Voxtral Mini 1.0 3B 2507 is Mistral AI’s compact text-and-audio understanding model built on the Ministral 3B language-model family. It accepts speech recordings and text instructions and produces text, including transcripts, translations, answers about the audio and structured summaries. The original Voxtral release positions this 3B model for compact or local applications, alongside the separate 24B Voxtral Small model. It is also distinct from the hosted transcription-optimised Voxtral Mini Transcribe service.
The model has a dedicated transcription mode that automatically predicts the source language by default and transcribes accordingly. Choose that mode when the required artifact is the spoken wording. Audio understanding instead permits a direct question or summary request about a recording, without requiring a separate speech-recognition stage followed by a text model. An interview can therefore produce a full transcript, a translation or an answer to a narrow question; specify which result is needed so those different tasks do not become one ambiguous brief.
The exact release card lists a 32K-token context and audio up to 30 minutes for transcription or 40 minutes for understanding. These are different task-specific duration statements, not a shared audio allowance for every operation. Keep the relevant recording, instructions and conversation within the intended task scope. The maker’s named languages are English, Spanish, French, Portuguese, Hindi, German, Dutch and Italian, with automatic language detection. For a translation or answer, distinguish the recording’s language from the required output language.
Several audio inputs in a message and audio across multiple user turns are supported. Give each recording a clear identifier when asking for comparisons. An original example might compare two spoken explanations and return which requirements changed, with a separate column for each source. A follow-up can ask for the detail behind one finding or supply another recording. The card also demonstrates text-only and audio-only use, allowing the model to work with the written question as well as the speech evidence.
For a meeting summary, supply the recording and request a useful structure such as topics, stated decisions and follow-up actions. Keep uncertain names, dates or responsibilities separate from facts that are clearly spoken. For an interview, ask for the statement that answers a particular question and enough surrounding context to make it understandable. These are task designs for a usable text artifact; they do not turn general audio understanding into a guarantee of diarisation, word timestamps or identification of an unnamed speaker.
The manufacturer describes function calling from spoken intent. In such a workflow, the application supplies function definitions, the model proposes the relevant call and arguments, and the application executes it and returns the observation. A voice request can therefore guide a lookup or another tool-assisted task, but the model’s proposed call is distinct from an executed action. Preserve the actual returned result when continuing the conversation so a later answer reflects what happened rather than what was requested.
The released usage card recommends temperature 0.0 for transcription, and temperature 0.2 with top_p 0.95 for audio understanding and chat. It states that system prompts are not supported in this flow, so put the task and response requirements in the user input. Its examples decode recordings into audio inputs and include MP3 material, but they do not establish an exhaustive codec list or a separate hard maximum output length. The 500-token generation setting in the examples remains an example budget.
Prepare a clear recording containing the material relevant to the task and specify whether you need original wording, a translation, an answer or a summary. The card also demonstrates batched inference with several conversations, a producer usage pattern separate from the identity of an individual result. Preserve the 3B 2507 edition and its transcription-versus-understanding duration guidance; the later Mini Transcribe 2 model’s diarisation, 13-language profile and longer recording support are separate producer features.
", "aboutText": "Voxtral Mini 1.0 3B 2507 is Mistral AI’s compact text-and-audio understanding model built on the Ministral 3B language-model family. It accepts speech recordings and text instructions and produces text, including transcripts, translations, answers about the audio and structured summaries. The original Voxtral release positions this 3B model for compact or local applications, alongside the separate 24B Voxtral Small model. It is also distinct from the hosted transcription-optimised Voxtral Mini Transcribe service.\n\nThe model has a dedicated transcription mode that automatically predicts the source language by default and transcribes accordingly. Choose that mode when the required artifact is the spoken wording. Audio understanding instead permits a direct question or summary request about a recording, without requiring a separate speech-recognition stage followed by a text model. An interview can therefore produce a full transcript, a translation or an answer to a narrow question; specify which result is needed so those different tasks do not become one ambiguous brief.\n\nThe exact release card lists a 32K-token context and audio up to 30 minutes for transcription or 40 minutes for understanding. These are different task-specific duration statements, not a shared audio allowance for every operation. Keep the relevant recording, instructions and conversation within the intended task scope. The maker’s named languages are English, Spanish, French, Portuguese, Hindi, German, Dutch and Italian, with automatic language detection. For a translation or answer, distinguish the recording’s language from the required output language.\n\nSeveral audio inputs in a message and audio across multiple user turns are supported. Give each recording a clear identifier when asking for comparisons. An original example might compare two spoken explanations and return which requirements changed, with a separate column for each source. A follow-up can ask for the detail behind one finding or supply another recording. The card also demonstrates text-only and audio-only use, allowing the model to work with the written question as well as the speech evidence.\n\nFor a meeting summary, supply the recording and request a useful structure such as topics, stated decisions and follow-up actions. Keep uncertain names, dates or responsibilities separate from facts that are clearly spoken. For an interview, ask for the statement that answers a particular question and enough surrounding context to make it understandable. These are task designs for a usable text artifact; they do not turn general audio understanding into a guarantee of diarisation, word timestamps or identification of an unnamed speaker.\n\nThe manufacturer describes function calling from spoken intent. In such a workflow, the application supplies function definitions, the model proposes the relevant call and arguments, and the application executes it and returns the observation. A voice request can therefore guide a lookup or another tool-assisted task, but the model’s proposed call is distinct from an executed action. Preserve the actual returned result when continuing the conversation so a later answer reflects what happened rather than what was requested.\n\nThe released usage card recommends temperature 0.0 for transcription, and temperature 0.2 with top_p 0.95 for audio understanding and chat. It states that system prompts are not supported in this flow, so put the task and response requirements in the user input. Its examples decode recordings into audio inputs and include MP3 material, but they do not establish an exhaustive codec list or a separate hard maximum output length. The 500-token generation setting in the examples remains an example budget.\n\nPrepare a clear recording containing the material relevant to the task and specify whether you need original wording, a translation, an answer or a summary. The card also demonstrates batched inference with several conversations, a producer usage pattern separate from the identity of an individual result. Preserve the 3B 2507 edition and its transcription-versus-understanding duration guidance; the later Mini Transcribe 2 model’s diarisation, 13-language profile and longer recording support are separate producer features.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "2405bdb231e1b6d47c147d0e091a19cd83ee2c606239a637f8112112bbdce472", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "mistralai/voxtral-mini-3b-2507Voxtral Mini 1.0 3B 2507 combines audio understanding with a compact text model for transcription, speech translation, questions and summaries. Its maker documents a 32K context, up to 30 minutes of transcription or 40 minutes of audio understanding, and text output.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Voxtral Mini 1.0 3B 2507 is Mistral AI’s compact text-and-audio understanding model built on the Ministral 3B language-model family. It accepts speech recordings and text instructions and produces text, including transcripts, translations, answers about the audio and structured summaries. The original Voxtral release positions this 3B model for compact or local applications, alongside the separate 24B Voxtral Small model. It is also distinct from the hosted transcription-optimised Voxtral Mini Transcribe service.
The model has a dedicated transcription mode that automatically predicts the source language by default and transcribes accordingly. Choose that mode when the required artifact is the spoken wording. Audio understanding instead permits a direct question or summary request about a recording, without requiring a separate speech-recognition stage followed by a text model. An interview can therefore produce a full transcript, a translation or an answer to a narrow question; specify which result is needed so those different tasks do not become one ambiguous brief.
The exact release card lists a 32K-token context and audio up to 30 minutes for transcription or 40 minutes for understanding. These are different task-specific duration statements, not a shared audio allowance for every operation. Keep the relevant recording, instructions and conversation within the intended task scope. The maker’s named languages are English, Spanish, French, Portuguese, Hindi, German, Dutch and Italian, with automatic language detection. For a translation or answer, distinguish the recording’s language from the required output language.
Several audio inputs in a message and audio across multiple user turns are supported. Give each recording a clear identifier when asking for comparisons. An original example might compare two spoken explanations and return which requirements changed, with a separate column for each source. A follow-up can ask for the detail behind one finding or supply another recording. The card also demonstrates text-only and audio-only use, allowing the model to work with the written question as well as the speech evidence.
For a meeting summary, supply the recording and request a useful structure such as topics, stated decisions and follow-up actions. Keep uncertain names, dates or responsibilities separate from facts that are clearly spoken. For an interview, ask for the statement that answers a particular question and enough surrounding context to make it understandable. These are task designs for a usable text artifact; they do not turn general audio understanding into a guarantee of diarisation, word timestamps or identification of an unnamed speaker.
The manufacturer describes function calling from spoken intent. In such a workflow, the application supplies function definitions, the model proposes the relevant call and arguments, and the application executes it and returns the observation. A voice request can therefore guide a lookup or another tool-assisted task, but the model’s proposed call is distinct from an executed action. Preserve the actual returned result when continuing the conversation so a later answer reflects what happened rather than what was requested.
The released usage card recommends temperature 0.0 for transcription, and temperature 0.2 with top_p 0.95 for audio understanding and chat. It states that system prompts are not supported in this flow, so put the task and response requirements in the user input. Its examples decode recordings into audio inputs and include MP3 material, but they do not establish an exhaustive codec list or a separate hard maximum output length. The 500-token generation setting in the examples remains an example budget.
Prepare a clear recording containing the material relevant to the task and specify whether you need original wording, a translation, an answer or a summary. The card also demonstrates batched inference with several conversations, a producer usage pattern separate from the identity of an individual result. Preserve the 3B 2507 edition and its transcription-versus-understanding duration guidance; the later Mini Transcribe 2 model’s diarisation, 13-language profile and longer recording support are separate producer features.
POST /v1/audio/transcriptionsaudiotext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits mistralai/voxtral-mini-3b-2507 | Default— |
Fieldinput_audio | Typeobject | RequirementRequired | Values & limits | Default— |
Fieldlanguage | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Minimum: 0 Maximum: 1 | Default— |
Fieldresponse_format | Typestring | RequirementOptional | Values & limits json, verbose_json | Defaultjson |
Fieldtimestamp_granularities | Typearray | RequirementOptional | Values & limits | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input_audio |
Parametermaterial_types | Contractaudio |
Parametermaterial_conditions | ContractRead an actual WAV file and encode its real bytes for the declared JSON input_audio; no multipart route is advertised for this exact JSON-only declaration. |
Lifecycle: synchronous_result_then_owned_library
GET /v1/media/tasks/{task_id} · actual returned task_id or media library record
GET /v1/media/assets/{asset_id}/content
GET /v1/media/assets
GET /v1/media/assets/{asset_id}
The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Billing unit | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Billing unit— | Charge Input · Input tokens · Estimate for comparison only — / 1K tokens Output · Output tokens · Estimate for comparison only — / 1K tokens Input · Cache read · Estimate for comparison only — / 1K tokens Input · Cache write · Estimate for comparison only — / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only — / 1K tokens |
| Configurationinput audio second | Rate schedule— | Forecast quantity— | Billing unitper input audio second | Charge Input · input audio second · Estimate for comparison only $0.0000333334 per input audio second |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Billing unit— | Charge Output · Estimated generation cost · Estimate for comparison only $0.0000333334 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_AUDIO_FILE:?Set the path to your actual WAV recording}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\nimport base64\naudio=Path(os.environ['AILOOT_AUDIO_FILE']).read_bytes()\nif not (audio[:4]==b'RIFF' and audio[8:12]==b'WAVE'): raise SystemExit('Supply an actual WAV audio file')\nif len(audio)>26214400: raise SystemExit('Use a documented supported large-file flow')\nrequest={'model': 'mistralai/voxtral-mini-3b-2507', 'input_audio': {'data': '${BASE64_ACTUAL_WAV_BYTES}', 'format': 'wav'}}\nrequest['input_audio']['data']=base64.b64encode(audio).decode('ascii')\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 $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o response.bin\n# Keep the complete original response and headers. Read every returned segment, word and usage field.\n# Preserve actual operation/task/asset IDs; continue reads of the original result without resubmitting.\nGET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nKeep 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.
Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.
| Field | Type |
|---|---|
Fieldtext | Type |
Fieldusage | Type |
Fieldsegments[] | Type |
Fieldwords[] | Type |
Fieldspeaker fields | Type |
Fieldtimestamps | Type |
Ailoot Ultra mistralai/voxtral-mini-3b-2507
Use the method paths shown above.