AI Endpoints - Responses API

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KB0075059

AI Endpoints - Responses API


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AI Endpoints is covered by the OVHcloud AI Endpoints Conditions and the OVHcloud Public Cloud Special Conditions.

Introduction

AI Endpoints is a serverless platform provided by OVHcloud that offers easy access to a selection of world-renowned, pre-trained AI models.

The Responses API (/v1/responses) is the most recent OpenAI-compatible route. Like v1/chat/completions, it can be used for text generation, multi-turn conversations, tool/function calling, structured outputs, and vision inputs (on compatible models).

The key difference is that /v1/responses is intended as the foundation for newer capabilities and agentic behaviour, introducing advanced features such as statefulness and built-in tools.

The v1/responses route was added recently. Some parameters and behaviours may differ between models. For up-to-date limitations, refer to Endpoint Limitations and check model capabilities in the Catalog.

Objective

This documentation provides an overview of the v1/responses route on AI Endpoints, including:

  • Basic requests and common response fields
  • Usage examples in Python, JavaScript, and cURL
  • A detailed explanation of the most important parameters
  • Known limitations on the platform

Requirements

The examples provided during this guide can be used with one of the following environments:

  • Python
  • JavaScript
  • cURL

A Python environment with the openai client.

pip install openai

A Node.js environment with the official openai SDK.

npm install openai

A standard terminal, with cURL installed on the system.

Authentication & Rate Limiting

Most examples provided in this guide are authenticated and expect the AI_ENDPOINT_API_KEY to be set in order to avoid rate limiting issues. If you wish to enable authentication using your own token, specify your own API key in the environment (export AI_ENDPOINT_API_KEY='your_api_key').

Follow the instructions in the AI Endpoints - Getting Started guide for more information on authentication.

Quickstart

On AI Endpoints, statefulness for v1/responses is currently not managed. To avoid unexpected behaviour and to match the current platform implementation, always send store: false.

Basic request (text input)

The simplest request is a single text input.

  • Python
  • JavaScript
  • cURL
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
    base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
    api_key=api_key,
)

response = client.responses.create(
    model="gpt-oss-20b",
    input="Explain RAG in one paragraph.",
    store=False,
)

print(response.output_text)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const response = await client.responses.create({
  model: "gpt-oss-20b",
  input: "Explain RAG in one paragraph.",
  store: false,
});

console.log(response.output_text);
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "input": "Explain RAG in one paragraph.",
    "store": false
  }'

Multi-turn conversations

To create a multi-turn conversation, keep the full conversation history on your side and send it as an input list at each request.

On AI Endpoints, statefulness for v1/responses is currently unavailable. This means you must always send the full history as part of input.

Client-managed conversation (input list)

  • Python
  • JavaScript
  • cURL
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

resp = client.responses.create(
  model="gpt-oss-20b",
  store=False,
  input=[
    {"type": "message", "role": "user", "content": "My name is Stéphane."},
    {"type": "message", "role": "assistant", "content": "Hello Stéphane! How can I help?"},
    {"type": "message", "role": "user", "content": "What is my name?"},
  ],
)

print(resp.output_text)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const resp = await client.responses.create({
  model: "gpt-oss-20b",
  store: false,
  input: [
    { type: "message", role: "user", content: "My name is Stéphane." },
    { type: "message", role: "assistant", content: "Hello Stéphane! How can I help?" },
    { type: "message", role: "user", content: "What is my name?" },
  ],
});

console.log(resp.output_text);
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "store": false,
    "input": [
      {"type": "message", "role": "user", "content": "My name is Stéphane."},
      {"type": "message", "role": "assistant", "content": "Hello Stéphane! How can I help?"},
      {"type": "message", "role": "user", "content": "What is my name?"}
    ]
  }'

Providing a system prompt

You can provide system-level instructions in two ways:

  • instructions (simple and compact)
  • A role: "system" item inside an input list (useful when you already send a list for multi-turn)

Option 1: instructions

  • Python
  • JavaScript
  • cURL
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

resp = client.responses.create(
  model="gpt-oss-20b",
  instructions="You are a technical writer. Answer in British English.",
  input="Write a short definition of embeddings.",
  store=False,
)

print(resp.output_text)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const resp = await client.responses.create({
  model: "gpt-oss-20b",
  instructions: "You are a technical writer. Answer in British English.",
  input: "Write a short definition of embeddings.",
  store: false,
});

console.log(resp.output_text);
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "instructions": "You are a technical writer. Answer in British English.",
    "input": "Write a short definition of embeddings.",
    "store": false
  }'

Option 2: role: "system" in an input list

  • Python
  • JavaScript
  • cURL
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

resp = client.responses.create(
  model="gpt-oss-20b",
  store=False,
  input=[
    {"type": "message", "role": "system", "content": "You are a technical writer. Answer in British English."},
    {"type": "message", "role": "user", "content": "Write a short definition of embeddings."}
  ],
)

print(resp.output_text)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const resp = await client.responses.create({
  model: "gpt-oss-20b",
  store: false,
  input: [
    { type: "message", role: "system", content: "You are a technical writer. Answer in British English." },
    { type: "message", role: "user", content: "Write a short definition of embeddings." },
  ],
});

console.log(resp.output_text);
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "store": false,
    "input": [
      {"type": "message", "role": "system", "content": "You are a technical writer. Answer in British English."},
      {"type": "message", "role": "user", "content": "Write a short definition of embeddings."}
    ]
  }'

Streaming (stream: true)

If stream is enabled, the API returns Server-Sent Events (SSE) with incremental output. This is useful for chat UIs and CLIs.

  • Python
  • JavaScript
  • cURL
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

stream = client.responses.create(
  model="gpt-oss-20b",
  input="Write a haiku about cloud computing.",
  stream=True,
  store=False,
)

for event in stream:
  # The exact event fields can vary by SDK version.
  # A common approach is to print any incremental output text.
  delta = getattr(event, "delta", None)
  if delta:
    print(delta, end="", flush=True)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const stream = await client.responses.create({
  model: "gpt-oss-20b",
  input: "Write a haiku about cloud computing.",
  stream: true,
  store: false,
});

for await (const event of stream) {
  const delta = event?.delta;
  if (delta) process.stdout.write(delta);
}
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "input": "Write a haiku about cloud computing.",
    "stream": true,
    "store": false
  }'

Structured outputs (text.format)

Some models support enforcing a structured output format. This is useful when you need predictable, machine-readable responses.

The text.format object can be used in these modes (model permitting):

  • {"type": "text"} Default textual format.

  • {"type": "json_schema", "name": "...", "schema": { ... }} Schema-enforced mode: the model returns JSON that matches your JSON Schema.

Example: JSON schema extraction

  • Python
  • JavaScript
  • cURL
import json
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

resp = client.responses.create(
  model="gpt-oss-20b",
  store=False,
  input=[
    {
      "type": "message",
      "role": "system",
      "content": "You are a helpful extractor. Return only valid JSON.",
    },
    {
      "type": "message",
      "role": "user",
      "content": "Extract the company name and the contract start date from: Contract starts on 2026-01-12 with OVHcloud.",
    },
  ],
  text={
    "format": {
      "type": "json_schema",
      "name": "contract_data",
      "description": "Extract contract fields",
      "schema": {
        "type": "object",
        "properties": {
          "company": {"type": "string"},
          "start_date": {"type": "string"},
        },
        "required": ["company", "start_date"],
        "additionalProperties": False,
      },
      "strict": False,
    }
  },
)

# `output_text` is typically the JSON string generated by the model.
data = json.loads(resp.output_text)
print(json.dumps(data, indent=2))
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const resp = await client.responses.create({
  model: "gpt-oss-20b",
  store: false,
  input: [
    {
      type: "message",
      role: "system",
      content: "You are a helpful extractor. Return only valid JSON.",
    },
    {
      type: "message",
      role: "user",
      content:
        "Extract the company name and the contract start date from: Contract starts on 2026-01-12 with OVHcloud.",
    },
  ],
  text: {
    format: {
      type: "json_schema",
      name: "contract_data",
      description: "Extract contract fields",
      schema: {
        type: "object",
        properties: {
          company: { type: "string" },
          start_date: { type: "string" },
        },
        required: ["company", "start_date"],
        additionalProperties: false,
      },
      strict: false,
    },
  },
});

const data = JSON.parse(resp.output_text);
console.log(data);
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "store": false,
    "input": [
      {
        "type": "message",
        "role": "system",
        "content": "You are a helpful extractor. Return only valid JSON."
      },
      {
        "type": "message",
        "role": "user",
        "content": "Extract the company name and the contract start date from: Contract starts on 2026-01-12 with OVHcloud."
      }
    ],
    "text": {
      "format": {
        "type": "json_schema",
        "name": "contract_data",
        "description": "Extract contract fields",
        "schema": {
          "type": "object",
          "properties": {
            "company": {"type": "string"},
            "start_date": {"type": "string"}
          },
          "required": ["company", "start_date"],
          "additionalProperties": false
        },
        "strict": false
      }
    }
  }'

Function calling (tools)

Function calling (tool calling) lets the model request that your application runs a function. You declare the function signature in tools, the model may emit tool calls, then you execute them and provide the results back so the model can produce a final answer.

On OVHcloud AI Endpoints for v1/responses, built-in tools are not supported (e.g. web_search, file_search, computer_use, code_execution, ...). Only custom function tools are supported.

The flow is similar to the v1/chat/completions function calling guide:

  1. Call the model with tools.
  2. If the model returns a tool call: execute the tool in your application.
  3. Send a follow-up request that includes the tool result in input, then read the final answer.

Below is a minimal end-to-end example.

  • Python
  • JavaScript
  • cURL (Tool definition only)
import json
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

# 1) Tool implementation (your code)
def get_vat_rate(country: str) -> float:
  if country.lower() in ["france", "fr"]:
    return 0.20
  raise ValueError("Unsupported country")

TOOLS = [
  {
    "type": "function",
    "name": "get_vat_rate",
    "strict": False,
    "description": "Return the VAT rate for a given country.",
    "parameters": {
      "type": "object",
      "properties": {"country": {"type": "string"}},
      "required": ["country"],
      "additionalProperties": False,
    },
  }
]

# 2) First call: let the model decide whether to call the tool
input_items = [
  {"type": "message", "role": "user", "content": "What is the VAT rate in France? If needed, call the tool."}
]

first = client.responses.create(
  model="gpt-oss-20b",
  store=False,
  input=input_items,
  tools=TOOLS,
)

# 3) If a tool call is present, execute it and send the tool result back
tool_calls = getattr(first, "tool_calls", None) or []
if tool_calls:
  call = tool_calls[0]
  args = json.loads(call.function.arguments)
  result = get_vat_rate(**args)

  input_items.extend([
    {
      "type": "message",
      "role": "assistant",
      "tool_calls": [
        {
          "id": call.id,
          "type": "function",
          "function": {"name": call.function.name, "arguments": call.function.arguments},
        }
      ],
    },
    {
      "type": "message",
      "role": "tool",
      "tool_call_id": call.id,
      "name": call.function.name,
      "content": json.dumps({"vat_rate": result}),
    },
  ])

  final = client.responses.create(
    model="gpt-oss-20b",
    store=False,
    input=input_items,
    tools=TOOLS,
  )

  print(final.output_text)
else:
  # The model might answer directly without calling a tool.
  print(first.output_text)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

// 1) Tool implementation (your code)
function getVatRate(country) {
  if (["france", "fr"].includes(country.toLowerCase())) return 0.2;
  throw new Error("Unsupported country");
}

const tools = [
  {
    type: "function",
    name: "get_vat_rate",
    description: "Return the VAT rate for a given country.",
    strict: false,
    parameters: {
      type: "object",
      properties: { country: { type: "string" } },
      required: ["country"],
      additionalProperties: false,
    },
  },
];

// 2) First call
const input = [
  { type: "message", role: "user", content: "What is the VAT rate in France? If needed, call the tool." },
];

const first = await client.responses.create({
  model: "gpt-oss-20b",
  store: false,
  input,
  tools,
});

const toolCalls = first.tool_calls ?? [];
if (toolCalls.length > 0) {
  const call = toolCalls[0];
  const args = JSON.parse(call.function.arguments);
  const result = getVatRate(args.country);

  input.push(
    {
      type: "message",
      role: "assistant",
      tool_calls: [
        {
          id: call.id,
          type: "function",
          function: { name: call.function.name, arguments: call.function.arguments },
        },
      ],
    },
    {
      type: "message",
      role: "tool",
      tool_call_id: call.id,
      name: call.function.name,
      content: JSON.stringify({ vat_rate: result }),
    },
  );

  const final = await client.responses.create({
    model: "gpt-oss-20b",
    store: false,
    input,
    tools,
  });

  console.log(final.output_text);
} else {
  console.log(first.output_text);
}

cURL is convenient to declare tools, but executing tools and sending tool results back requires application-side logic.

curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "store": false,
    "input": "What is the VAT rate in France? If needed, call the tool.",
    "tools": [
      {
        "type": "function",
        "name": "get_vat_rate",
        "description": "Return the VAT rate for a given country.",
        "strict": false,
        "parameters": {
          "type": "object",
          "properties": {"country": {"type": "string"}},
          "required": ["country"],
          "additionalProperties": false
        }
      }
    ]
  }'

Vision language models (image inputs)

Some models accept image inputs. When supported, you can pass an input array containing a mix of text and image parts.

OVHcloud AI Endpoints currently does not support fetching images from remote URLs for input_image. Provide images as a base64-encoded data URL (for example: data:image/png;base64,...).

  • Python
  • JavaScript
  • cURL
import base64
import mimetypes
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

def to_data_url(image_path: str) -> str:
  mime_type, _ = mimetypes.guess_type(image_path)
  if mime_type is None:
    mime_type = "image/jpeg"

  with open(image_path, "rb") as f:
    b64 = base64.b64encode(f.read()).decode("utf-8")

  return f"data:{mime_type};base64,{b64}"

resp = client.responses.create(
  model="Qwen2.5-VL-72B-Instruct",
  store=False,
  input=[
    {
      "type": "message",
      "role": "user",
      "content": [
        {"type": "input_text", "text": "Describe this image."},
        {"type": "input_image", "image_url": to_data_url("sample.jpg")},
      ],
    }
  ],
)

print(resp.output_text)
import fs from "node:fs";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

function toDataUrl(filePath, mimeType = "image/jpeg") {
  const b64 = fs.readFileSync(filePath, { encoding: "base64" });
  return `data:${mimeType};base64,${b64}`;
}

const resp = await client.responses.create({
  model: "Qwen2.5-VL-72B-Instruct",
  store: false,
  input: [
    {
      type: "message",
      role: "user",
      content: [
        { type: "input_text", text: "Describe this image." },
        { type: "input_image", image_url: toDataUrl("sample.jpg") },
      ],
    },
  ],
});

console.log(resp.output_text);
IMAGE_B64=$(base64 -i sample.jpg | tr -d '\n')
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d "{\
    \"model\": \"Qwen2.5-VL-72B-Instruct\",\
    \"store\": false,\
    \"input\": [\
      {\
        \"type\": \"message\",\
        \"role\": \"user\",\
        \"content\": [\
          {\"type\": \"input_text\", \"text\": \"Describe this image.\"},\
          {\"type\": \"input_image\", \"image_url\": \"data:image/jpeg;base64,$IMAGE_B64\"}\
        ]\
      }\
    ]\
  }"

Image inputs are supported only by vision-capable models. Refer to the Catalog and model pages for supported content types.

Reasoning models (reasoning)

Some models expose reasoning-related controls. When supported, a reasoning object can be used to tune the reasoning effort and/or retrieve reasoning metadata.

Reasoning parameters are model-specific. If you get validation errors, either remove reasoning or switch to a reasoning-capable model.

  • Python
  • JavaScript
  • cURL
import os
from openai import OpenAI

api_key = os.environ["AI_ENDPOINT_API_KEY"]  # export AI_ENDPOINT_API_KEY='your_api_key'

client = OpenAI(
  base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  api_key=api_key,
)

resp = client.responses.create(
  model="gpt-oss-20b",
  store=False,
  input="Compute 17*23 and explain the steps.",
  reasoning={"effort": "medium"},
)

print(resp.output_text)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
  apiKey: process.env.AI_ENDPOINT_API_KEY || "", // Read from environment variable
});

const resp = await client.responses.create({
  model: "gpt-oss-20b",
  store: false,
  input: "Compute 17*23 and explain the steps.",
  reasoning: { effort: "medium" },
});

console.log(resp.output_text);
curl https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AI_ENDPOINT_API_KEY" \
  -d '{
    "model": "gpt-oss-20b",
    "store": false,
    "input": "Compute 17*23 and explain the steps.",
    "reasoning": {"effort": "medium"}
  }'

Endpoint limitations

The v1/responses endpoint is still undergoing development and all features may not be available. If you are interested in specific features that would like us to prioritise, don't hesitate to let us know on the OVHcloud Discord server.

Statefulness

Statefulness is currently not managed on AI Endpoints for the v1/responses route.

  • Always send store: false to avoid unexpected behaviour (the OpenAI specification defaults to store: true).
  • previous_response_id is currently not supported.
  • To implement multi-turn, send the full history in the input list.

Built-in tools

OpenAI-compatible built-in tools are currently not supported on OVHcloud AI Endpoints for v1/responses (for example: web_search, file_search, computer_use, code_execution, remote tools with type: "mcp", etc.).

If you need tool calling, only custom function tools are supported: declare them explicitly in the tools array (see Function calling (tools)).

Known issues / unsupported parameters

The following parameters may be unsupported, ignored, or inconsistently implemented depending on the model/backend:

  • Reasoning summaries and some reasoning metadata fields
  • background
  • include
  • max_tool_calls
  • prompt_cache_key
  • truncation
  • Reusable prompts (prompt parameter)
  • safety_identifier
  • service_tier
  • stream_options
  • user
  • verbosity

Model-specific limitations you may encounter:

  • Some models are not compatible with the v1/responses route
  • JSON object / JSON schema support varies (structured outputs)
  • Tool calling may be unsupported, or tool_choice values may be restricted (for example: not supporting non-auto modes)
  • Some models do not support system prompts / instructions
  • Multi-turn conversations may behave unexpectedly when combining structured outputs, system instructions, or reasoning parameters
  • Structured outputs with streaming may be unsupported
  • logprobs may not be supported on some models
  • Parallel tool calls may be unsupported on some models
  • Image inputs are supported only by vision-capable models

Conclusion

The Responses API provides a unified way to interact with LLMs on OVHcloud AI Endpoints, covering basic text generation as well as advanced use cases such as multi-turn conversations, streaming, structured outputs, function calling, and vision inputs (model permitting).

To maximise compatibility, always verify supported features for your chosen model in the AI Endpoints catalog, and consider falling back to v1/chat/completions when a feature is not available on v1/responses.

Go further

Browse the full AI Endpoints documentation to explore other guides and tutorials.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

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