# Model Management
> Retrieve detailed information about each model listed in the /model/info endpoint, including descriptions from the config.yaml file, and additional model info (e.g.
- Skill: `tools-only/model-management` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/model-management`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/model-management/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/model-management
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Model Management
Add new models + Get model info without restarting proxy.
## In Config.yaml
```yaml
model_list:
- model_name: text-davinci-003
litellm_params:
model: "text-completion-openai/text-davinci-003"
model_info:
metadata: "here's additional metadata on the model" # returned via GET /model/info
```
## Get Model Information - `/model/info`
Retrieve detailed information about each model listed in the `/model/info` endpoint, including descriptions from the `config.yaml` file, and additional model info (e.g. max tokens, cost per input token, etc.) pulled from the model_info you set and the [litellm model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). Sensitive details like API keys are excluded for security purposes.
:::tip Sync Model Data
Keep your model pricing data up to date by [syncing models from GitHub](sync_models_github.md).
:::
```bash
curl -X GET "http://0.0.0.0:4000/model/info" \
-H "accept: application/json" \
```
## Add a New Model
Add a new model to the proxy via the `/model/new` API, to add models without restarting the proxy.
```bash
curl -X POST "http://0.0.0.0:4000/model/new" \
-H "accept: application/json" \
-H "Content-Type: application/json" \
-d '{ "model_name": "azure-gpt-turbo", "litellm_params": {"model": "azure/gpt-3.5-turbo", "api_key": "os.environ/AZURE_API_KEY", "api_base": "my-azure-api-base"} }'
```
```yaml
model_list:
- model_name: gpt-3.5-turbo ### RECEIVED MODEL NAME ### `openai.chat.completions.create(model="gpt-3.5-turbo",...)`
litellm_params: # all params accepted by litellm.completion() - https://github.com/BerriAI/litellm/blob/9b46ec05b02d36d6e4fb5c32321e51e7f56e4a6e/litellm/types/router.py#L297
model: azure/gpt-turbo-small-eu ### MODEL NAME sent to `litellm.completion()` ###
api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
rpm: 6 # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm)
model_info:
my_custom_key: my_custom_value # additional model metadata
```
### Model Parameters Structure
When adding a new model, your JSON payload should conform to the following structure:
- `model_name`: The name of the new model (required).
- `litellm_params`: A dictionary containing parameters specific to the Litellm setup (required).
- `model_info`: An optional dictionary to provide additional information about the model.
Here's an example of how to structure your `ModelParams`:
```json
{
"model_name": "my_awesome_model",
"litellm_params": {
"some_parameter": "some_value",
"another_parameter": "another_value"
},
"model_info": {
"author": "Your Name",
"version": "1.0",
"description": "A brief description of the model."
}
}
```
---
Keep in mind that as both endpoints are in [BETA], you may need to visit the associated GitHub issues linked in the API descriptions to check for updates or provide feedback:
- Get Model Information: [Issue #933](https://github.com/BerriAI/litellm/issues/933)
- Add a New Model: [Issue #964](https://github.com/BerriAI/litellm/issues/964)
Feedback on the beta endpoints is valuable and helps improve the API for all users.
## Add Additional Model Information
If you want the ability to add a display name, description, and labels for models, just use `model_info:`
```yaml
model_list:
- model_name: "gpt-4"
litellm_params:
model: "gpt-4"
api_key: "os.environ/OPENAI_API_KEY"
model_info: # 👈 KEY CHANGE
my_custom_key: "my_custom_value"
```
### Usage
1. Add additional information to model
```yaml
model_list:
- model_name: "gpt-4"
litellm_params:
model: "gpt-4"
api_key: "os.environ/OPENAI_API_KEY"
model_info: # 👈 KEY CHANGE
my_custom_key: "my_custom_value"
```
2. Call with `/model/info`
Use a key with access to the model `gpt-4`.
```bash
curl -L -X GET 'http://0.0.0.0:4000/v1/model/info' \
-H 'Authorization: Bearer LITELLM_KEY' \
```
3. **Expected Response**
Returned `model_info = Your custom model_info + (if exists) LITELLM MODEL INFO`
[**How LiteLLM Model Info is found**](https://github.com/BerriAI/litellm/blob/9b46ec05b02d36d6e4fb5c32321e51e7f56e4a6e/litellm/proxy/proxy_server.py#L7460)
[Tell us how this can be improved!](https://github.com/BerriAI/litellm/issues)
```bash
{
"data": [
{
"model_name": "gpt-4",
"litellm_params": {
"model": "gpt-4"
},
"model_info": {
"id": "e889baacd17f591cce4c63639275ba5e8dc60765d6c553e6ee5a504b19e50ddc",
"db_model": false,
"my_custom_key": "my_custom_value", # 👈 CUSTOM INFO
"key": "gpt-4", # 👈 KEY in LiteLLM MODEL INFO/COST MAP - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json
"max_tokens": 4096,
"max_input_tokens": 8192,
"max_output_tokens": 4096,
"input_cost_per_token": 3e-05,
"input_cost_per_character": null,
"input_cost_per_token_above_128k_tokens": null,
"output_cost_per_token": 6e-05,
"output_cost_per_character": null,
"output_cost_per_token_above_128k_tokens": null,
"output_cost_per_character_above_128k_tokens": null,
"output_vector_size": null,
"litellm_provider": "openai",
"mode": "chat"
}
},
]
}
```