Open-Source vs Closed-Source LLMs. What should you actually use?

[SIEmon]

If you’re debating whether you should choose an open-source or a closed-source large language model (LLM), this article can help you solve the dilemma. We’ll go through the questions you need to ask yourself (or your team) before making this call.

For now, let me state their advantages briefly. Frontier closed models are still ahead when it comes to the “max” reasoning. Open-weight models, on the other hand, are cheaper, more private, and superb at high volume.

TL;DR: For most agent developers, the answer to this dilemma is “both”. Use a closed frontier model for hard reasoning and open-weight models you host yourself for high-volume, repetitive work, such as embeddings, reranking, extraction, and small-model generation.

Also, before we start, it’s important to note that "open-source" is mostly the wrong label. Almost every model people call open-source is actually open-weight, which comes with a different set of rules.

"Open-Source" vs. "Open-Weight"​

What is even meant by “open-source”? That you can download it? Rebuild it from scratch?

Well, those are two different claims.

The Open Source Initiative (OSI) published the Open Source AI Definition (OSAID) 1.0 in October 2024. According to it, to qualify as open source, a model must ship its weights plus the training code plus enough information about the training data for someone to reproduce it, all under a license that allows use, study, modification, and sharing for any purpose. Strict, right?

Term	What You Get	Examples (September 2026)
Open-Source	Weights, training code, and data information. You can rebuild and audit.	OLMo, Pythia, T5
Open-Weight	Downloadable weights and a license, but not the training data or full recipe.	Qwen3, DeepSeek V4, Llama 4, gpt-oss, Mistral, Gemma
Closed / Proprietary	An API. No weights, no download.	Claude, GPT, Gemini

Production-wise, the license matters a lot. It varies, depending on the open-weight model:

• 
  Permissive (Apache 2.0 or MIT): Qwen3 and OpenAI's gpt-oss are Apache 2.0; DeepSeek V4 is MIT. These are genuinely easy to use commercially.
  
  
• 
  Custom "community" licenses: Meta ships Llama's weights under Llama Community License, which adds extra conditions (for example, limits that apply to very large companies).
  
  
• 
  Non-commercial or conditional: Some releases prohibit commercial use outright or add obligations once you ship a product.
  
  

Tip: Read the license before you focus on the benchmark scores. A model you can’t legally deploy is not a candidate, regardless of how high it ranks.

Blurry Questions​

Most “open vs. closed” discussions come down to two basic questions:

1. Can you get the weights? Open-weight: yes. Closed: no, API only.
2. Who runs the GPUs? A managed API vs. your own infrastructure.

You can run an open-weight model through a hosted API (DeepSeek's own or providers like Together and Fireworks) without self-hosting anything. A closed model, however, is only ever available through someone else’s API.

Closed-Source: Best for Frontier Reasoning and Zero Ops​

So, when does closed still come out on top? Well, the truth is, more often than open-source, even if you or I may not like it.

Closed frontier models remain the top tier of the quality curve for hard, multi-step reasoning. Plus, there are no GPUs to manage, no model servers, and no capacity planning to worry about. Tokens in = tokens out.

Closed is the better pick when:

• The task needs frontier reasoning, and open models fail your evaluation.
• Your volume is small, new, or spiky, so a dedicated GPU would spend most of the time idle.
• You need managed image, audio, or real-time capabilities.
• You want the newest models the day they ship, without having to validate them.

Open-Weight: Best for Cost, Control, and Privacy​

Open-weight models have become remarkably capable, and the licensing on the best ones is rather friendly. Oh, and FYI, Superlinked keeps a catalog of open models you can serve.

Here are some situations when open-weight models are the better pick:

• Prompts and documents have to stay inside your cloud or network (this is usually the case in fields like finance, healthcare, legal, or government).
• Sustained, high-volume inference keeps a GPU busy enough so that you can justify owning it.
• You want to pin a specific model version and stop depending on one vendor.
• You are running high-volume, repetitive tasks, such as classification, extraction, routing, or first-pass drafting.

It’s important to note that "open-weight" doesn’t mean "you can casually self-host it." The strongest open models are enormous. For instance, a 1.6-trillion-parameter MoE model needs a multi-GPU cluster, not a VPS. Sorry.

What you can realistically self-host on one GPU is the smaller stuff (gpt-oss-20b in roughly 16GB, or small Qwen3 variants). That’s exactly what suits an agent's supporting tasks.

Per-Token vs Per-Hour Math​

A closed API bills you per token, whereas a self-hosted GPU bills you per hour, whether it’s busy or not. Deciding which one is cheaper comes down to utilization:

	Closed API	Self-Hosted Open-Weight
Billing Unit	Per token	Per GPU-hour
Cost at Low Volume	Very cheap	Expensive (mostly idle)
Cost at High, Steady Volume	Grows without limit	Flat and predictable
Data Boundary	Leaves your network	Stays in your network
Ops Burden	None (vendor owns it)	Yours (or a managed provider's)

A Decision Checklist for Agent Developers​

Run your workload through this checklist:

• Does the task need frontier reasoning? Yes: Go for closed. No: An open-weight model probably clears the bar, so test it.
• Must the data stay in your network? Yes: Self-host an open-weight model. No: Either works.
• Is volume high and steady or low and spiky? High and steady: Self-hosting gets cheaper. Low and spiky: Stay on an API.
• Can you legally ship it? Check the model’s license to confirm.
• Do you have the hardware? A small model fits on one GPU. A frontier MoE model needs a cluster or a rented endpoint.
• Who owns operations? If you cannot staff GPU ops, use a hosted or managed option.

Notice how the answers don’t all point in the same direction?

If You Land on Open, You Still Have to Run It​

Deciding to use open-weight models is the easy part. Running them in production is where the real work starts.

Superlinked's SIE (Superlinked Inference Engine) can help you out here. It works as an inference server for those chunky open-weight models and runs:

• Encoders
• Rerankers
• Extractors
• Small self-hosted generation models

All behind one API, on your own hardware, and with no per-token bill.

So, let’s go back to our dilemma from the introduction. Should you choose an open-weight or a frontier closed model? Pick the one that works for you depending on the task. Route accordingly. There’s your answer. (ノ◕ヮ◕)ノ

FAQ​

Are open-source and open-weight LLMs the same thing?​

Open-weight means you can download the weights, while open source (by the OSI definition) also requires training code and data information needed to reproduce the model.

Is Llama open source?​

Nope. Meta releases Llama's weights under a custom community license with conditions, so OSI and the Free Software Foundation classify it as open-weight.

Which is better for building AI agents, open or closed?​

For most agents, a hybrid stack is the way to go. You use a closed frontier model for hard reasoning, and you opt for open-weight models that you host for high-volume embeddings, reranking, extraction, and small-model generation.

Can I run a top open-weight model on a single GPU?​

Usually not. The strongest models are trillion-parameter MoE systems that need a cluster, and a lot of kilowatts