---
title: "Drop Unsupported Params"
description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/drop-params
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:24:04.115Z
license: CC-BY-4.0
attribution: "Drop Unsupported Params — Claudary (https://claudary.paisolsolutions.com/skills/drop-params)"
---

# Drop Unsupported Params
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

## Overview

import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Drop Unsupported Params 

Drop unsupported OpenAI params by your LLM Provider.

## Default Behavior

**By default, LiteLLM raises an exception** if you send a parameter to a model that doesn't support it. 

For example, if you send `temperature=0.2` to a model that doesn't support the `temperature` parameter, LiteLLM will raise an exception.

**When `drop_params=True` is set**, LiteLLM will drop the unsupported parameter instead of raising an exception. This allows your code to work seamlessly across different providers without having to customize parameters for each one.

## Quick Start 

```python 
import litellm 
import os 

# set keys 
os.environ["COHERE_API_KEY"] = "co-.."

litellm.drop_params = True # 👈 KEY CHANGE

response = litellm.completion(
                model="command-r",
                messages=[{"role": "user", "content": "Hey, how's it going?"}],
                response_format={"key": "value"},
            )
```


LiteLLM maps all supported openai params by provider + model (e.g. function calling is supported by anthropic on bedrock but not titan). 

See `litellm.get_supported_openai_params("command-r")` [**Code**](https://github.com/BerriAI/litellm/blob/main/litellm/utils.py#L3584)

If a provider/model doesn't support a particular param, you can drop it. 

## OpenAI Proxy Usage

```yaml
litellm_settings:
    drop_params: true
```

## Pass drop_params in `completion(..)`

Just drop_params when calling specific models 

<Tabs>
<TabItem value="sdk" label="SDK">

```python 
import litellm 
import os 

# set keys 
os.environ["COHERE_API_KEY"] = "co-.."

response = litellm.completion(
                model="command-r",
                messages=[{"role": "user", "content": "Hey, how's it going?"}],
                response_format={"key": "value"},
                drop_params=True
            )
```
</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
- litellm_params:
    api_base: my-base
    model: openai/my-model
    drop_params: true # 👈 KEY CHANGE
  model_name: my-model
```
</TabItem>
</Tabs>

## Specify params to drop 

To drop specific params when calling a provider (E.g. 'logit_bias' for vllm)

Use `additional_drop_params`

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm 
import os 

# set keys 
os.environ["COHERE_API_KEY"] = "co-.."

response = litellm.completion(
                model="command-r",
                messages=[{"role": "user", "content": "Hey, how's it going?"}],
                response_format={"key": "value"},
                additional_drop_params=["response_format"]
            )
```
</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
- litellm_params:
    api_base: my-base
    model: openai/my-model
    additional_drop_params: ["response_format"] # 👈 KEY CHANGE
  model_name: my-model
```
</TabItem>
</Tabs>

**additional_drop_params**: List or null - Is a list of openai params you want to drop when making a call to the model.

### Nested Field Removal

Drop nested fields within complex objects using JSONPath-like notation:

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm

response = litellm.completion(
    model="bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    messages=[{"role": "user", "content": "Hello"}],
    tools=[{
        "name": "search",
        "description": "Search files",
        "input_schema": {"type": "object", "properties": {"query": {"type": "string"}}},
        "input_examples": [{"query": "test"}]  # Will be removed
    }],
    additional_drop_params=["tools[*].input_examples"]  # Remove from all tools
)
```

</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
model_list:
  - model_name: my-bedrock-model
    litellm_params:
      model: bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0
      additional_drop_params: ["tools[*].input_examples"]  # Remove from all tools
```

</TabItem>
</Tabs>

**Supported syntax:**
- `field` - Top-level field
- `parent.child` - Nested object field
- `array[*]` - All array elements
- `array[0]` - Specific array index
- `tools[*].input_examples` - Field in all array elements
- `tools[0].metadata.field` - Specific index + nested field

**Example use cases:**
- Remove `input_examples` from tool definitions (Claude Code + AWS Bedrock)
- Drop provider-specific fields from nested structures
- Clean up nested parameters before sending to LLM

## Specify allowed openai params in a request

Tell litellm to allow specific openai params in a request. Use this if you get a `litellm.UnsupportedParamsError` and want to allow a param. LiteLLM will pass the param as is to the model.



<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">

In this example we pass `allowed_openai_params=["tools"]` to allow the `tools` param.

```python showLineNumbers title="Pass allowed_openai_params to LiteLLM Python SDK"
await litellm.acompletion(
    model="azure/o_series/<my-deployment-name>",
    api_key="xxxxx",
    api_base=api_base,
    messages=[{"role": "user", "content": "Hello! return a json object"}],
    tools=[{"type": "function", "function": {"name": "get_current_time", "description": "Get the current time in a given location.", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city name, e.g. San Francisco"}}, "required": ["location"]}}}]
    allowed_openai_params=["tools"],
)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">

When using litellm proxy you can pass `allowed_openai_params` in two ways:

1. Dynamically pass `allowed_openai_params` in a request
2. Set `allowed_openai_params` on the config.yaml file for a specific model

#### Dynamically pass allowed_openai_params in a request
In this example we pass `allowed_openai_params=["tools"]` to allow the `tools` param for a request sent to the model set on the proxy.

```python showLineNumbers title="Dynamically pass allowed_openai_params in a request"
import openai
from openai import AsyncAzureOpenAI

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={ 
        "allowed_openai_params": ["tools"]
    }
)
```

#### Set allowed_openai_params on config.yaml

You can also set `allowed_openai_params` on the config.yaml file for a specific model. This means that all requests to this deployment are allowed to pass in the `tools` param.

```yaml showLineNumbers title="Set allowed_openai_params on config.yaml"
model_list:
  - model_name: azure-o1-preview
    litellm_params:
      model: azure/o_series/<my-deployment-name>
      api_key: xxxxx
      api_base: https://openai-prod-test.openai.azure.com/openai/deployments/o1/chat/completions?api-version=2025-01-01-preview
      allowed_openai_params: ["tools"]
```
</TabItem>
</Tabs>

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/drop-params) · https://claudary.paisolsolutions.com