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cli/resources/realtime/subresources/client_secrets/index.md 2026-06-16 21:57 UTC to 2026-06-17 18:02 UTC

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Client Secrets

Create client secret

$ openai realtime:client-secrets create

post /realtime/client_secrets

Create a Realtime client secret with an associated session configuration.

Client secrets are short-lived tokens that can be passed to a client app, such as a web frontend or mobile client, which grants access to the Realtime API without leaking your main API key. You can configure a custom TTL for each client secret.

You can also attach session configuration options to the client secret, which will be applied to any sessions created using that client secret, but these can also be overridden by the client connection.

Learn more about authentication with client secrets over WebRTC.

Returns the created client secret and the effective session object. The client secret is a string that looks like ek_1234.

Parameters

  • --expires-after: optional object { anchor, seconds }

    Configuration for the client secret expiration. Expiration refers to the time after which a client secret will no longer be valid for creating sessions. The session itself may continue after that time once started. A secret can be used to create multiple sessions until it expires.

  • --session: optional RealtimeSessionCreateRequest or RealtimeTranscriptionSessionCreateRequest

    Session configuration to use for the client secret. Choose either a realtime session or a transcription session.

Returns

  • ClientSecretNewResponse: object { expires_at, session, value }

    Response from creating a session and client secret for the Realtime API.

    • expires_at: number

      Expiration timestamp for the client secret, in seconds since epoch.

    • session: RealtimeSessionCreateResponse or RealtimeTranscriptionSessionCreateResponse

      The session configuration for either a realtime or transcription session.

      • realtime_session_create_response: object { id, object, type, 13 more }

        A Realtime session configuration object.

        • id: string

          Unique identifier for the session that looks like sess_1234567890abcdef.

        • object: "realtime.session"

          The object type. Always realtime.session.

        • type: "realtime"

          The type of session to create. Always realtime for the Realtime API.

        • audio: optional object { input, output }

          Configuration for input and output audio.

          • input: optional object { format, noise_reduction, transcription, turn_detection }

            • format: optional object { rate, type } or object { type } or object { type }

              The format of the input audio.

              • audio/pcm: object { rate, type }

                The PCM audio format. Only a 24kHz sample rate is supported.

                • rate: optional 24000

                  The sample rate of the audio. Always 24000.

                  • 24000

                • type: optional "audio/pcm"

                  The audio format. Always audio/pcm.

                  • "audio/pcm"

              • audio/pcmu: object { type }

                The G.711 μ-law format.

                • type: optional "audio/pcmu"

                  The audio format. Always audio/pcmu.

                  • "audio/pcmu"

              • audio/pcma: object { type }

                The G.711 A-law format.

                • type: optional "audio/pcma"

                  The audio format. Always audio/pcma.

                  • "audio/pcma"

            • noise_reduction: optional object { type }

              Configuration for input audio noise reduction. This can be set to null to turn off. Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model. Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.

              • type: optional "near_field" or "far_field"

                Type of noise reduction. near_field is for close-talking microphones such as headphones, far_field is for far-field microphones such as laptop or conference room microphones.

                • "near_field"

                • "far_field"

            • transcription: optional object { delay, language, model, prompt }

              • delay: optional "minimal" or "low" or "medium" or 2 more

                Controls how long the model waits before emitting transcription text. Higher values can improve transcription accuracy at the cost of latency. Only supported with gpt-realtime-whisper in GA Realtime sessions.

                • "minimal"

                • "low"

                • "medium"

                • "high"

                • "xhigh"

              • language: optional string

                The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

              • model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 3 more

                The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, gpt-4o-transcribe-diarize, and gpt-realtime-whisper. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.

                • "whisper-1"

                • "gpt-4o-mini-transcribe"

                • "gpt-4o-mini-transcribe-2025-12-15"

                • "gpt-4o-transcribe"

                • "gpt-4o-transcribe-diarize"

                • "gpt-realtime-whisper"

              • prompt: optional string

                An optional text to guide the model's style or continue a previous audio segment. For whisper-1, the prompt is a list of keywords. For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology". Prompt is not supported with gpt-realtime-whisper in GA Realtime sessions.

            • turn_detection: optional object { type, create_response, idle_timeout_ms, 4 more } or object { type, create_response, eagerness, interrupt_response }

              Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.

              Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.

              Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.

              For gpt-realtime-whisper transcription sessions, turn detection must be set to null; VAD is not supported.

              • server_vad: object { type, create_response, idle_timeout_ms, 4 more }

                Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.

                • type: "server_vad"

                  Type of turn detection, server_vad to turn on simple Server VAD.

                • create_response: optional boolean

                  Whether or not to automatically generate a response when a VAD stop event occurs. If interrupt_response is set to false this may fail to create a response if the model is already responding.

                  If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.

                • idle_timeout_ms: optional number

                  Optional timeout after which a model response will be triggered automatically. This is useful for situations in which a long pause from the user is unexpected, such as a phone call. The model will effectively prompt the user to continue the conversation based on the current context.

                  The timeout value will be applied after the last model response's audio has finished playing, i.e. it's set to the response.done time plus audio playback duration.

                  An input_audio_buffer.timeout_triggered event (plus events associated with the Response) will be emitted when the timeout is reached. Idle timeout is currently only supported for server_vad mode.

                • interrupt_response: optional boolean

                  Whether or not to automatically interrupt (cancel) any ongoing response with output to the default conversation (i.e. conversation of auto) when a VAD start event occurs. If true then the response will be cancelled, otherwise it will continue until complete.

                  If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.

                • prefix_padding_ms: optional number

                  Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

                • silence_duration_ms: optional number

                  Used only for server_vad mode. Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

                • threshold: optional number

                  Used only for server_vad mode. Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

              • semantic_vad: object { type, create_response, eagerness, interrupt_response }

                Server-side semantic turn detection which uses a model to determine when the user has finished speaking.

                • type: "semantic_vad"

                  Type of turn detection, semantic_vad to turn on Semantic VAD.

                • create_response: optional boolean

                  Whether or not to automatically generate a response when a VAD stop event occurs.

                • eagerness: optional "low" or "medium" or "high" or "auto"

                  Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.

                  • "low"

                  • "medium"

                  • "high"

                  • "auto"

                • interrupt_response: optional boolean

                  Whether or not to automatically interrupt any ongoing response with output to the default conversation (i.e. conversation of auto) when a VAD start event occurs.

          • output: optional object { format, speed, voice }

            • format: optional object { rate, type } or object { type } or object { type }

              The format of the output audio.

              • audio/pcm: object { rate, type }

                The PCM audio format. Only a 24kHz sample rate is supported.

              • audio/pcmu: object { type }

                The G.711 μ-law format.

              • audio/pcma: object { type }

                The G.711 A-law format.

            • speed: optional number

              The speed of the model's spoken response as a multiple of the original speed. 1.0 is the default speed. 0.25 is the minimum speed. 1.5 is the maximum speed. This value can only be changed in between model turns, not while a response is in progress.

              This parameter is a post-processing adjustment to the audio after it is generated, it's also possible to prompt the model to speak faster or slower.

            • voice: optional string or "alloy" or "ash" or "ballad" or 7 more

              The voice the model uses to respond. Voice cannot be changed during the session once the model has responded with audio at least once. Current voice options are alloy, ash, ballad, coral, echo, sage, shimmer, verse, marin, and cedar. We recommend marin and cedar for best quality.

              • "alloy"

              • "ash"

              • "ballad"

              • "coral"

              • "echo"

              • "sage"

              • "shimmer"

              • "verse"

              • "marin"

              • "cedar"

        • expires_at: optional number

          Expiration timestamp for the session, in seconds since epoch.

        • include: optional array of "item.input_audio_transcription.logprobs"

          Additional fields to include in server outputs.

          item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.

          • "item.input_audio_transcription.logprobs"

        • instructions: optional string

          The default system instructions (i.e. system message) prepended to model calls. This field allows the client to guide the model on desired responses. The model can be instructed on response content and format, (e.g. "be extremely succinct", "act friendly", "here are examples of good responses") and on audio behavior (e.g. "talk quickly", "inject emotion into your voice", "laugh frequently"). The instructions are not guaranteed to be followed by the model, but they provide guidance to the model on the desired behavior.

          Note that the server sets default instructions which will be used if this field is not set and are visible in the session.created event at the start of the session.

        • max_output_tokens: optional number or "inf"

          Maximum number of output tokens for a single assistant response, inclusive of tool calls. Provide an integer between 1 and 4096 to limit output tokens, or inf for the maximum available tokens for a given model. Defaults to inf.

          • union_member_0: number

          • union_member_1: "inf"

        • model: optional string or "gpt-realtime" or "gpt-realtime-1.5" or "gpt-realtime-2" or 14 more

          The Realtime model used for this session.

          • "gpt-realtime"

          • "gpt-realtime-1.5"

          • "gpt-realtime-2"

          • "gpt-realtime-2025-08-28"

          • "gpt-4o-realtime-preview"

          • "gpt-4o-realtime-preview-2024-10-01"

          • "gpt-4o-realtime-preview-2024-12-17"

          • "gpt-4o-realtime-preview-2025-06-03"

          • "gpt-4o-mini-realtime-preview"

          • "gpt-4o-mini-realtime-preview-2024-12-17"

          • "gpt-realtime-mini"

          • "gpt-realtime-mini-2025-10-06"

          • "gpt-realtime-mini-2025-12-15"

          • "gpt-audio-1.5"

          • "gpt-audio-mini"

          • "gpt-audio-mini-2025-10-06"

          • "gpt-audio-mini-2025-12-15"

        • output_modalities: optional array of "text" or "audio"

          The set of modalities the model can respond with. It defaults to ["audio"], indicating that the model will respond with audio plus a transcript. ["text"] can be used to make the model respond with text only. It is not possible to request both text and audio at the same time.

          • "text"

          • "audio"

        • prompt: optional object { id, variables, version }

          Reference to a prompt template and its variables. Learn more.

          • id: string

            The unique identifier of the prompt template to use.

          • variables: optional map[string or ResponseInputText or ResponseInputImage or ResponseInputFile]

            Optional map of values to substitute in for variables in your prompt. The substitution values can either be strings, or other Response input types like images or files.

            • union_member_0: string

            • response_input_text: object { text, type }

              A text input to the model.

              • text: string

                The text input to the model.

              • type: "input_text"

                The type of the input item. Always input_text.

            • response_input_image: object { detail, type, file_id, image_url }

              An image input to the model. Learn about image inputs.

              • detail: "low" or "high" or "auto" or "original"

                The detail level of the image to be sent to the model. One of high, low, auto, or original. Defaults to auto.

                • "low"

                • "high"

                • "auto"

                • "original"

              • type: "input_image"

                The type of the input item. Always input_image.

              • file_id: optional string

                The ID of the file to be sent to the model.

              • image_url: optional string

                The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

            • response_input_file: object { type, detail, file_data, 3 more }

              A file input to the model.

              • type: "input_file"

                The type of the input item. Always input_file.

              • detail: optional "low" or "high"

                The detail level of the file to be sent to the model. Use low for the default rendering behavior, or high to render the file at higher quality. Defaults to low.

                • "low"

                • "high"

              • file_data: optional string

                The content of the file to be sent to the model.

              • file_id: optional string

                The ID of the file to be sent to the model.

              • file_url: optional string

                The URL of the file to be sent to the model.

              • filename: optional string

                The name of the file to be sent to the model.

          • version: optional string

            Optional version of the prompt template.

        • reasoning: optional object { effort }

          Configuration for reasoning-capable Realtime models such as gpt-realtime-2.

          • effort: optional "minimal" or "low" or "medium" or 2 more

            Constrains effort on reasoning for reasoning-capable Realtime models such as gpt-realtime-2.

            • "minimal"

            • "low"

            • "medium"

            • "high"

            • "xhigh"

        • tool_choice: optional ToolChoiceOptions or ToolChoiceFunction or ToolChoiceMcp

          How the model chooses tools. Provide one of the string modes or force a specific function/MCP tool.

          • tool_choice_options: "none" or "auto" or "required"

            Controls which (if any) tool is called by the model.

            none means the model will not call any tool and instead generates a message.

            auto means the model can pick between generating a message or calling one or more tools.

            required means the model must call one or more tools.

            • "none"

            • "auto"

            • "required"

          • tool_choice_function: object { name, type }

            Use this option to force the model to call a specific function.

            • name: string

              The name of the function to call.

            • type: "function"

              For function calling, the type is always function.

          • tool_choice_mcp: object { server_label, type, name }

            Use this option to force the model to call a specific tool on a remote MCP server.

            • server_label: string

              The label of the MCP server to use.

            • type: "mcp"

              For MCP tools, the type is always mcp.

            • name: optional string

              The name of the tool to call on the server.

        • tools: optional array of RealtimeFunctionTool or object { server_label, type, allowed_tools, 8 more }

          Tools available to the model.

          • realtime_function_tool: object { description, name, parameters, type }

            • description: optional string

              The description of the function, including guidance on when and how to call it, and guidance about what to tell the user when calling (if anything).

            • name: optional string

              The name of the function.

            • parameters: optional unknown

              Parameters of the function in JSON Schema.

            • type: optional "function"

              The type of the tool, i.e. function.

              • "function"

          • MCP tool: object { server_label, type, allowed_tools, 8 more }

            Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

            • server_label: string

              A label for this MCP server, used to identify it in tool calls.

            • type: "mcp"

              The type of the MCP tool. Always mcp.

            • allowed_tools: optional array of string or object { read_only, tool_names }

              List of allowed tool names or a filter object.

              • MCP allowed tools: array of string

                A string array of allowed tool names

              • MCP tool filter: object { read_only, tool_names }

                A filter object to specify which tools are allowed.

                • read_only: optional boolean

                  Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                • tool_names: optional array of string

                  List of allowed tool names.

            • authorization: optional string

              An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.

            • connector_id: optional "connector_dropbox" or "connector_gmail" or "connector_googlecalendar" or 5 more

              Identifier for service connectors, like those available in ChatGPT. One of server_url, connector_id, or tunnel_id must be provided. Learn more about service connectors here.

              Currently supported connector_id values are:

              • Dropbox: connector_dropbox

              • Gmail: connector_gmail

              • Google Calendar: connector_googlecalendar

              • Google Drive: connector_googledrive

              • Microsoft Teams: connector_microsoftteams

              • Outlook Calendar: connector_outlookcalendar

              • Outlook Email: connector_outlookemail

              • SharePoint: connector_sharepoint

              • "connector_dropbox"

              • "connector_gmail"

              • "connector_googlecalendar"

              • "connector_googledrive"

              • "connector_microsoftteams"

              • "connector_outlookcalendar"

              • "connector_outlookemail"

              • "connector_sharepoint"

            • defer_loading: optional boolean

              Whether this MCP tool is deferred and discovered via tool search.

            • headers: optional map[string]

              Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.

            • require_approval: optional object { always, never } or "always" or "never"

              Specify which of the MCP server's tools require approval.

              • MCP tool approval filter: object { always, never }

                Specify which of the MCP server's tools require approval. Can be always, never, or a filter object associated with tools that require approval.

                • always: optional object { read_only, tool_names }

                  A filter object to specify which tools are allowed.

                  • read_only: optional boolean

                    Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                  • tool_names: optional array of string

                    List of allowed tool names.

                • never: optional object { read_only, tool_names }

                  A filter object to specify which tools are allowed.

                  • read_only: optional boolean

                    Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

                  • tool_names: optional array of string

                    List of allowed tool names.

              • MCP tool approval setting: "always" or "never"

                Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

                • "always"

                • "never"

            • server_description: optional string

              Optional description of the MCP server, used to provide more context.

            • server_url: optional string

              The URL for the MCP server. One of server_url, connector_id, or tunnel_id must be provided.

            • tunnel_id: optional string

              The Secure MCP Tunnel ID to use instead of a direct server URL. One of server_url, connector_id, or tunnel_id must be provided.

        • tracing: optional "auto" or object { group_id, metadata, workflow_name }

          Realtime API can write session traces to the Traces Dashboard. Set to null to disable tracing. Once tracing is enabled for a session, the configuration cannot be modified.

          auto will create a trace for the session with default values for the workflow name, group id, and metadata.

          • auto: "auto"

            Enables tracing and sets default values for tracing configuration options. Always auto.

          • Tracing Configuration: object { group_id, metadata, workflow_name }

            Granular configuration for tracing.

            • group_id: optional string

              The group id to attach to this trace to enable filtering and grouping in the Traces Dashboard.

            • metadata: optional unknown

              The arbitrary metadata to attach to this trace to enable filtering in the Traces Dashboard.

            • workflow_name: optional string

              The name of the workflow to attach to this trace. This is used to name the trace in the Traces Dashboard.

        • truncation: optional "auto" or "disabled" or RealtimeTruncationRetentionRatio

          When the number of tokens in a conversation exceeds the model's input token limit, the conversation be truncated, meaning messages (starting from the oldest) will not be included in the model's context. A 32k context model with 4,096 max output tokens can only include 28,224 tokens in the context before truncation occurs.

          Clients can configure truncation behavior to truncate with a lower max token limit, which is an effective way to control token usage and cost.

          Truncation will reduce the number of cached tokens on the next turn (busting the cache), since messages are dropped from the beginning of the context. However, clients can also configure truncation to retain messages up to a fraction of the maximum context size, which will reduce the need for future truncations and thus improve the cache rate.

          Truncation can be disabled entirely, which means the server will never truncate but would instead return an error if the conversation exceeds the model's input token limit.

          • RealtimeTruncationStrategy: "auto" or "disabled"

            The truncation strategy to use for the session. auto is the default truncation strategy. disabled will disable truncation and emit errors when the conversation exceeds the input token limit.

            • "auto"

            • "disabled"

          • realtime_truncation_retention_ratio: object { retention_ratio, type, token_limits }

            Retain a fraction of the conversation tokens when the conversation exceeds the input token limit. This allows you to amortize truncations across multiple turns, which can help improve cached token usage.

            • retention_ratio: number

              Fraction of post-instruction conversation tokens to retain (0.0 - 1.0) when the conversation exceeds the input token limit. Setting this to 0.8 means that messages will be dropped until 80% of the maximum allowed tokens are used. This helps reduce the frequency of truncations and improve cache rates.

            • type: "retention_ratio"

              Use retention ratio truncation.

            • token_limits: optional object { post_instructions }

              Optional custom token limits for this truncation strategy. If not provided, the model's default token limits will be used.

              • post_instructions: optional number

                Maximum tokens allowed in the conversation after instructions (which including tool definitions). For example, setting this to 5,000 would mean that truncation would occur when the conversation exceeds 5,000 tokens after instructions. This cannot be higher than the model's context window size minus the maximum output tokens.

      • realtime_transcription_session_create_response: object { id, object, type, 3 more }

        A Realtime transcription session configuration object.

        • id: string

          Unique identifier for the session that looks like sess_1234567890abcdef.

        • object: string

          The object type. Always realtime.transcription_session.

        • type: "transcription"

          The type of session. Always transcription for transcription sessions.

        • audio: optional object { input }

          Configuration for input audio for the session.

          • input: optional object { format, noise_reduction, transcription, turn_detection }

            • format: optional object { rate, type } or object { type } or object { type }

              The PCM audio format. Only a 24kHz sample rate is supported.

              • audio/pcm: object { rate, type }

                The PCM audio format. Only a 24kHz sample rate is supported.

              • audio/pcmu: object { type }

                The G.711 μ-law format.

              • audio/pcma: object { type }

                The G.711 A-law format.

            • noise_reduction: optional object { type }

              Configuration for input audio noise reduction.

              • type: optional "near_field" or "far_field"

                Type of noise reduction. near_field is for close-talking microphones such as headphones, far_field is for far-field microphones such as laptop or conference room microphones.

                • "near_field"

                • "far_field"

            • transcription: optional object { delay, language, model, prompt }

              • delay: optional "minimal" or "low" or "medium" or 2 more

                Controls how long the model waits before emitting transcription text. Higher values can improve transcription accuracy at the cost of latency. Only supported with gpt-realtime-whisper in GA Realtime sessions.

              • language: optional string

                The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

              • model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 3 more

                The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, gpt-4o-transcribe-diarize, and gpt-realtime-whisper. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.

              • prompt: optional string

                An optional text to guide the model's style or continue a previous audio segment. For whisper-1, the prompt is a list of keywords. For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology". Prompt is not supported with gpt-realtime-whisper in GA Realtime sessions.

            • turn_detection: optional object { prefix_padding_ms, silence_duration_ms, threshold, type }

              Configuration for turn detection. Can be set to null to turn off. Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech. For gpt-realtime-whisper, this must be null; VAD is not supported.

              • prefix_padding_ms: optional number

                Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

              • silence_duration_ms: optional number

                Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

              • threshold: optional number

                Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

              • type: optional string

                Type of turn detection, only server_vad is currently supported.

        • expires_at: optional number

          Expiration timestamp for the session, in seconds since epoch.

        • include: optional array of "item.input_audio_transcription.logprobs"

          Additional fields to include in server outputs.

          • item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.

          • "item.input_audio_transcription.logprobs"

    • value: string

      The generated client secret value.

Example

openai realtime:client-secrets create \
  --api-key 'My API Key'

Response

{
  "expires_at": 0,
  "session": {
    "id": "id",
    "object": "realtime.session",
    "type": "realtime",
    "audio": {
      "input": {
        "format": {
          "rate": 24000,
          "type": "audio/pcm"
        },
        "noise_reduction": {
          "type": "near_field"
        },
        "transcription": {
          "delay": "minimal",
          "language": "language",
          "model": "whisper-1",
          "prompt": "prompt"
        },
        "turn_detection": {
          "type": "server_vad",
          "create_response": true,
          "idle_timeout_ms": 5000,
          "interrupt_response": true,
          "prefix_padding_ms": 0,
          "silence_duration_ms": 0,
          "threshold": 0
        }
      },
      "output": {
        "format": {
          "rate": 24000,
          "type": "audio/pcm"
        },
        "speed": 0.25,
        "voice": "ash"
      }
    },
    "expires_at": 0,
    "include": [
      "item.input_audio_transcription.logprobs"
    ],
    "instructions": "instructions",
    "max_output_tokens": "inf",
    "model": "gpt-realtime",
    "output_modalities": [
      "text"
    ],
    "prompt": {
      "id": "id",
      "variables": {
        "foo": "string"
      },
      "version": "version"
    },
    "reasoning": {
      "effort": "minimal"
    },
    "tool_choice": "none",
    "tools": [
      {
        "description": "description",
        "name": "name",
        "parameters": {},
        "type": "function"
      }
    ],
    "tracing": "auto",
    "truncation": "auto"
  },
  "value": "value"
}

Domain Types

Realtime Session Create Response

  • realtime_session_create_response: object { id, object, type, 13 more }

    A Realtime session configuration object.

    • id: string

      Unique identifier for the session that looks like sess_1234567890abcdef.

    • object: "realtime.session"

      The object type. Always realtime.session.

    • type: "realtime"

      The type of session to create. Always realtime for the Realtime API.

    • audio: optional object { input, output }

      Configuration for input and output audio.

      • input: optional object { format, noise_reduction, transcription, turn_detection }

        • format: optional object { rate, type } or object { type } or object { type }

          The format of the input audio.

          • audio/pcm: object { rate, type }

            The PCM audio format. Only a 24kHz sample rate is supported.

            • rate: optional 24000

              The sample rate of the audio. Always 24000.

              • 24000

            • type: optional "audio/pcm"

              The audio format. Always audio/pcm.

              • "audio/pcm"

          • audio/pcmu: object { type }

            The G.711 μ-law format.

            • type: optional "audio/pcmu"

              The audio format. Always audio/pcmu.

              • "audio/pcmu"

          • audio/pcma: object { type }

            The G.711 A-law format.

            • type: optional "audio/pcma"

              The audio format. Always audio/pcma.

              • "audio/pcma"

        • noise_reduction: optional object { type }

          Configuration for input audio noise reduction. This can be set to null to turn off. Noise reduction filters audio added to the input audio buffer before it is sent to VAD and the model. Filtering the audio can improve VAD and turn detection accuracy (reducing false positives) and model performance by improving perception of the input audio.

          • type: optional "near_field" or "far_field"

            Type of noise reduction. near_field is for close-talking microphones such as headphones, far_field is for far-field microphones such as laptop or conference room microphones.

            • "near_field"

            • "far_field"

        • transcription: optional object { delay, language, model, prompt }

          • delay: optional "minimal" or "low" or "medium" or 2 more

            Controls how long the model waits before emitting transcription text. Higher values can improve transcription accuracy at the cost of latency. Only supported with gpt-realtime-whisper in GA Realtime sessions.

            • "minimal"

            • "low"

            • "medium"

            • "high"

            • "xhigh"

          • language: optional string

            The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

          • model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 3 more

            The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, gpt-4o-transcribe-diarize, and gpt-realtime-whisper. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.

            • "whisper-1"

            • "gpt-4o-mini-transcribe"

            • "gpt-4o-mini-transcribe-2025-12-15"

            • "gpt-4o-transcribe"

            • "gpt-4o-transcribe-diarize"

            • "gpt-realtime-whisper"

          • prompt: optional string

            An optional text to guide the model's style or continue a previous audio segment. For whisper-1, the prompt is a list of keywords. For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology". Prompt is not supported with gpt-realtime-whisper in GA Realtime sessions.

        • turn_detection: optional object { type, create_response, idle_timeout_ms, 4 more } or object { type, create_response, eagerness, interrupt_response }

          Configuration for turn detection, ether Server VAD or Semantic VAD. This can be set to null to turn off, in which case the client must manually trigger model response.

          Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech.

          Semantic VAD is more advanced and uses a turn detection model (in conjunction with VAD) to semantically estimate whether the user has finished speaking, then dynamically sets a timeout based on this probability. For example, if user audio trails off with "uhhm", the model will score a low probability of turn end and wait longer for the user to continue speaking. This can be useful for more natural conversations, but may have a higher latency.

          For gpt-realtime-whisper transcription sessions, turn detection must be set to null; VAD is not supported.

          • server_vad: object { type, create_response, idle_timeout_ms, 4 more }

            Server-side voice activity detection (VAD) which flips on when user speech is detected and off after a period of silence.

            • type: "server_vad"

              Type of turn detection, server_vad to turn on simple Server VAD.

            • create_response: optional boolean

              Whether or not to automatically generate a response when a VAD stop event occurs. If interrupt_response is set to false this may fail to create a response if the model is already responding.

              If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.

            • idle_timeout_ms: optional number

              Optional timeout after which a model response will be triggered automatically. This is useful for situations in which a long pause from the user is unexpected, such as a phone call. The model will effectively prompt the user to continue the conversation based on the current context.

              The timeout value will be applied after the last model response's audio has finished playing, i.e. it's set to the response.done time plus audio playback duration.

              An input_audio_buffer.timeout_triggered event (plus events associated with the Response) will be emitted when the timeout is reached. Idle timeout is currently only supported for server_vad mode.

            • interrupt_response: optional boolean

              Whether or not to automatically interrupt (cancel) any ongoing response with output to the default conversation (i.e. conversation of auto) when a VAD start event occurs. If true then the response will be cancelled, otherwise it will continue until complete.

              If both create_response and interrupt_response are set to false, the model will never respond automatically but VAD events will still be emitted.

            • prefix_padding_ms: optional number

              Used only for server_vad mode. Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

            • silence_duration_ms: optional number

              Used only for server_vad mode. Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

            • threshold: optional number

              Used only for server_vad mode. Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

          • semantic_vad: object { type, create_response, eagerness, interrupt_response }

            Server-side semantic turn detection which uses a model to determine when the user has finished speaking.

            • type: "semantic_vad"

              Type of turn detection, semantic_vad to turn on Semantic VAD.

            • create_response: optional boolean

              Whether or not to automatically generate a response when a VAD stop event occurs.

            • eagerness: optional "low" or "medium" or "high" or "auto"

              Used only for semantic_vad mode. The eagerness of the model to respond. low will wait longer for the user to continue speaking, high will respond more quickly. auto is the default and is equivalent to medium. low, medium, and high have max timeouts of 8s, 4s, and 2s respectively.

              • "low"

              • "medium"

              • "high"

              • "auto"

            • interrupt_response: optional boolean

              Whether or not to automatically interrupt any ongoing response with output to the default conversation (i.e. conversation of auto) when a VAD start event occurs.

      • output: optional object { format, speed, voice }

        • format: optional object { rate, type } or object { type } or object { type }

          The format of the output audio.

          • audio/pcm: object { rate, type }

            The PCM audio format. Only a 24kHz sample rate is supported.

          • audio/pcmu: object { type }

            The G.711 μ-law format.

          • audio/pcma: object { type }

            The G.711 A-law format.

        • speed: optional number

          The speed of the model's spoken response as a multiple of the original speed. 1.0 is the default speed. 0.25 is the minimum speed. 1.5 is the maximum speed. This value can only be changed in between model turns, not while a response is in progress.

          This parameter is a post-processing adjustment to the audio after it is generated, it's also possible to prompt the model to speak faster or slower.

        • voice: optional string or "alloy" or "ash" or "ballad" or 7 more

          The voice the model uses to respond. Voice cannot be changed during the session once the model has responded with audio at least once. Current voice options are alloy, ash, ballad, coral, echo, sage, shimmer, verse, marin, and cedar. We recommend marin and cedar for best quality.

          • "alloy"

          • "ash"

          • "ballad"

          • "coral"

          • "echo"

          • "sage"

          • "shimmer"

          • "verse"

          • "marin"

          • "cedar"

    • expires_at: optional number

      Expiration timestamp for the session, in seconds since epoch.

    • include: optional array of "item.input_audio_transcription.logprobs"

      Additional fields to include in server outputs.

      item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.

      • "item.input_audio_transcription.logprobs"

    • instructions: optional string

      The default system instructions (i.e. system message) prepended to model calls. This field allows the client to guide the model on desired responses. The model can be instructed on response content and format, (e.g. "be extremely succinct", "act friendly", "here are examples of good responses") and on audio behavior (e.g. "talk quickly", "inject emotion into your voice", "laugh frequently"). The instructions are not guaranteed to be followed by the model, but they provide guidance to the model on the desired behavior.

      Note that the server sets default instructions which will be used if this field is not set and are visible in the session.created event at the start of the session.

    • max_output_tokens: optional number or "inf"

      Maximum number of output tokens for a single assistant response, inclusive of tool calls. Provide an integer between 1 and 4096 to limit output tokens, or inf for the maximum available tokens for a given model. Defaults to inf.

      • union_member_0: number

      • union_member_1: "inf"

    • model: optional string or "gpt-realtime" or "gpt-realtime-1.5" or "gpt-realtime-2" or 14 more

      The Realtime model used for this session.

      • "gpt-realtime"

      • "gpt-realtime-1.5"

      • "gpt-realtime-2"

      • "gpt-realtime-2025-08-28"

      • "gpt-4o-realtime-preview"

      • "gpt-4o-realtime-preview-2024-10-01"

      • "gpt-4o-realtime-preview-2024-12-17"

      • "gpt-4o-realtime-preview-2025-06-03"

      • "gpt-4o-mini-realtime-preview"

      • "gpt-4o-mini-realtime-preview-2024-12-17"

      • "gpt-realtime-mini"

      • "gpt-realtime-mini-2025-10-06"

      • "gpt-realtime-mini-2025-12-15"

      • "gpt-audio-1.5"

      • "gpt-audio-mini"

      • "gpt-audio-mini-2025-10-06"

      • "gpt-audio-mini-2025-12-15"

    • output_modalities: optional array of "text" or "audio"

      The set of modalities the model can respond with. It defaults to ["audio"], indicating that the model will respond with audio plus a transcript. ["text"] can be used to make the model respond with text only. It is not possible to request both text and audio at the same time.

      • "text"

      • "audio"

    • prompt: optional object { id, variables, version }

      Reference to a prompt template and its variables. Learn more.

      • id: string

        The unique identifier of the prompt template to use.

      • variables: optional map[string or ResponseInputText or ResponseInputImage or ResponseInputFile]

        Optional map of values to substitute in for variables in your prompt. The substitution values can either be strings, or other Response input types like images or files.

        • union_member_0: string

        • response_input_text: object { text, type }

          A text input to the model.

          • text: string

            The text input to the model.

          • type: "input_text"

            The type of the input item. Always input_text.

        • response_input_image: object { detail, type, file_id, image_url }

          An image input to the model. Learn about image inputs.

          • detail: "low" or "high" or "auto" or "original"

            The detail level of the image to be sent to the model. One of high, low, auto, or original. Defaults to auto.

            • "low"

            • "high"

            • "auto"

            • "original"

          • type: "input_image"

            The type of the input item. Always input_image.

          • file_id: optional string

            The ID of the file to be sent to the model.

          • image_url: optional string

            The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.

        • response_input_file: object { type, detail, file_data, 3 more }

          A file input to the model.

          • type: "input_file"

            The type of the input item. Always input_file.

          • detail: optional "low" or "high"

            The detail level of the file to be sent to the model. Use low for the default rendering behavior, or high to render the file at higher quality. Defaults to low.

            • "low"

            • "high"

          • file_data: optional string

            The content of the file to be sent to the model.

          • file_id: optional string

            The ID of the file to be sent to the model.

          • file_url: optional string

            The URL of the file to be sent to the model.

          • filename: optional string

            The name of the file to be sent to the model.

      • version: optional string

        Optional version of the prompt template.

    • reasoning: optional object { effort }

      Configuration for reasoning-capable Realtime models such as gpt-realtime-2.

      • effort: optional "minimal" or "low" or "medium" or 2 more

        Constrains effort on reasoning for reasoning-capable Realtime models such as gpt-realtime-2.

        • "minimal"

        • "low"

        • "medium"

        • "high"

        • "xhigh"

    • tool_choice: optional ToolChoiceOptions or ToolChoiceFunction or ToolChoiceMcp

      How the model chooses tools. Provide one of the string modes or force a specific function/MCP tool.

      • tool_choice_options: "none" or "auto" or "required"

        Controls which (if any) tool is called by the model.

        none means the model will not call any tool and instead generates a message.

        auto means the model can pick between generating a message or calling one or more tools.

        required means the model must call one or more tools.

        • "none"

        • "auto"

        • "required"

      • tool_choice_function: object { name, type }

        Use this option to force the model to call a specific function.

        • name: string

          The name of the function to call.

        • type: "function"

          For function calling, the type is always function.

      • tool_choice_mcp: object { server_label, type, name }

        Use this option to force the model to call a specific tool on a remote MCP server.

        • server_label: string

          The label of the MCP server to use.

        • type: "mcp"

          For MCP tools, the type is always mcp.

        • name: optional string

          The name of the tool to call on the server.

    • tools: optional array of RealtimeFunctionTool or object { server_label, type, allowed_tools, 8 more }

      Tools available to the model.

      • realtime_function_tool: object { description, name, parameters, type }

        • description: optional string

          The description of the function, including guidance on when and how to call it, and guidance about what to tell the user when calling (if anything).

        • name: optional string

          The name of the function.

        • parameters: optional unknown

          Parameters of the function in JSON Schema.

        • type: optional "function"

          The type of the tool, i.e. function.

          • "function"

      • MCP tool: object { server_label, type, allowed_tools, 8 more }

        Give the model access to additional tools via remote Model Context Protocol (MCP) servers. Learn more about MCP.

        • server_label: string

          A label for this MCP server, used to identify it in tool calls.

        • type: "mcp"

          The type of the MCP tool. Always mcp.

        • allowed_tools: optional array of string or object { read_only, tool_names }

          List of allowed tool names or a filter object.

          • MCP allowed tools: array of string

            A string array of allowed tool names

          • MCP tool filter: object { read_only, tool_names }

            A filter object to specify which tools are allowed.

            • read_only: optional boolean

              Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

            • tool_names: optional array of string

              List of allowed tool names.

        • authorization: optional string

          An OAuth access token that can be used with a remote MCP server, either with a custom MCP server URL or a service connector. Your application must handle the OAuth authorization flow and provide the token here.

        • connector_id: optional "connector_dropbox" or "connector_gmail" or "connector_googlecalendar" or 5 more

          Identifier for service connectors, like those available in ChatGPT. One of server_url, connector_id, or tunnel_id must be provided. Learn more about service connectors here.

          Currently supported connector_id values are:

          • Dropbox: connector_dropbox

          • Gmail: connector_gmail

          • Google Calendar: connector_googlecalendar

          • Google Drive: connector_googledrive

          • Microsoft Teams: connector_microsoftteams

          • Outlook Calendar: connector_outlookcalendar

          • Outlook Email: connector_outlookemail

          • SharePoint: connector_sharepoint

          • "connector_dropbox"

          • "connector_gmail"

          • "connector_googlecalendar"

          • "connector_googledrive"

          • "connector_microsoftteams"

          • "connector_outlookcalendar"

          • "connector_outlookemail"

          • "connector_sharepoint"

        • defer_loading: optional boolean

          Whether this MCP tool is deferred and discovered via tool search.

        • headers: optional map[string]

          Optional HTTP headers to send to the MCP server. Use for authentication or other purposes.

        • require_approval: optional object { always, never } or "always" or "never"

          Specify which of the MCP server's tools require approval.

          • MCP tool approval filter: object { always, never }

            Specify which of the MCP server's tools require approval. Can be always, never, or a filter object associated with tools that require approval.

            • always: optional object { read_only, tool_names }

              A filter object to specify which tools are allowed.

              • read_only: optional boolean

                Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

              • tool_names: optional array of string

                List of allowed tool names.

            • never: optional object { read_only, tool_names }

              A filter object to specify which tools are allowed.

              • read_only: optional boolean

                Indicates whether or not a tool modifies data or is read-only. If an MCP server is annotated with readOnlyHint, it will match this filter.

              • tool_names: optional array of string

                List of allowed tool names.

          • MCP tool approval setting: "always" or "never"

            Specify a single approval policy for all tools. One of always or never. When set to always, all tools will require approval. When set to never, all tools will not require approval.

            • "always"

            • "never"

        • server_description: optional string

          Optional description of the MCP server, used to provide more context.

        • server_url: optional string

          The URL for the MCP server. One of server_url, connector_id, or tunnel_id must be provided.

        • tunnel_id: optional string

          The Secure MCP Tunnel ID to use instead of a direct server URL. One of server_url, connector_id, or tunnel_id must be provided.

    • tracing: optional "auto" or object { group_id, metadata, workflow_name }

      Realtime API can write session traces to the Traces Dashboard. Set to null to disable tracing. Once tracing is enabled for a session, the configuration cannot be modified.

      auto will create a trace for the session with default values for the workflow name, group id, and metadata.

      • auto: "auto"

        Enables tracing and sets default values for tracing configuration options. Always auto.

      • Tracing Configuration: object { group_id, metadata, workflow_name }

        Granular configuration for tracing.

        • group_id: optional string

          The group id to attach to this trace to enable filtering and grouping in the Traces Dashboard.

        • metadata: optional unknown

          The arbitrary metadata to attach to this trace to enable filtering in the Traces Dashboard.

        • workflow_name: optional string

          The name of the workflow to attach to this trace. This is used to name the trace in the Traces Dashboard.

    • truncation: optional "auto" or "disabled" or RealtimeTruncationRetentionRatio

      When the number of tokens in a conversation exceeds the model's input token limit, the conversation be truncated, meaning messages (starting from the oldest) will not be included in the model's context. A 32k context model with 4,096 max output tokens can only include 28,224 tokens in the context before truncation occurs.

      Clients can configure truncation behavior to truncate with a lower max token limit, which is an effective way to control token usage and cost.

      Truncation will reduce the number of cached tokens on the next turn (busting the cache), since messages are dropped from the beginning of the context. However, clients can also configure truncation to retain messages up to a fraction of the maximum context size, which will reduce the need for future truncations and thus improve the cache rate.

      Truncation can be disabled entirely, which means the server will never truncate but would instead return an error if the conversation exceeds the model's input token limit.

      • RealtimeTruncationStrategy: "auto" or "disabled"

        The truncation strategy to use for the session. auto is the default truncation strategy. disabled will disable truncation and emit errors when the conversation exceeds the input token limit.

        • "auto"

        • "disabled"

      • realtime_truncation_retention_ratio: object { retention_ratio, type, token_limits }

        Retain a fraction of the conversation tokens when the conversation exceeds the input token limit. This allows you to amortize truncations across multiple turns, which can help improve cached token usage.

        • retention_ratio: number

          Fraction of post-instruction conversation tokens to retain (0.0 - 1.0) when the conversation exceeds the input token limit. Setting this to 0.8 means that messages will be dropped until 80% of the maximum allowed tokens are used. This helps reduce the frequency of truncations and improve cache rates.

        • type: "retention_ratio"

          Use retention ratio truncation.

        • token_limits: optional object { post_instructions }

          Optional custom token limits for this truncation strategy. If not provided, the model's default token limits will be used.

          • post_instructions: optional number

            Maximum tokens allowed in the conversation after instructions (which including tool definitions). For example, setting this to 5,000 would mean that truncation would occur when the conversation exceeds 5,000 tokens after instructions. This cannot be higher than the model's context window size minus the maximum output tokens.

Realtime Transcription Session Create Response

  • realtime_transcription_session_create_response: object { id, object, type, 3 more }

    A Realtime transcription session configuration object.

    • id: string

      Unique identifier for the session that looks like sess_1234567890abcdef.

    • object: string

      The object type. Always realtime.transcription_session.

    • type: "transcription"

      The type of session. Always transcription for transcription sessions.

    • audio: optional object { input }

      Configuration for input audio for the session.

      • input: optional object { format, noise_reduction, transcription, turn_detection }

        • format: optional object { rate, type } or object { type } or object { type }

          The PCM audio format. Only a 24kHz sample rate is supported.

          • audio/pcm: object { rate, type }

            The PCM audio format. Only a 24kHz sample rate is supported.

            • rate: optional 24000

              The sample rate of the audio. Always 24000.

              • 24000

            • type: optional "audio/pcm"

              The audio format. Always audio/pcm.

              • "audio/pcm"

          • audio/pcmu: object { type }

            The G.711 μ-law format.

            • type: optional "audio/pcmu"

              The audio format. Always audio/pcmu.

              • "audio/pcmu"

          • audio/pcma: object { type }

            The G.711 A-law format.

            • type: optional "audio/pcma"

              The audio format. Always audio/pcma.

              • "audio/pcma"

        • noise_reduction: optional object { type }

          Configuration for input audio noise reduction.

          • type: optional "near_field" or "far_field"

            Type of noise reduction. near_field is for close-talking microphones such as headphones, far_field is for far-field microphones such as laptop or conference room microphones.

            • "near_field"

            • "far_field"

        • transcription: optional object { delay, language, model, prompt }

          • delay: optional "minimal" or "low" or "medium" or 2 more

            Controls how long the model waits before emitting transcription text. Higher values can improve transcription accuracy at the cost of latency. Only supported with gpt-realtime-whisper in GA Realtime sessions.

            • "minimal"

            • "low"

            • "medium"

            • "high"

            • "xhigh"

          • language: optional string

            The language of the input audio. Supplying the input language in ISO-639-1 (e.g. en) format will improve accuracy and latency.

          • model: optional string or "whisper-1" or "gpt-4o-mini-transcribe" or "gpt-4o-mini-transcribe-2025-12-15" or 3 more

            The model to use for transcription. Current options are whisper-1, gpt-4o-mini-transcribe, gpt-4o-mini-transcribe-2025-12-15, gpt-4o-transcribe, gpt-4o-transcribe-diarize, and gpt-realtime-whisper. Use gpt-4o-transcribe-diarize when you need diarization with speaker labels.

            • "whisper-1"

            • "gpt-4o-mini-transcribe"

            • "gpt-4o-mini-transcribe-2025-12-15"

            • "gpt-4o-transcribe"

            • "gpt-4o-transcribe-diarize"

            • "gpt-realtime-whisper"

          • prompt: optional string

            An optional text to guide the model's style or continue a previous audio segment. For whisper-1, the prompt is a list of keywords. For gpt-4o-transcribe models (excluding gpt-4o-transcribe-diarize), the prompt is a free text string, for example "expect words related to technology". Prompt is not supported with gpt-realtime-whisper in GA Realtime sessions.

        • turn_detection: optional object { prefix_padding_ms, silence_duration_ms, threshold, type }

          Configuration for turn detection. Can be set to null to turn off. Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech. For gpt-realtime-whisper, this must be null; VAD is not supported.

          • prefix_padding_ms: optional number

            Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

          • silence_duration_ms: optional number

            Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

          • threshold: optional number

            Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

          • type: optional string

            Type of turn detection, only server_vad is currently supported.

    • expires_at: optional number

      Expiration timestamp for the session, in seconds since epoch.

    • include: optional array of "item.input_audio_transcription.logprobs"

      Additional fields to include in server outputs.

      • item.input_audio_transcription.logprobs: Include logprobs for input audio transcription.

      • "item.input_audio_transcription.logprobs"

Realtime Transcription Session Turn Detection

  • realtime_transcription_session_turn_detection: object { prefix_padding_ms, silence_duration_ms, threshold, type }

    Configuration for turn detection. Can be set to null to turn off. Server VAD means that the model will detect the start and end of speech based on audio volume and respond at the end of user speech. For gpt-realtime-whisper, this must be null; VAD is not supported.

    • prefix_padding_ms: optional number

      Amount of audio to include before the VAD detected speech (in milliseconds). Defaults to 300ms.

    • silence_duration_ms: optional number

      Duration of silence to detect speech stop (in milliseconds). Defaults to 500ms. With shorter values the model will respond more quickly, but may jump in on short pauses from the user.

    • threshold: optional number

      Activation threshold for VAD (0.0 to 1.0), this defaults to 0.5. A higher threshold will require louder audio to activate the model, and thus might perform better in noisy environments.

    • type: optional string

      Type of turn detection, only server_vad is currently supported.