Model parameters vs. API parameters
Two unrelated things go by the name "parameters", and people looking for one routinely land on the other. The short version: a parameter count tells you how big a model is, and this site doesn't track it. API parameters are what you put in the request body, and that's the entire catalog.
Parameter count: the weights
A model's parameter count is how many trained weights it has. GPT-3 shipped with 175 billion of them. Llama 3.1 405B puts the number in its name. It's a rough proxy for how expensive a model is to run, and not much else — a good 70B model from this year will beat a 175B model from 2020 on almost any task you care about.
For most of the models people ask about, the number isn't public. OpenAI has never published a count for GPT-3.5 or anything since. Neither has Anthropic for Claude, or Google for Gemini. Figures that circulate for those models are estimates and leaks. Open-weight models are the exception: if you can download the weights you can count them, which is why Llama, Qwen, DeepSeek and Mistral all state exact numbers in their model cards.
API parameters: the request settings
These are the fields you send when you call a model: temperature,
top_p,
max_tokens,
stop, reasoning effort,
and whatever else a given endpoint accepts. They change what comes back for a given
prompt. They don't change the model.
Every provider names and gates them differently. OpenAI's temperature range isn't Google's. Some parameters only apply once another one is set, and some quietly stop working on reasoning models. That's the mess this catalog documents: 242 models under one schema, with the type, default, valid range, and gating conditions for each field.
Which one are you after?
The request settings
Defaults, ranges and gating conditions for every tracked model.
The weight count
We don't have it. Check the model card on Hugging Face for open-weight models, or the provider's own announcement post. For closed models, expect to find nothing official.
Frequently asked questions
- Does modelparams.dev list how many parameters a model has?
- No. 242 models are in the catalog and none of them carry a weight count. We track API parameters, meaning the settings you send in a request. For an open-weight model the count is usually in its Hugging Face model card. For a closed model there is often no published figure at all.
- How many parameters does GPT-3.5 have?
- OpenAI has never published it. The 175 billion figure people repeat belongs to GPT-3, which is a different model, and every number quoted for GPT-3.5 and later is an estimate rather than a disclosure.
- Are model parameters and hyperparameters the same thing?
- No, and that is a third meaning of the word. Hyperparameters are the settings used to train a model, like learning rate and batch size. Parameters are the weights that training produces. API parameters are what you send at inference time. Same word, three unrelated things.
- Is temperature a model parameter?
- Temperature is an API parameter. It changes how the output is sampled and is not stored in the model. Two requests to the same model at different temperatures run against identical weights.