Key takeaways
- Open models release weights you can run and fine-tune; proprietary models stay behind APIs.
- Choose closed models for frontier capability and open ones for privacy, control and cost at scale.
- Llama 3 is open-weight with a custom licence, not fully open source with training data.
- Open source, open weights and open access differ, so read licences rather than labels.
- For local use and RAG, mid-size quantized Qwen, Llama, Gemma or gpt-oss models work well.
Open-source or open-weight LLMs publish their model weights so anyone can download, run and fine-tune them, while proprietary LLMs such as ChatGPT's models are accessed only through the provider's apps and APIs. The choice affects cost, privacy, control and capability. I build with both, using Groq-hosted Llama in DocuLens AI and multi-provider fallback in RAG.NextUpgrad, so this page covers the differences, choosing, ChatGPT versus open models, Llama 3's openness, open source versus open access, and the best open models.
What is the primary difference between open source and proprietary LLMs?
Access to the model itself. Open models release weights you can download, inspect, run on your own hardware and fine-tune. Proprietary models stay on the provider's servers, used only through APIs or apps under their terms.
This difference drives everything else: open models give control over data, deployment, customisation and long-term availability, while proprietary models offer frontier capability, managed infrastructure and less operational work. Licences vary widely among open models, from permissive Apache 2.0 and MIT to custom licences with restrictions. Proprietary models can change or be retired by the provider, while open weights you have downloaded keep working. Many teams use both for different tasks.
Closed vs open source LLMs: which should you choose?
Choose closed models when you need the strongest reasoning, multimodal features or fastest time to market with minimal infrastructure. Choose open models when privacy, data residency, cost at scale, customisation or offline use matter most.
Hybrid setups are common: route complex queries to a frontier API and routine tasks, such as classification or extraction, to an open model you host or rent. Evaluate on your own tasks, measuring quality, latency and cost per request, since benchmark rankings rarely match specific workloads. Consider operational skills: self-hosting needs GPU management and monitoring. Keep your application provider-agnostic, so switching between closed and open models stays easy.
ChatGPT vs open source LLMs: how do they compare?
ChatGPT is a polished product with frontier models, tools, memory and multimodal features, ready to use. Open-source LLMs are models you run or host yourself, through apps such as Ollama, LM Studio or Open WebUI, with full control but more setup.
For general assistance, ChatGPT typically leads in capability and convenience. Open models have closed much of the gap for many tasks and win on privacy, offline use, cost control and customisation through fine-tuning. Developers building products often combine them: open models for high-volume or sensitive tasks, frontier APIs for the hardest cases. Students can learn more about how models work by running open ones locally, inspecting tokens, prompts and outputs directly.
Is Llama 3 fully open source?
Not fully. Meta releases Llama 3 weights, the tokenizer and inference code under the Llama community licence, which permits most uses with conditions. It does not release the full training data or complete training code, and the licence is not OSI-approved.
Also asked as: is llama3 fully open-source, including tokenizer, transformers, and other components needed to build a custom llm
In practice you have what you need to run, fine-tune and deploy Llama 3: the model architecture is a standard transformer supported by Hugging Face Transformers, the tokenizer is included, and many tools support it. To build your own model from scratch, you would need training data and infrastructure that Meta does not provide. For fully open models with data and code, look at projects such as AI2's OLMo.
Open source vs open access: what is the difference?
Open source means code, and for AI ideally weights, data and training code, released under licences that allow use, modification and redistribution. Open access means content is free to read, such as research papers, without necessarily granting rights to modify or reuse it.
In AI, an important middle term is open weights: models whose weights are downloadable, but whose licences may restrict use or whose training data is undisclosed. The Open Source Initiative published an Open Source AI Definition in 2024 requiring sufficient information about data, plus code and weights under open terms. Many models called open source are really open-weight. Check the actual licence before commercial use, rather than relying on labels.
What is the best open source AI LLM?
Leading open-weight families in 2026 include Qwen, DeepSeek, Llama, Mistral, Gemma and OpenAI's gpt-oss, with the strongest large models approaching frontier proprietary performance on many benchmarks. Rankings change frequently.
Also asked as: best open source local llm
Choose by licence, size and task: Qwen and gpt-oss use Apache 2.0, DeepSeek uses MIT for many releases, while Llama and Gemma use custom terms. Large mixture-of-experts models need serious GPUs, while mid-size dense models run on a single high-memory GPU. Check leaderboards such as LMArena and task-specific benchmarks, then test on your own prompts. For coding, reasoning or multilingual needs, specialised variants often outperform general models of similar size.
What is the best small open-source LLM?
Strong small models include Qwen3 variants from under one to eight billion parameters, Gemma 3 at 1 and 4 billion, Llama 3.2 at 1 and 3 billion, Microsoft's Phi models, and SmolLM from Hugging Face.
Also asked as: best small open source llm
Small models suit phones, laptops, edge devices and fine-tuning for narrow tasks such as classification, extraction or routing. They are weaker at broad knowledge and long reasoning, so pair them with RAG for facts. Fine-tuning a small model on your task often beats prompting a large general one, at a fraction of the cost and latency. FineTune Studio demonstrates this pattern with QLoRA and base-versus-tuned comparisons. Test several on your data, since strengths differ by task.
What is the best open-source LLM to run locally?
For most laptops with 8 to 16 GB of memory, 7 to 14 billion parameter models such as Qwen3, Llama 3.1 8B, Gemma 3 and gpt-oss-20b, quantized to 4 bits through Ollama or LM Studio, offer the best balance of quality and speed.
With 24 GB or more of VRAM or a high-memory Mac, larger models around 30 billion parameters become practical. Pick instruct versions for chat, coding models for programming, and vision models for images. Quantization level matters: Q4_K_M is a good default. Measure tokens per second and answer quality on your own tasks. Local models keep data private and work offline, which suits sensitive documents and travel.
What is the best open-source LLM for RAG?
Good RAG models follow instructions closely, use long context well and cite sources faithfully. Strong open choices include Qwen instruct models, Llama 3.1 and later instruct models, Mistral models and gpt-oss, chosen by size for your hardware.
Also asked as: best open source llm for rag
For RAG, retrieval quality matters more than model size: a mid-size model with excellent hybrid retrieval and reranking often beats a larger model with weak retrieval. Test whether the model answers only from provided context, refuses when evidence is missing, and handles your languages. Some models are fine-tuned specifically for grounded answers with citations. DocuLens AI uses Groq-hosted Llama for fast document processing, an example of open weights served by a managed provider.
How do development communities differ for open and closed LLMs?
Open model communities collaborate publicly: people fine-tune, quantize, benchmark and share derivatives on Hugging Face, build tools like llama.cpp and Ollama, and discuss results openly. Closed model development happens inside companies, with outside developers using APIs.
Also asked as: difference between development communities of open source and closed source llms
Open communities move fast on efficiency, quantization, local inference and domain fine-tunes, and they can inspect models for safety and bias. Closed providers concentrate large compute and research teams, releasing frontier capabilities and polished developer platforms, with communities forming around their APIs, SDKs and forums. Both ecosystems influence each other: open research ideas spread to closed labs, and closed model techniques inspire open releases. Developers benefit by participating in both.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
