemini).
- Open-weight LLM — the trained model weights are published, so you can download and run them yourself (e.g. Llama, DeepSeek).
- Open-source — a stricter term meaning weights and training details are both open; many 'open' models are actually only open-weight.
- Self-hosting — running an open-weight model on your own hardware or rented GPU.
How do GPT-5, Gemini, Llama, and DeepSeek compare?
| Model | Type | Runs where | Best at | Data leaves your control? |
|---|---|---|---|---|
| GPT-5 | Closed | Cloud API only | General reasoning, tools | Yes (sent to provider) |
| Gemini | Closed | Cloud API only | Search, Google ecosystem | Yes (sent to provider) |
| Llama | Open-weight | Cloud or self-host | Customising, on-prem apps | No, if self-hosted |
| DeepSeek | Open-weight | Cloud or self-host | Cost-efficient reasoning/code | No, if self-hosted |
Which is cheaper for an Indian business or developer?
Pros
- Closed APIs have zero upfront cost — pay per use
- No GPU or DevOps team needed
- Open-weight models are free to download and run
- Self-hosting can be far cheaper at high, steady volume
Cons
- Closed API bills grow with every request — costly at scale
- Self-hosting needs GPU rental (₹ thousands/month) + setup skill
- Open-weight needs you to handle updates and uptime
- Idle self-hosted GPUs still cost money
When should I choose open-weight over closed?
- Data privacy — sensitive data (health, finance, legal) that can't leave your servers.
- Customisation — you need to fine-tune the model on your own domain or language data.
- Predictable cost — high steady volume where a fixed GPU bill beats per-call pricing.
- Offline / on-prem — apps that must run inside your own network or a local device.
# Closed LLM: a few lines, provider runs everything.
response = client.chat(model="gpt-5", messages=[{"role": "user", "content": "Summarise this invoice in Hindi."}])
print(response.text)
# Open-weight (self-host) needs more setup first:
# 1) Rent a GPU server.
# 2) Download the model weights (Llama / DeepSeek).
# 3) Serve it with a runtime, then call your own endpoint.Is my data safe with each option?
Frequently asked questions
Is open-weight the same as open-source?
Not exactly. Open-weight means the model weights are downloadable, but the training data may not be public. True open-source means weights and training details are both open. Many popular 'open' models are only open-weight.
Are Llama and DeepSeek free to use?
The weights are free to download and run under their licences, so there's no per-call fee if you self-host. But you pay for the hardware or GPU rental and the engineering time to run them reliably.
Which is better for a startup in India?
Start with a closed API like GPT-5 or Gemini to build and validate fast with no infrastructure. Move to a self-hosted open-weight model later if data privacy, customisation, or cost at scale demands it.
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AICreatorHub Team
The AICreatorHub editorial team is a group of hands-on AI practitioners, writers and developers based in India. We test AI tools and models ourselves, track official releases from OpenAI, Anthropic, Google, Meta and xAI, and translate them into simple, India-first guides in English and Hindi. Every article is written for real Indian use cases — pricing in rupees, free-tier tips and practical, tested steps — so you get accurate, up-to-date and genuinely useful AI information.