AI Model vs Algorithm: What's the Difference in 2026?

Quick Answer

• Algorithm: the recipe (e.g., stochastic gradient descent, backpropagation)
• Model: the cake (the trained network with specific weights)

The algorithm is the method; the model is the artifact.

What Do These Terms Mean?

An algorithm is a sequence of steps a computer follows. In ML specifically, it refers to the learning procedure — how weights are updated (Stanford CS229; MIT OpenCourseware).

A model is the resulting function: architecture plus learned parameters. You can run the same algorithm on different data to get different models (Google AI Glossary, 2024).

How Each Works

Algorithm

• Written once in code (e.g., AdamW optimizer)
• Consumes data, produces gradients, updates parameters
• Does not "know" anything specific until trained

Model

• Data structure: architecture (layers) + weights (numbers)
• Runs inference: input -> output
• Serializable (saved to disk as .safetensors, .ckpt, .bin)

Examples

Algorithms

1. Gradient descent
2. Backpropagation
3. Transformer architecture (also an architecture)
4. K-means clustering
5. Q-learning

Models

1. GPT-4 (weights)
2. Llama 3 70B
3. Stable Diffusion XL
4. BERT-base
5. Your fine-tuned support classifier

Algorithm vs Model

Aspect	Algorithm	Model
Tangible?	No (pure instructions)	Yes (file on disk)
Changes during training	Usually fixed	Yes — weights update
Reusable	Across datasets	Specific to one training run
Size	A few lines to a few thousand	MB to TB
Swapping	Easy	Hard (retrain)

An architecture like "Transformer" is sometimes called a model family — the combination of architecture + weights is the specific model.

When the Distinction Matters

• Research papers propose new algorithms (attention, Mixture of Experts)
• Products ship specific models (GPT-4o, Claude Sonnet 4.5)
• Licensing: algorithms are rarely licensed; model weights are (Llama 3 license, Mistral license)
• Reproducibility: publishing the algorithm is not enough — sharing weights or training data may be needed

FAQs

Is "the transformer" a model or algorithm? Architecture (a type of algorithm). Specific instances (GPT-4) are models.

Can I use GPT-4's algorithm without the weights? The transformer architecture is public; GPT-4's specific training recipe and weights are not.

Are model weights copyrightable? Legally debated; most labs treat them as proprietary regardless.

Is a model the same as an AI system? Broader — an AI system includes model + inference code + safety filters + UI.

Do open-weight models share algorithms? Usually yes — papers describe the training recipe; weights are released.

What about hyperparameters? Settings tuning the algorithm's behavior for a specific model (covered elsewhere).

Can I combine algorithms? Yes — modern training uses many: AdamW + warmup + mixed precision + gradient accumulation.

Conclusion

Algorithms are the craft; models are the artifacts. Knowing the difference clarifies licensing, reproducibility, and product discussions. More on Misar Blog.