{ "aboutHtml": "

GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It accepts audio with optional text guidance and produces recognized text. It is a distinct model from GPT-4o Transcribe, which uses GPT-4o, and from the newer GPT Transcribe profile with its separate keyword and multilingual-hint fields. The useful artifact here is a transcript of the recording’s original speech, prepared for search, review or a subsequent content-processing step.

Preparing the recording

OpenAI’s file-transcription guide accepts audio uploads up to 25 MB. Its current endpoint reference lists FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV and WebM, with sufficient filename and content-type metadata to identify the audio format. Larger recordings can be compressed or split into pieces of 25 MB or less. Preserve sentence boundaries when splitting, and keep each piece associated with its source recording and position so the text can later be assembled in the right order.

For voice notes, retain one identifiable transcript per recording and check any names or numbers before using the text to create tasks. For a spoken tutorial, compare the recognized wording with the original instructions before editing it for publication. A useful preparation brief identifies the spoken language and the terms that could otherwise be misheard. Transcribing the words is the first step; an interpretation, summary or translation can use the checked transcript afterward.

Prompt guidance and a single language hint

Existing GPT-4o Mini Transcribe integrations support a free-form prompt. OpenAI describes prompts for names, acronyms, recording-specific vocabulary, punctuation, capitalization, filler words and the preferred writing system. The endpoint reference says the prompt should match the audio language and can carry context from a preceding audio segment. Include relevant spellings or a short description of the subject rather than asking a transcription prompt to behave like an unrestricted chat instruction.

This model’s established language hint uses the singular language field. The producer reference documents an ISO 639-1 input-language code, such as en. The separate keywords and plural languages fields are documented for GPT Transcribe; they are not part of this Mini profile. For a series of recordings, retain the intended spoken-language setting with each input instead of interpreting it as a request to translate the result.

JSON result and optional token probabilities

The current endpoint reference documents JSON as the supported response format for GPT-4o Mini Transcribe. The recognized wording appears in the text field. When requested, token log probabilities can accompany the JSON response; these values describe the model’s token probabilities and can help identify material for closer review. They do not establish that a name, number or sentence matches the sound. Use the recording to decide whether a questionable word should be corrected.

The ordinary Mini transcription profile does not provide native SRT, VTT, verbose word-timestamp or speaker-labeled output through that JSON contract. OpenAI documents specialized models and formats for those capabilities. If the intended artifact is a subtitle track, keep the reviewed transcript available for a separate timing workflow rather than assuming that a text-only result already contains caption boundaries.

Streaming file output and documented modes

The producer’s speech-to-text guide explicitly says existing GPT-4o Mini Transcribe integrations support file streaming. A completed recording can produce partial transcript text followed by a final result while it is processed. Keep the final transcript as the completed artifact; the appearance of partial text does not mean the file workflow is continuously receiving live microphone audio. The model card separately lists transcription and Realtime endpoint families. Those producer interfaces do not imply that every session field from a different transcription model applies here.

Context, output allowance and useful checks

OpenAI’s current model specification lists a 16,000-token context window, a maximum output of 2,000 tokens and a knowledge cutoff of June 1, 2024. These are model-specification figures, not a universal duration in minutes for an audio recording. The file-size rule and a token allowance describe different constraints. For extended material, prepare the recording around the documented file workflow, carry relevant context between pieces and check that the final text includes the ending of the source.

A useful Mini Transcribe result preserves the wording and its relationship to the recording, with any optional probability data kept separate from editorial corrections. Review unfamiliar vocabulary, accents, background noise, language changes and overlapping speech with the original audio available. When revising text for readability, distinguish that editorial version from the recognized transcript so that later users can tell which wording came from the recording and which was changed during preparation.

", "aboutText": "GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It accepts audio with optional text guidance and produces recognized text. It is a distinct model from GPT-4o Transcribe, which uses GPT-4o, and from the newer GPT Transcribe profile with its separate keyword and multilingual-hint fields. The useful artifact here is a transcript of the recording’s original speech, prepared for search, review or a subsequent content-processing step.\n\n### Preparing the recording\n\nOpenAI’s file-transcription guide accepts audio uploads up to 25 MB. Its current endpoint reference lists FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV and WebM, with sufficient filename and content-type metadata to identify the audio format. Larger recordings can be compressed or split into pieces of 25 MB or less. Preserve sentence boundaries when splitting, and keep each piece associated with its source recording and position so the text can later be assembled in the right order.\n\nFor voice notes, retain one identifiable transcript per recording and check any names or numbers before using the text to create tasks. For a spoken tutorial, compare the recognized wording with the original instructions before editing it for publication. A useful preparation brief identifies the spoken language and the terms that could otherwise be misheard. Transcribing the words is the first step; an interpretation, summary or translation can use the checked transcript afterward.\n\n### Prompt guidance and a single language hint\n\nExisting GPT-4o Mini Transcribe integrations support a free-form prompt. OpenAI describes prompts for names, acronyms, recording-specific vocabulary, punctuation, capitalization, filler words and the preferred writing system. The endpoint reference says the prompt should match the audio language and can carry context from a preceding audio segment. Include relevant spellings or a short description of the subject rather than asking a transcription prompt to behave like an unrestricted chat instruction.\n\nThis model’s established language hint uses the singular language field. The producer reference documents an ISO 639-1 input-language code, such as en. The separate keywords and plural languages fields are documented for GPT Transcribe; they are not part of this Mini profile. For a series of recordings, retain the intended spoken-language setting with each input instead of interpreting it as a request to translate the result.\n\n### JSON result and optional token probabilities\n\nThe current endpoint reference documents JSON as the supported response format for GPT-4o Mini Transcribe. The recognized wording appears in the text field. When requested, token log probabilities can accompany the JSON response; these values describe the model’s token probabilities and can help identify material for closer review. They do not establish that a name, number or sentence matches the sound. Use the recording to decide whether a questionable word should be corrected.\n\nThe ordinary Mini transcription profile does not provide native SRT, VTT, verbose word-timestamp or speaker-labeled output through that JSON contract. OpenAI documents specialized models and formats for those capabilities. If the intended artifact is a subtitle track, keep the reviewed transcript available for a separate timing workflow rather than assuming that a text-only result already contains caption boundaries.\n\n### Streaming file output and documented modes\n\nThe producer’s speech-to-text guide explicitly says existing GPT-4o Mini Transcribe integrations support file streaming. A completed recording can produce partial transcript text followed by a final result while it is processed. Keep the final transcript as the completed artifact; the appearance of partial text does not mean the file workflow is continuously receiving live microphone audio. The model card separately lists transcription and Realtime endpoint families. Those producer interfaces do not imply that every session field from a different transcription model applies here.\n\n### Context, output allowance and useful checks\n\nOpenAI’s current model specification lists a 16,000-token context window, a maximum output of 2,000 tokens and a knowledge cutoff of June 1, 2024. These are model-specification figures, not a universal duration in minutes for an audio recording. The file-size rule and a token allowance describe different constraints. For extended material, prepare the recording around the documented file workflow, carry relevant context between pieces and check that the final text includes the ending of the source.\n\nA useful Mini Transcribe result preserves the wording and its relationship to the recording, with any optional probability data kept separate from editorial corrections. Review unfamiliar vocabulary, accents, background noise, language changes and overlapping speech with the original audio available. When revising text for readability, distinguish that editorial version from the recognized transcript so that later users can tell which wording came from the recording and which was changed during preparation.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "22564d12857b671dbca3dfbf72525aec35e4ffcce4634584f8352f9f6b600fdb", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "
O

OpenAI: GPT-4o Mini Transcribe

OpenAI /openai/gpt-4o-mini-transcribe

Overview

GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It turns audio into a JSON transcript, supports recording-specific text prompts and streamed file results, and can return token log probabilities for targeted transcript review.

Accepted inputs
Audio
Results
Text
About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It accepts audio with optional text guidance and produces recognized text. It is a distinct model from GPT-4o Transcribe, which uses GPT-4o, and from the newer GPT Transcribe profile with its separate keyword and multilingual-hint fields. The useful artifact here is a transcript of the recording’s original speech, prepared for search, review or a subsequent content-processing step.

Preparing the recording

OpenAI’s file-transcription guide accepts audio uploads up to 25 MB. Its current endpoint reference lists FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV and WebM, with sufficient filename and content-type metadata to identify the audio format. Larger recordings can be compressed or split into pieces of 25 MB or less. Preserve sentence boundaries when splitting, and keep each piece associated with its source recording and position so the text can later be assembled in the right order.

For voice notes, retain one identifiable transcript per recording and check any names or numbers before using the text to create tasks. For a spoken tutorial, compare the recognized wording with the original instructions before editing it for publication. A useful preparation brief identifies the spoken language and the terms that could otherwise be misheard. Transcribing the words is the first step; an interpretation, summary or translation can use the checked transcript afterward.

Prompt guidance and a single language hint

Existing GPT-4o Mini Transcribe integrations support a free-form prompt. OpenAI describes prompts for names, acronyms, recording-specific vocabulary, punctuation, capitalization, filler words and the preferred writing system. The endpoint reference says the prompt should match the audio language and can carry context from a preceding audio segment. Include relevant spellings or a short description of the subject rather than asking a transcription prompt to behave like an unrestricted chat instruction.

This model’s established language hint uses the singular language field. The producer reference documents an ISO 639-1 input-language code, such as en. The separate keywords and plural languages fields are documented for GPT Transcribe; they are not part of this Mini profile. For a series of recordings, retain the intended spoken-language setting with each input instead of interpreting it as a request to translate the result.

JSON result and optional token probabilities

The current endpoint reference documents JSON as the supported response format for GPT-4o Mini Transcribe. The recognized wording appears in the text field. When requested, token log probabilities can accompany the JSON response; these values describe the model’s token probabilities and can help identify material for closer review. They do not establish that a name, number or sentence matches the sound. Use the recording to decide whether a questionable word should be corrected.

The ordinary Mini transcription profile does not provide native SRT, VTT, verbose word-timestamp or speaker-labeled output through that JSON contract. OpenAI documents specialized models and formats for those capabilities. If the intended artifact is a subtitle track, keep the reviewed transcript available for a separate timing workflow rather than assuming that a text-only result already contains caption boundaries.

Streaming file output and documented modes

The producer’s speech-to-text guide explicitly says existing GPT-4o Mini Transcribe integrations support file streaming. A completed recording can produce partial transcript text followed by a final result while it is processed. Keep the final transcript as the completed artifact; the appearance of partial text does not mean the file workflow is continuously receiving live microphone audio. The model card separately lists transcription and Realtime endpoint families. Those producer interfaces do not imply that every session field from a different transcription model applies here.

Context, output allowance and useful checks

OpenAI’s current model specification lists a 16,000-token context window, a maximum output of 2,000 tokens and a knowledge cutoff of June 1, 2024. These are model-specification figures, not a universal duration in minutes for an audio recording. The file-size rule and a token allowance describe different constraints. For extended material, prepare the recording around the documented file workflow, carry relevant context between pieces and check that the final text includes the ending of the source.

A useful Mini Transcribe result preserves the wording and its relationship to the recording, with any optional probability data kept separate from editorial corrections. Review unfamiliar vocabulary, accents, background noise, language changes and overlapping speech with the original audio available. When revising text for readability, distinguish that editorial version from the recognized transcript so that later users can tell which wording came from the recording and which was changed during preparation.

Available in Ailoot · inputs & methods

Ailoot Ultra · Transcription POST /v1/audio/transcriptions
Input types
audio
Output types
text
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
openai/gpt-4o-mini-transcribe
Default—
Fieldinput_audioTypeobjectRequirementRequiredValues & limitsDefault—
Method parameters and lifecycle
ParameterContract
Parametercontent_typeContractapplication/json
ParameterrequiredContractmodel, input_audio
Parametermaterial_typesContractaudio
Parametermaterial_conditionsContractThe example reads your actual nonempty PCM WAV file, encodes the complete file bytes as base64 and sends input_audio.format as wav.

Lifecycle: synchronous_result_then_owned_library

GET /v1/media/tasks/{task_id} · exact task_id from an existing owned media record

GET /v1/media/assets/{asset_id}/content

GET /v1/media/assets

GET /v1/media/assets/{asset_id}

Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode
Not specified When fast mode is selected
Flex mode
Not specified When flex mode is selected
Maximum reasoning effort
Not specified When maximum reasoning effort uses the default adjustment

Ailoot rates

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleBilling unitCharge
ConfigurationBase token ratesRate scheduleBase rateBilling unit—Charge
Input · Input tokens — / 1K tokens
Output · Output tokens — / 1K tokens
Input · Cache read — / 1K tokens
Input · Cache write — / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
Configurationinput tokenRate schedule—Billing unit/ 1K tokensCharge
Input · input token $0.0025 / 1K tokens
Configurationoutput tokenRate schedule—Billing unit/ 1K tokensCharge
Output · output token $0.01 / 1K tokens

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →

Example requests

Ailoot Ultra · Transcription

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_AUDIO_FILE:?Set the path to your actual PCM WAV recording}"\nexport AILOOT_AUDIO_FILE\npython3 - <<'PY_REQUEST'\nimport base64, io, json, os, wave\nfrom pathlib import Path\naudio = Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4] == b'RIFF' and audio[8:12] == b'WAVE'):\n    raise SystemExit('Supply your actual PCM WAV audio file')\nwith wave.open(io.BytesIO(audio), 'rb') as recording:\n    if recording.getcomptype() != 'NONE' or recording.getnframes() <= 0:\n        raise SystemExit('Supply a nonempty PCM WAV recording')\nrequest = {'model': 'openai/gpt-4o-mini-transcribe', 'input_audio': {'data': base64.b64encode(audio).decode('ascii'), 'format': 'wav'}}\nPath('request.json').write_text(json.dumps(request, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o response.bin\n# Retain the complete original response, headers and actual X-Client-Request-ID.\n# Read existing owned records for recovery; do not resubmit an uncertain operation.\n

Receive and save results

Ailoot Ultra · Transcription

Complete original response body, status and forwarded headers. No universal JSON shape, text field or task envelope is added by this handler.

Keep all completed results

Keep every complete returned response, file and content part.

Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.

If the original response needs checking

Retain the actual X-Client-Request-ID and complete response. Read the original owned assets and their exact signed content URLs; never resubmit an uncertain creation.

Response fields
FieldType
Fieldtext (when present)Type
Fieldusage (when present)Type
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionThe JSON body or explicit canonical model ID is invalid.Next stepRetain the response and correct the indicated request field.
Response401 · authentication_errorConditionThe Ailoot API key is invalid.Next stepUse your authorized Ailoot API key.
Response403 · permission_errorConditionThe key group does not admit this method.Next stepRetain the returned error details.
Response404 · not_found_errorConditionThe selected method has no admitted route.Next stepRetain the returned error details.
Response422 · price_unavailableConditionThe accepted tariff cannot obtain its required input quantity or bound.Next stepRetain the response; use the input representation required by the accepted tariff.
Response503 · ConditionRequired storage, route, method price or reservation is unavailable.Next stepKeep the complete original response. An uncertain reservation is not permission to resubmit.
Response502 · server_errorConditionThe original result, usage or settlement requires checking.Next stepRetain the actual request ID and original response. Read the existing owned record; do not resubmit creation.

Aliases

Model aliases

Ailoot Ultra openai/gpt-4o-mini-transcribe

Method path aliases

Use the method paths shown above.

Related caller IDs

openai/gpt-4o-mini-transcribe

", "method_identity": { "effective_request_model_id": "openai/gpt-4o-mini-transcribe", "fmt": "transcriptions", "http": "POST", "path": "/v1/audio/transcriptions", "request_model_id": "openai/gpt-4o-mini-transcribe", "route_label": "Ultra" }, "method_key": "Ultra|transcriptions|/v1/audio/transcriptions|openai%2Fgpt-4o-mini-transcribe", "ordinal": 0, "rawMethod": { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "errors": [ { "code": null, "condition": "The JSON body or explicit canonical model ID is invalid.", "http_status": 400, "recovery": "Retain the response and correct the indicated request field.", "type": "invalid_request_error" }, { "code": null, "condition": "The Ailoot API key is invalid.", "http_status": 401, "recovery": "Use your authorized Ailoot API key.", "type": "authentication_error" }, { "code": null, "condition": "The key group does not admit this method.", "http_status": 403, "recovery": "Retain the returned error details.", "type": "permission_error" }, { "code": null, "condition": "The selected method has no admitted route.", "http_status": 404, "recovery": "Retain the returned error details.", "type": "not_found_error" }, { "code": null, "condition": "The accepted tariff cannot obtain its required input quantity or bound.", "http_status": 422, "recovery": "Retain the response; use the input representation required by the accepted tariff.", "type": "price_unavailable" }, { "code": null, "condition": "Required storage, route, method price or reservation is unavailable.", "http_status": 503, "recovery": "Keep the complete original response. An uncertain reservation is not permission to resubmit.", "type": null }, { "code": null, "condition": "The original result, usage or settlement requires checking.", "http_status": 502, "operation_id_source": "actual X-Client-Request-ID", "recovery": "Retain the actual request ID and original response. Read the existing owned record; do not resubmit creation.", "response_headers": { "x-should-retry": "false" }, "type": "server_error" } ], "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed content URL" } ], "dedicated_method_polling_or_cancel": false, "mode": "synchronous_result_then_owned_library", "original_POST_adds_task_envelope": false, "status": [ { "http": "GET", "id_source": "exact task_id from an existing owned media record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Retain the actual X-Client-Request-ID and complete response. Read the original owned assets and their exact signed content URLs; never resubmit an uncertain creation." }, "outputs": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "family": "text", "fields": [ { "description": null, "name": "text (when present)", "type": null }, { "description": null, "name": "usage (when present)", "type": null } ], "result": "Complete original response body, status and forwarded headers. No universal JSON shape, text field or task envelope is added by this handler." }, "parameters": [ { "default_known": false, "enum": [ "openai/gpt-4o-mini-transcribe" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "default_known": false, "enum_known": false, "name": "input_audio", "required": true, "type": "object" } ], "request_schema": { "properties": { "input_audio": { "properties": { "data": { "contentEncoding": "base64", "type": "string" }, "format": { "type": "string" } }, "required": [ "data", "format" ], "type": "object" }, "model": { "enum": [ "openai/gpt-4o-mini-transcribe" ], "type": "string" } }, "required": [ "model", "input_audio" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": true, "body": { "input_audio": { "data": "${BASE64_ACTUAL_WAV_BYTES}", "format": "wav" }, "model": "openai/gpt-4o-mini-transcribe" }, "content_type": "application/json" } }, "input": { "content_type": "application/json", "material_conditions": [ "The example reads your actual nonempty PCM WAV file, encodes the complete file bytes as base64 and sends input_audio.format as wav." ], "material_types": [ "audio" ], "required": [ "model", "input_audio" ] }, "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed content URL" } ], "dedicated_method_polling_or_cancel": false, "mode": "synchronous_result_then_owned_library", "original_POST_adds_task_envelope": false, "status": [ { "http": "GET", "id_source": "exact task_id from an existing owned media record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Retain the actual X-Client-Request-ID and complete response. Read the original owned assets and their exact signed content URLs; never resubmit an uncertain creation." }, "method": { "http": "POST", "path": "/v1/audio/transcriptions" }, "output": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "family": "text", "fields": [ { "description": null, "name": "text (when present)", "type": null }, { "description": null, "name": "usage (when present)", "type": null } ], "result": "Complete original response body, status and forwarded headers. No universal JSON shape, text field or task envelope is added by this handler." } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "input token", "units": [ { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "output token", "units": [ { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_AUDIO_FILE:?Set the path to your actual PCM WAV recording}\"\nexport AILOOT_AUDIO_FILE\npython3 - <<'PY_REQUEST'\nimport base64, io, json, os, wave\nfrom pathlib import Path\naudio = Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4] == b'RIFF' and audio[8:12] == b'WAVE'):\n raise SystemExit('Supply your actual PCM WAV audio file')\nwith wave.open(io.BytesIO(audio), 'rb') as recording:\n if recording.getcomptype() != 'NONE' or recording.getnframes() <= 0:\n raise SystemExit('Supply a nonempty PCM WAV recording')\nrequest = {'model': 'openai/gpt-4o-mini-transcribe', 'input_audio': {'data': base64.b64encode(audio).decode('ascii'), 'format': 'wav'}}\nPath('request.json').write_text(json.dumps(request, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o response.bin\n# Retain the complete original response, headers and actual X-Client-Request-ID.\n# Read existing owned records for recovery; do not resubmit an uncertain operation.\n", "label": "curl · actual WAV file and complete original result", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "transcriptions", "hints": [ "The example reads your actual nonempty PCM WAV file, encodes the complete file bytes as base64 and sends input_audio.format as wav.", "The format value in the example is the documented recipe; no model-specific format list, limit or default is asserted.", "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again." ], "http": "POST", "in": null, "input": [ "audio" ], "label": "Transcription", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "audio_transcriptions", "out": null, "output": [ "text" ], "path": "/v1/audio/transcriptions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "input_token": 2.5e-06, "output_token": 1e-05 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "input token", "units": [ { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "output token", "units": [ { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ] } ], "request_model_id": "openai/gpt-4o-mini-transcribe", "route_label": "Ultra", "status_reads": [ "GET /v1/media/tasks/{task_id}" ], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ] } } ], "model_id": "openai/gpt-4o-mini-transcribe", "nativeProvenance": { "input_facts_canonical_sha256": "f9dcc11955dd60edbde0074018d2f8a524f48212f2d6bba4c78832ba5131a575", "input_html_sha256": "055c8ad5ba5565ec72407be28feae21ac65263326abbe1619f91f1c7ef2ccda5", "private_audit_sha256": "efccd514471a47c7d0fd27cfe0c0b61a510283c80cb4418b6c75fc43d0dcd0b8", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/en/openai-gpt-4o-mini-transcribe/", "description": "GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It turns audio into a JSON transcript, supports recording-specific text prompts and streamed file results, and can return token log probabilities for targeted transcript review.", "locale": "en", "md_url": "https://ailoot.ai/models/en/openai-gpt-4o-mini-transcribe/index.md", "title": "OpenAI: GPT-4o Mini Transcribe — Ailoot", "url": "https://ailoot.ai/models/en/openai-gpt-4o-mini-transcribe/" }, "rawModel": { "about": "GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It accepts audio with optional text guidance and produces recognized text. It is a distinct model from GPT-4o Transcribe, which uses GPT-4o, and from the newer GPT Transcribe profile with its separate keyword and multilingual-hint fields. The useful artifact here is a transcript of the recording’s original speech, prepared for search, review or a subsequent content-processing step.\n\n### Preparing the recording\n\nOpenAI’s file-transcription guide accepts audio uploads up to 25 MB. Its current endpoint reference lists FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV and WebM, with sufficient filename and content-type metadata to identify the audio format. Larger recordings can be compressed or split into pieces of 25 MB or less. Preserve sentence boundaries when splitting, and keep each piece associated with its source recording and position so the text can later be assembled in the right order.\n\nFor voice notes, retain one identifiable transcript per recording and check any names or numbers before using the text to create tasks. For a spoken tutorial, compare the recognized wording with the original instructions before editing it for publication. A useful preparation brief identifies the spoken language and the terms that could otherwise be misheard. Transcribing the words is the first step; an interpretation, summary or translation can use the checked transcript afterward.\n\n### Prompt guidance and a single language hint\n\nExisting GPT-4o Mini Transcribe integrations support a free-form prompt. OpenAI describes prompts for names, acronyms, recording-specific vocabulary, punctuation, capitalization, filler words and the preferred writing system. The endpoint reference says the prompt should match the audio language and can carry context from a preceding audio segment. Include relevant spellings or a short description of the subject rather than asking a transcription prompt to behave like an unrestricted chat instruction.\n\nThis model’s established language hint uses the singular language field. The producer reference documents an ISO 639-1 input-language code, such as en. The separate keywords and plural languages fields are documented for GPT Transcribe; they are not part of this Mini profile. For a series of recordings, retain the intended spoken-language setting with each input instead of interpreting it as a request to translate the result.\n\n### JSON result and optional token probabilities\n\nThe current endpoint reference documents JSON as the supported response format for GPT-4o Mini Transcribe. The recognized wording appears in the text field. When requested, token log probabilities can accompany the JSON response; these values describe the model’s token probabilities and can help identify material for closer review. They do not establish that a name, number or sentence matches the sound. Use the recording to decide whether a questionable word should be corrected.\n\nThe ordinary Mini transcription profile does not provide native SRT, VTT, verbose word-timestamp or speaker-labeled output through that JSON contract. OpenAI documents specialized models and formats for those capabilities. If the intended artifact is a subtitle track, keep the reviewed transcript available for a separate timing workflow rather than assuming that a text-only result already contains caption boundaries.\n\n### Streaming file output and documented modes\n\nThe producer’s speech-to-text guide explicitly says existing GPT-4o Mini Transcribe integrations support file streaming. A completed recording can produce partial transcript text followed by a final result while it is processed. Keep the final transcript as the completed artifact; the appearance of partial text does not mean the file workflow is continuously receiving live microphone audio. The model card separately lists transcription and Realtime endpoint families. Those producer interfaces do not imply that every session field from a different transcription model applies here.\n\n### Context, output allowance and useful checks\n\nOpenAI’s current model specification lists a 16,000-token context window, a maximum output of 2,000 tokens and a knowledge cutoff of June 1, 2024. These are model-specification figures, not a universal duration in minutes for an audio recording. The file-size rule and a token allowance describe different constraints. For extended material, prepare the recording around the documented file workflow, carry relevant context between pieces and check that the final text includes the ending of the source.\n\nA useful Mini Transcribe result preserves the wording and its relationship to the recording, with any optional probability data kept separate from editorial corrections. Review unfamiliar vocabulary, accents, background noise, language changes and overlapping speech with the original audio available. When revising text for readability, distinguish that editorial version from the recognized transcript so that later users can tell which wording came from the recording and which was changed during preparation.", "alias_of": null, "aliases": [], "author": "OpenAI", "capabilities": { "context_length": 128000, "manufacturer_input": [ "audio" ], "manufacturer_output": [ "transcription" ] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ], "summary": "Full configuration pricing in details." } ], "ctx": null, "formats": [ "transcriptions" ], "id": "openai/gpt-4o-mini-transcribe", "input": [ "audio" ], "kind": "audio", "max_out": null, "methods": [ { "available": null, "cache_read": null, "cache_write": null, "cache_write_1h": null, "contract": { "data": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "errors": [ { "code": null, "condition": "The JSON body or explicit canonical model ID is invalid.", "http_status": 400, "recovery": "Retain the response and correct the indicated request field.", "type": "invalid_request_error" }, { "code": null, "condition": "The Ailoot API key is invalid.", "http_status": 401, "recovery": "Use your authorized Ailoot API key.", "type": "authentication_error" }, { "code": null, "condition": "The key group does not admit this method.", "http_status": 403, "recovery": "Retain the returned error details.", "type": "permission_error" }, { "code": null, "condition": "The selected method has no admitted route.", "http_status": 404, "recovery": "Retain the returned error details.", "type": "not_found_error" }, { "code": null, "condition": "The accepted tariff cannot obtain its required input quantity or bound.", "http_status": 422, "recovery": "Retain the response; use the input representation required by the accepted tariff.", "type": "price_unavailable" }, { "code": null, "condition": "Required storage, route, method price or reservation is unavailable.", "http_status": 503, "recovery": "Keep the complete original response. An uncertain reservation is not permission to resubmit.", "type": null }, { "code": null, "condition": "The original result, usage or settlement requires checking.", "http_status": 502, "operation_id_source": "actual X-Client-Request-ID", "recovery": "Retain the actual request ID and original response. Read the existing owned record; do not resubmit creation.", "response_headers": { "x-should-retry": "false" }, "type": "server_error" } ], "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed content URL" } ], "dedicated_method_polling_or_cancel": false, "mode": "synchronous_result_then_owned_library", "original_POST_adds_task_envelope": false, "status": [ { "http": "GET", "id_source": "exact task_id from an existing owned media record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Retain the actual X-Client-Request-ID and complete response. Read the original owned assets and their exact signed content URLs; never resubmit an uncertain creation." }, "outputs": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "family": "text", "fields": [ { "description": null, "name": "text (when present)", "type": null }, { "description": null, "name": "usage (when present)", "type": null } ], "result": "Complete original response body, status and forwarded headers. No universal JSON shape, text field or task envelope is added by this handler." }, "parameters": [ { "default_known": false, "enum": [ "openai/gpt-4o-mini-transcribe" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "default_known": false, "enum_known": false, "name": "input_audio", "required": true, "type": "object" } ], "request_schema": { "properties": { "input_audio": { "properties": { "data": { "contentEncoding": "base64", "type": "string" }, "format": { "type": "string" } }, "required": [ "data", "format" ], "type": "object" }, "model": { "enum": [ "openai/gpt-4o-mini-transcribe" ], "type": "string" } }, "required": [ "model", "input_audio" ], "type": "object" }, "schema_template": { "actual_file_preparation_required": true, "body": { "input_audio": { "data": "${BASE64_ACTUAL_WAV_BYTES}", "format": "wav" }, "model": "openai/gpt-4o-mini-transcribe" }, "content_type": "application/json" } }, "input": { "content_type": "application/json", "material_conditions": [ "The example reads your actual nonempty PCM WAV file, encodes the complete file bytes as base64 and sends input_audio.format as wav." ], "material_types": [ "audio" ], "required": [ "model", "input_audio" ] }, "lifecycle": { "assets": [ { "http": "GET", "path": "/v1/media/assets" }, { "http": "GET", "path": "/v1/media/assets/{asset_id}" } ], "content": [ { "http": "GET", "path": "/v1/media/assets/{asset_id}/content", "url_source": "exact returned signed content URL" } ], "dedicated_method_polling_or_cancel": false, "mode": "synchronous_result_then_owned_library", "original_POST_adds_task_envelope": false, "status": [ { "http": "GET", "id_source": "exact task_id from an existing owned media record", "path": "/v1/media/tasks/{task_id}" } ], "unknown": "Retain the actual X-Client-Request-ID and complete response. Read the original owned assets and their exact signed content URLs; never resubmit an uncertain creation." }, "method": { "http": "POST", "path": "/v1/audio/transcriptions" }, "output": { "all_results": "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.", "family": "text", "fields": [ { "description": null, "name": "text (when present)", "type": null }, { "description": null, "name": "usage (when present)", "type": null } ], "result": "Complete original response body, status and forwarded headers. No universal JSON shape, text field or task envelope is added by this handler." } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "input token", "units": [ { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "output token", "units": [ { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ] } ], "examples": [ { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_AUDIO_FILE:?Set the path to your actual PCM WAV recording}\"\nexport AILOOT_AUDIO_FILE\npython3 - <<'PY_REQUEST'\nimport base64, io, json, os, wave\nfrom pathlib import Path\naudio = Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4] == b'RIFF' and audio[8:12] == b'WAVE'):\n raise SystemExit('Supply your actual PCM WAV audio file')\nwith wave.open(io.BytesIO(audio), 'rb') as recording:\n if recording.getcomptype() != 'NONE' or recording.getnframes() <= 0:\n raise SystemExit('Supply a nonempty PCM WAV recording')\nrequest = {'model': 'openai/gpt-4o-mini-transcribe', 'input_audio': {'data': base64.b64encode(audio).decode('ascii'), 'format': 'wav'}}\nPath('request.json').write_text(json.dumps(request, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/transcriptions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o response.bin\n# Retain the complete original response, headers and actual X-Client-Request-ID.\n# Read existing owned records for recovery; do not resubmit an uncertain operation.\n", "label": "curl · actual WAV file and complete original result", "language": "bash" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "fmt": "transcriptions", "hints": [ "The example reads your actual nonempty PCM WAV file, encodes the complete file bytes as base64 and sends input_audio.format as wav.", "The format value in the example is the documented recipe; no model-specific format list, limit or default is asserted.", "Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again." ], "http": "POST", "in": null, "input": [ "audio" ], "label": "Transcription", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "native_endpoint": "audio_transcriptions", "out": null, "output": [ "text" ], "path": "/v1/audio/transcriptions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "per_request", "image_input_price": null, "image_output_price": null, "per_request_price": null, "unit_prices": { "input_token": 2.5e-06, "output_token": 1e-05 } }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "input token", "units": [ { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" } ] }, { "dimensions": { "quoted_unit": "/ 1K tokens" }, "label": "output token", "units": [ { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ] } ], "request_model_id": "openai/gpt-4o-mini-transcribe", "route_label": "Ultra", "status_reads": [ "GET /v1/media/tasks/{task_id}" ], "tiers": [], "units": [ { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "cached_input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "cached_input_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "generic_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "generic_cache_creation_token" }, { "amount": null, "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_1h_price", "label": "Cache write, 1 hour", "native_amount_usd_per_unit": null, "native_quantity": 1, "native_unit": "one_hour_cache_creation_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "one_hour_cache_creation_token" }, { "amount": "0.0025000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_token", "label": "input token", "native_amount_usd_per_unit": "0.0000025", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0.01000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_token", "label": "output token", "native_amount_usd_per_unit": "0.00001", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" } ] } ], "model_aliases": [ { "canonical_model_id": "openai/gpt-4o-mini-transcribe", "request_model_id": "openai/gpt-4o-mini-transcribe", "route_label": "Ultra" } ], "name": "OpenAI: GPT-4o Mini Transcribe", "price": { "in": null, "in_label": null, "out": 0.01, "out_label": "/ 1K tokens", "sort_in": null, "sort_out": 0.01, "varies": true }, "short": "GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It turns audio into a JSON transcript, supports recording-specific text prompts and streamed file results, and can return token log probabilities for targeted transcript review." }, "renderer_schema": { "method_order": "Original DTO methods order; every exact route/format/path/caller survives.", "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers." }, "representation": "exact-public-dto-and-native-renderer-html", "representationNotes": { "current_reference": { "about_json_pointer": "/aboutHtml", "methods": [ { "contract_json_pointer": "/methods/0/rawMethod/contract", "html_json_pointer": "/methods/0/html", "method_ordinal": 0 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "GPT-4o Mini Transcribe is OpenAI’s speech-to-text model powered by GPT-4o Mini. It turns audio into a JSON transcript, supports recording-specific text prompts and streamed file results, and can return token log probabilities for targeted transcript review.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "a0d7d43e251118e7ef8838427fbfed87d18195e9950ab9ac7c06c7dcec94c8fc", "renderer_sha256": "fed50305bd3bcc4980ac14df23640efb8c1bc1e819f9d53643d6f43e8974d3fe" }, "sourceRevision": "caac5b6671554aba4db864fb58c2dd0efabb36b64cfc77d3f2a446b265473d7d" }