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OpenAI's dedicated speech-to-text model for turning audio into accurate text transcripts at volume.
Overview. GPT-4o Mini Transcribe (2025-12-15) is OpenAI's dedicated speech-to-text model built on the GPT-4o Mini family. It takes audio, plus an optional text prompt, and returns a text transcript, making it a purpose-built endpoint for teams that need Inference focused specifically on turning speech into accurate text rather than general chat or reasoning. The 2025-12-15 snapshot brings accuracy and reliability improvements over OpenAI's earlier transcription models.
Key strengths. As a narrow audio-to-text endpoint, it performs a single, well-defined task rather than multi-step reasoning, which keeps its behavior predictable for transcription pipelines. OpenAI positions this snapshot as more accurate and more reliable than prior transcription releases in the same family. Its compact design mirrors the cost-efficient, high-volume orientation of the broader GPT-4o Mini family, making it a fit for workloads that need to process a lot of audio without complex downstream logic.
When to use it. Reach for this model when you need to convert spoken audio into text at volume — call transcription, meeting notes, voice-driven interfaces, or captioning pipelines. It suits latency-sensitive, high-throughput settings where the task is strictly transcription rather than summarization, analysis, or multi-turn conversation. If your workflow needs the model to reason over the transcript, call tools, or hold a system-level conversation, pair it with a separate text model downstream rather than expecting it to do that work itself.
Limitations. The transcription endpoint runs on a smaller context window (16,000 tokens) and output cap (2,000 tokens) than the base GPT-4o Mini chat model, so it isn't suited to long-form audio in a single call or to generating long text. It does not support tool calling, audio output, or a conventional system prompt — the optional prompt field is meant to bias vocabulary and style rather than set behavior. OpenAI has not published a training cutoff or parameter count for this variant.
Sovereignty note. GPT-4o Mini Transcribe is built and operated by OpenAI, a US-based creator, so audio and text processed through this endpoint fall under US jurisdiction. Specific data-residency and training-on-input policies for this transcription endpoint are not documented in current research and should be confirmed directly with OpenAI before handling sensitive audio.
| Inference Provider | Sovereignty | Runs in | Price | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.00269 per audio minute | Online |
8 use cases classified by the curator. The note says why.
Model name explicitly flags transcription capability (gpt-4o-mini-transcribe-2025-12-15); 128K context window accommodates full audio sessions and transcripts. This is the stated purpose variant of the family.
128K context supports multi-turn conversation history and ticket context; cost-sensitive family positioning suits high-volume support deployment, though mid-tier quality (6.7) places it in acceptable rather than strong tier.
128K context enables document ingestion and retrieval workflows; closed-weights + U.S.-origin limits sovereign deployment but cost profile suits cost-sensitive document-QA at scale.
128K context window enables batch entity extraction across documents; cost-efficient for high-volume pipelines, though family quality index and absence of structured output capabilities suggest acceptable rather than strong fit.
128K context window and transcription capability enable processing of meeting transcripts; cost-efficient inference suits high-volume meeting workflows, though quality index 6.7 suggests acceptable rather than strong accuracy for critical summaries.
128K context supports multi-document extraction; mid-tier quality (6.7) is acceptable for non-critical extraction, though no tool-calling or function-calling signal limits agentic extraction workflows.
128K context accommodates long documents; family's cost efficiency suits high-volume summarisation, but quality index 6.7 indicates acceptable rather than strong performance on accuracy-critical summarisation tasks.
Sourced numbers, not our own evaluations. Percentiles are against the transcription models on Stav.
No benchmark source yet
curl https://api.stav.ai/v1/audio/transcriptions \
-H "Authorization: Bearer $STAV_API_KEY" \
-F model="openai/gpt-4o-mini-transcribe-2025-12-15" \
-F file="@meeting.mp3"→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at