Decoding Model Types and How They Are Licensed

Disclaimer: I create this content entirely on my own time, and the views expressed here are mine alone (not my employer’s). Because I love leveraging new tech, I use AI tools like Gemini, NotebookLM, Claude, Perplexity and others as a “digital team” to help research and polish these articles so I can share the best possible insights with you!

The rapid expansion of artificial intelligence has created an overwhelming marketplace of large language models. But choosing the right model for a project isn’t just about comparing benchmarks or parameter counts. Developers and enterprises must navigate two critical dimensions: technical capability (how smart it is) and legal licensing (how you are allowed to use it).

Part 1: The Three Tiers of AI Model Types

AI models are generally categorized into three tiers based on their size, compute budget, and reasoning capabilities.

1. Frontier Tiers (The Bleeding Edge)

“Frontier” is a term used by researchers and policymakers to describe the absolute state-of-the-art AI systems available at the current moment. These are general-purpose models trained on massive, multi-million-dollar computing budgets.

• Capabilities: Advanced multi-step reasoning, complex tool-use, deep coding architecture planning, and emergent problem-solving behaviors.
• Examples: OpenAI’s o1/o1-pro, Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and Meta’s massive Llama 3.1 405B.

2. Workhorse / Flagship Tiers (The Production Standards)

These are highly optimized models designed to handle 90% of daily commercial workflows. They offer an exceptional balance between high intelligence, fast processing speeds, and low per-token costs.

• Capabilities: Structured data extraction (JSON parsing), customer support routing, drafting articles, and standard web development tasks.
• Examples: OpenAI’s gpt-4o-mini, Anthropic’s Claude 3.5 Haiku, and DeepSeek’s chat models.

3. Edge / Local Tiers (The Lightweight Contenders)

Small models (typically ranging from 1B to 8B parameters) designed to run efficiently on local developer laptops, mobile devices, or single corporate server graphics cards without cloud network latency.

• Capabilities: On-device privacy filtering, text summarization, rapid auto-completion, and edge computing.
• Examples: Meta’s Llama 3-8B, Google’s Gemma 2-2B, and Microsoft’s Phi-3.

Part 2: The Licensing Maze: “Open” vs. Closed

Understanding access and legality is where most engineering teams run into friction. The industry has a bad habit of “openwashing”—calling a model open when it actually carries massive legal restrictions.

To clear this up, AI models fall into three distinct legal buckets:

[ Proprietary / Closed ] ────> Hosted Cloud API Only (No Weight Access)
[ Open Weights / Source Available ] ─> Downloadable, but has commercial restrictions
[ True Open-Source AI ] ─────> Free to use, study, modify, and share unconditionally

1. Proprietary / Closed Source

In this model, the vendor keeps the raw weights (the internal brain parameters) strictly locked behind closed doors. You are completely blocked from downloading, modifying, or inspecting the model. You can only interact with it by paying to send data through a cloud API.

• The Deal: You get bleeding-edge frontier performance instantly, but you have no local data privacy control, you are locked into their cloud hosting infrastructure, and the vendor can change or deprecate the model at any time.
• Examples: OpenAI (gpt-4o), Anthropic (Claude), Amazon (Nova).

2. Open Source: Open Weights / “Source Available”

This is the tier most people mistakenly call “open-source.” In an open-weight model, the creator publishes the final trained parameters online. Anyone can download the model file for free and run it on their own servers or local home labs, guaranteeing complete data privacy.

• The Catch: The license it ships with is heavily restricted. Because traditional software licenses do not gracefully map to neural network parameters, companies write custom agreements.
• The Restrictions: For example, Meta’s Llama license blocks platforms with over 700 million active users from using it without a separate custom contract, and completely bars usage in certain heavily regulated geographic zones for multimodal tasks. Therefore, the Open Source Initiative (OSI) classifies these as “source-available” rather than open-source.

3. True Open-Source AI (Compliance with OSAID 1.0)

To combat openwashing, the Open Source Initiative (OSI) established the canonical Open Source AI Definition (OSAID). For an AI system to be considered legally open source, the creator must grant users the unconditional freedom to use, study, modify, and share the model for any purpose without asking permission.

To comply, the creator must release:

1. The Inference Code (to run the system).
2. The Training Code (to inspect how it was built).
3. The Data Provenance (a detailed description of the training data characteristics, collection methods, and filtering pipeline, allowing users to understand how outputs were shaped).

• The Licenses: These are usually published under standard permissive software licenses like Apache 2.0 or MIT. ( also see Choose a License site)
• Examples: Models like Allen Institute for AI’s OLMo, EleutherAI’s Pythia, or Google’s T5 have been verified as fully compliant with true open-source AI standards.

Summary Cheat Sheet

License Type	Can I download the weights?	Commercial restrictions?	Who controls data privacy?	Best Use Case
Proprietary	No	Yes (Paid API tiers)	The Cloud Provider	Cutting-edge apps where engineering speed beats raw hosting control.
Open Weights	Yes	Yes (Based on user volume/region)	You (On your hardware)	Enterprise workflows scaling past token boundaries with strict data privacy.
True Open-Source	Yes	No (Completely permissive)	You (On your hardware)	Academic research, fully sovereign corporate infrastructure, and unconstrained modification.

To better visualize how a single API layer can seamlessly bridge the gap between these proprietary cloud giants and open-weight models in your code stack, check out this excellent OpenRouter Structural Architecture Walkthrough. This video explains how developers use unified API layers to instantly hot-swap between closed frontier engines and open architectures with a single line of configuration.