---
title: "v1/messages → /responses Parameter Mapping"
url: "/docs/anthropic_unified/messages_to_responses_mapping"
canonical_url: "https://docs.litellm.ai/docs/anthropic_unified/messages_to_responses_mapping"
type: "docs"
last_updated: "2026-10-09"
summary: "When you send a request to /v1/messages targeting an OpenAI or Azure model, LiteLLM internally routes it through the OpenAI Responses API. This page documents exactly how every parameter gets translated in both directions."
related:
  - "/docs/anthropic_unified/structured_output"
  - "/docs/anthropic_unified/native_passthrough"
---
# v1/messages → /responses Parameter Mapping

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


When you send a request to `/v1/messages` targeting an OpenAI or Azure model, LiteLLM internally routes it through the OpenAI Responses API. This page documents exactly how every parameter gets translated in both directions.

The transformation lives in `litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py`.

## Request: Anthropic → Responses API

### Top-level parameters

| Anthropic (`/v1/messages`) | Responses API | Notes |
|---|---|---|
| `model` | `model` | Passed through as-is |
| `messages` | `input` | Structurally transformed — see the messages section below |
| `system` (string) | `instructions` | Passed as a plain string |
| `system` (list of content blocks) | `instructions` | Text blocks are joined with `\n`; non-text blocks are ignored |
| `max_tokens` | `max_output_tokens` | Renamed |
| `temperature` | `temperature` | Passed through as-is |
| `top_p` | `top_p` | Passed through as-is |
| `tools` | `tools` | Format-translated — see the tools section below |
| `tool_choice` | `tool_choice` | Type-remapped — see the tool_choice section below |
| `thinking` | `reasoning` | Budget tokens mapped to effort level — see the thinking section below |
| `output_format` or `output_config.format` | `text` | Wrapped as `{"format": {"type": "json_schema", "name": "structured_output", "schema": ..., "strict": ...}}`. `strict` is copied from the request's `strict` flag and defaults to `false`, so schemas with optional properties pass through; set `strict: true` to opt into OpenAI strict mode, which requires every property in `required` |
| `context_management` | `context_management` | Converted from Anthropic dict to OpenAI array format — see the context_management section below |
| `metadata.user_id` | `user` | Extracted from the metadata object and truncated to 64 characters |
| `metadata.user_id` | `prompt_cache_key` | Same value, truncated to 64 characters (OpenAI's key limit), so one user's requests keep hitting the same prompt cache. Not set when `user_id` is empty or null. A `prompt_cache_key` sent in the request body wins over the derived one |
| `stop_sequences` | ❌ Not mapped | Dropped silently |
| `top_k` | ❌ Not mapped | Dropped silently |
| `speed` | ❌ Not mapped | Only used to set Anthropic beta headers on the native path |

The chat-completions path (`litellm_settings.use_chat_completions_url_for_anthropic_messages: true`, and every non-OpenAI provider) derives the same two parameters from `metadata.user_id`, with two differences: `user` is passed through untruncated, and `prompt_cache_key` is only set when the target provider's supported params include it. OpenAI, Azure OpenAI and other OpenAI-compatible providers do; Gemini, Vertex AI, Bedrock and Anthropic do not. `litellm_proxy/` deployments (one LiteLLM proxy in front of another) never get the derived key, because the downstream proxy's real provider is unknown and would reject it unless `drop_params` is set there.

### How messages get converted

Each Anthropic message is expanded into one or more Responses API input items. The key difference is that `tool_result` and `tool_use` blocks become **top-level items** in the input array rather than being nested inside a message.

| Anthropic message | Responses API input item |
|---|---|
| `user` role, string content | `{"type": "message", "role": "user", "content": [{"type": "input_text", "text": "..."}]}` |
| `user` role, `{"type": "text"}` block | `{"type": "input_text", "text": "..."}` inside a user message |
| `user` role, `{"type": "image", "source": {"type": "base64"}}` | `{"type": "input_image", "image_url": "data:<media_type>;base64,<data>"}` inside a user message |
| `user` role, `{"type": "image", "source": {"type": "url"}}` | `{"type": "input_image", "image_url": "<url>"}` inside a user message |
| `user` role, `{"type": "tool_result"}` block | Top-level `{"type": "function_call_output", "call_id": "...", "output": "..."}` — pulled out of the message entirely |
| `assistant` role, string content | `{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "..."}]}` |
| `assistant` role, `{"type": "text"}` block | `{"type": "output_text", "text": "..."}` inside an assistant message |
| `assistant` role, `{"type": "tool_use"}` block | Top-level `{"type": "function_call", "call_id": "<id>", "name": "...", "arguments": "<JSON string>"}` — pulled out of the message entirely |
| `assistant` role, `{"type": "thinking"}` block | `{"type": "output_text", "text": "<thinking text>"}` inside an assistant message |

### tools

| Anthropic tool | Responses API tool |
|---|---|
| Any tool where `type` starts with `"web_search"` or `name == "web_search"` | `{"type": "web_search_preview"}` |
| All other tools | `{"type": "function", "name": "...", "description": "...", "parameters": <input_schema>}` |

### tool_choice

| Anthropic `tool_choice.type` | Responses API `tool_choice` |
|---|---|
| `"auto"` | `{"type": "auto"}` |
| `"any"` | `{"type": "required"}` |
| `"tool"` | `{"type": "function", "name": "<tool name>"}` |

### thinking → reasoning

The `budget_tokens` value is mapped to a string effort level. `summary` is always set to `"detailed"`.

| `thinking.budget_tokens` | `reasoning.effort` |
|---|---|
| >= 10000 | `"high"` |
| >= 5000 | `"medium"` |
| >= 2000 | `"low"` |
| < 2000 | `"minimal"` |

If `thinking.type` is anything other than `"enabled"`, the `reasoning` field is not sent at all.

### context_management

Anthropic uses a nested dict with an `edits` array. OpenAI uses a flat array of compaction objects.

```
Anthropic input:
{
  "edits": [
    {
      "type": "compact_20260112",
      "trigger": {"type": "input_tokens", "value": 150000}
    }
  ]
}

Responses API output:
[
  {"type": "compaction", "compact_threshold": 150000}
]
```

## Response: Responses API → Anthropic

When the Responses API reply comes back, LiteLLM converts it into an Anthropic `AnthropicMessagesResponse`.

| Responses API field | Anthropic response field | Notes |
|---|---|---|
| `response.id` | `id` | |
| `response.model` | `model` | Falls back to `"unknown-model"` if missing |
| `ResponseReasoningItem` — `summary[*].text` | `content` block `{"type": "thinking", "thinking": "..."}` | Each non-empty summary text becomes a thinking block |
| `ResponseOutputMessage` — `content[*]` where `type == "output_text"` | `content` block `{"type": "text", "text": "..."}` | |
| `ResponseFunctionToolCall` — `{call_id, name, arguments}` | `content` block `{"type": "tool_use", "id": "...", "name": "...", "input": {...}}` | `arguments` is JSON-parsed back into a dict |
| Any `function_call` present in output | `stop_reason: "tool_use"` | |
| `response.status == "incomplete"` | `stop_reason: "max_tokens"` | Takes precedence over the default |
| Everything else | `stop_reason: "end_turn"` | Default |
| `response.usage.input_tokens` | `usage.input_tokens` | |
| `response.usage.output_tokens` | `usage.output_tokens` | |
| *(hardcoded)* | `type: "message"` | Always set |
| *(hardcoded)* | `role: "assistant"` | Always set |
| *(hardcoded)* | `stop_sequence: null` | Always null on this path |

## Related pages

- [Structured Output /v1/messages](https://docs.litellm.ai/docs/anthropic_unified/structured_output.md)
- [Native /v1/messages and /v1/responses Passthrough for OpenAI-Compatible Providers](https://docs.litellm.ai/docs/anthropic_unified/native_passthrough.md)