---
title: "OpenAI"
type: "AI Tool"
url: "https://aidemos.com/tools/openai"
description: "Word-timestamped transcripts on jargon-heavy bilingual audio, but Whisper-1's 25 MB cap and weak Spanish recall limit meeting use."
category: "audio-speech"
website: "https://platform.openai.com/docs/guides/speech-to-text"
published: "2026-08-21T12:33:23.460869+00:00"
updated: "2026-08-21T12:33:23.460869+00:00"
evidenceCount: 16
verifiedCount: 16
coverage: "dense"
---

# OpenAI

Batch speech-to-text with word timestamps, but a strict upload cap and weak multilingual performance make it a mixed fit for hard audio.

## TL;DR Verdict

**Useful for simple batch transcription, but not a hard-audio default**

**Where it wins:**
- You need a simple hosted batch STT API with word-level timestamps.
- Your audio files are under 25 MB.
- Your inputs are mostly English or jargon-heavy narration rather than heavily code-switched speech.

**Main limitation:** You need speaker diarization or confidence scores.

**Pricing:** whisper-1 $0.006/min ($0.36/hr) · gpt-4o-transcribe $0.006/min ($0.36/hr) · gpt-4o-mini-transcribe $0.003/min ($0.18/hr) · gpt-4o-transcribe-diarize $0.006/min ($0.36/hr)

`25 MB upload cap` · `Word-level timestamps` · `No diarization` · `Mixed-language tested`

**Website:** [Visit OpenAI](https://platform.openai.com/docs/guides/speech-to-text)

## Evidence (first-party, tested)

*16 tested cells · 16/16 artifact-verified. Scores are out of 5. Cite a cell by its Evidence ID, e.g. `ev:openai·cross·automation-level`.*

| Criterion | Scenario | Verdict | Score | Proof | Evidence ID |
| --- | --- | --- | --- | --- | --- |
| Automation level | cross-scenario | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/779f9a413ae14ff5b8984ac4c7debb5e.png?v=1) | `ev:openai·cross·automation-level` |
| Automation level | Overlapping meeting speech with cross-talk | ✗ failed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/96638b98259c4d2aac95d1f588cca038.png?v=1) | `ev:openai·overlapping-meeting-speech-with-cross-talk·automation-level` |
| Automation level | Medical anatomy narration with dense jargon | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/4446fa2a39d844bab21fd46987159402.png?v=1) | `ev:openai·medical-anatomy-narration-with-dense-jargon·automation-level` |
| Automation level | Bilingual Spanish-English code-switching speech | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/ed1e8c921bcd4ced8e5aa03d04b01b26.png?v=1) | `ev:openai·bilingual-spanish-english-code-switching-speech·automation-level` |
| Export | Bilingual Spanish-English code-switching speech | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/4aa7c6faa826460ba29d6b929ec042ee.png?v=1) | `ev:openai·bilingual-spanish-english-code-switching-speech·export` |
| Export | Overlapping meeting speech with cross-talk | ✗ failed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/354b416409de410091540e2fff6d7877.png?v=1) | `ev:openai·overlapping-meeting-speech-with-cross-talk·export` |
| Export | Medical anatomy narration with dense jargon | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/04bcddb387974e7693066d4bbcefed6d.png?v=1) | `ev:openai·medical-anatomy-narration-with-dense-jargon·export` |
| Export | cross-scenario | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/5e38d0613a324b269256234bda0e1c29.png?v=1) | `ev:openai·cross·export` |
| Input handling | Bilingual Spanish-English code-switching speech | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/f61c6be7a62a472dbf02d38312f178b7.png?v=1) | `ev:openai·bilingual-spanish-english-code-switching-speech·input-handling` |
| Input handling | Medical anatomy narration with dense jargon | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/4446fa2a39d844bab21fd46987159402.png?v=1) | `ev:openai·medical-anatomy-narration-with-dense-jargon·input-handling` |
| Input handling | cross-scenario | ✓ worked | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/7ad7d217f66e4740a2344a4cf082d94d.jpeg?v=1) | `ev:openai·cross·input-handling` |
| Input handling | Overlapping meeting speech with cross-talk | ✗ failed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/2d9859ef01c9489e848ac12a440168a8.png?v=1) | `ev:openai·overlapping-meeting-speech-with-cross-talk·input-handling` |
| Output quality | cross-scenario | ◐ mixed | — | 🧾 [proof](https://d3epheqghktydj.cloudfront.net/research-media-openai-whisper-66c2d3e41cd0.md) | `ev:openai·cross·output-quality` |
| Output quality | Overlapping meeting speech with cross-talk | ◐ mixed | — | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/2d9859ef01c9489e848ac12a440168a8.png?v=1) | `ev:openai·overlapping-meeting-speech-with-cross-talk·output-quality` |
| Output quality | Medical anatomy narration with dense jargon | ✓ worked | 5.2/5 | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/4446fa2a39d844bab21fd46987159402.png?v=1) | `ev:openai·medical-anatomy-narration-with-dense-jargon·output-quality` |
| Output quality | Bilingual Spanish-English code-switching speech | ⚠ struggled | 26.1/5 | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/f61c6be7a62a472dbf02d38312f178b7.png?v=1) | `ev:openai·bilingual-spanish-english-code-switching-speech·output-quality` |

> 🧾 = artifact-verified (proof captured) · 👁 = observed (noted, no artifact) · verdicts: worked / mixed / struggled / failed.

## Benchmark summary

Current authoritative run: one oversized file was rejected; the other two transcripts succeeded with word timestamps but no speaker labels.

- **Rejected** Overlapping Speech / Crosstalk — HTTP 413 after 50.08s; the 65.39 MB file exceeded the 25 MB upload cap, so no transcript was returned.
- **5.17%** Medical Jargon — Returned a transcript with 5.17% WER, 88.9% jargon recall, and word-level timestamps.
- **26.12%** Bilingual Code-Switching — Returned a transcript with 26.12% WER and 46.2% Spanish token recall; the transcript was mostly English.

> **Useful for simple batch transcription, but not a hard-audio default**
>
> Whisper-1 returned usable transcripts on the jargon-heavy and bilingual samples, with word-level timestamps and predictable list pricing. But this benchmark also found a hard 25 MB upload cap, no speaker labels or confidence fields, and weak Spanish recall on the code-switched clip, so it is a mixed fit for real-world multilingual or meeting-style audio.

## Demo Recording

[Video: OpenAI demo recording](https://cdn.futuresmart.ai/public/aidemos/809f8c02edf44d47a3a13621f4e00826.mov?v=1)
*Video — Screen recording of the benchmark workflow for auditability.*

## Feature-by-Feature Breakdown

### Batch Audio Transcription

Transcribes uploaded audio files through a single multipart POST to `/v1/audio/transcriptions`, returning text output when the file is within the upload limit. It was exercised on the 8.58 MB medical-jargon clip and the 22.19 MB bilingual clip, with the 65.39 MB crosstalk file rejected by HTTP 413.

**Input:**

> **Audio**

**Output:** RESULT

> **Image** — RESULT

**Input:**

> **Audio**

**Output:** Transcript payload

> **Json** — Transcript payload

**Input:**

> **Audio**

**Output:** Transcript payload

> **Json** — Transcript payload

**Bottom line:** Works as a simple hosted batch transcription endpoint, but the 25 MB ceiling is strict and blocks larger real-world audio.

### Word-Level Timestamps

Produces verbose JSON transcription output with per-word timing when `response_format=verbose_json` and `timestamp_granularities[]=word` are enabled. It was exercised on the medical-jargon clip and the bilingual clip, yielding timed token outputs.

**Input:**

> **Audio**

**Output:** Verbose transcript payload

> **Json** — Verbose transcript payload

**Input:**

> **Audio**

**Output:** Verbose transcript payload

> **Json** — Verbose transcript payload

**Bottom line:** Good for captions and search alignment, but the response still lacks confidence values and speaker labels.

### Code-Switching Transcription — 26.12/100

**Verdict:** Mixed results

Transcribes mixed-language speech, including Spanish-English audio, into a single transcript. It was exercised on the bilingual sample, which transcribed successfully but showed uneven Spanish recall.

**Input:**

> **Audio**

**Output:** Transcript payload

> **Json** — Transcript payload

**Bottom line:** Use with caution for multilingual or code-switched speech; this benchmark does not support treating it as strong balanced multilingual transcription.

## Transcription pricing

Vendor-published list prices for OpenAI speech-to-text models.

| Plan | Price | Notes |
| --- | --- | --- |
| whisper-1 ★ (tested) | $0.006/min ($0.36/hr) | Flat per-minute pricing; listed as Whisper on the pricing page; tested in this benchmark. |
| gpt-4o-transcribe | $0.006/min ($0.36/hr) | Token-billed model; vendor shows $2.50/1M input tokens and $10.00/1M output tokens. |
| gpt-4o-mini-transcribe | $0.003/min ($0.18/hr) | Cheapest 4o-family transcription model on the published page. |
| gpt-4o-transcribe-diarize | $0.006/min ($0.36/hr) | Diarization is included at the same rate; it is not a separate surcharge. |
| gpt-transcribe | $0.0045/min ($0.27/hr) | Newer non-4o transcription model; cheaper than gpt-4o-transcribe. |
| gpt-live-transcribe | $0.017/min | Live/streaming transcription. |
| gpt-realtime-whisper | $0.017/min | Live transcription. |
| gpt-realtime-translate | $0.034/min | Live translation. |

*No free tier or published volume discount is shown on the API pricing page. Batch API is not priced separately for transcription.*

## Is It Right For You?

**Use it if**
- You need a simple hosted batch STT API with word-level timestamps.
- Your audio files are under 25 MB.
- Your inputs are mostly English or jargon-heavy narration rather than heavily code-switched speech.

**Skip it if**
- You need speaker diarization or confidence scores.
- You need balanced multilingual or strong code-switching transcription.
- Your audio files routinely exceed 25 MB.

## Classification

- **Category:** audio-speech
- **Subcategory:** audio-to-text
- **Type:** speech
- **Built for:** Other

## Frequently Asked Questions

**Q: Does OpenAI Whisper-1 support speaker diarization?**

Not in this benchmark. The responses had no native speaker labels, and the request config notes "No native diarization."

**Q: What file-size limit did it hit?**

The benchmark hit a 25 MB upload cap. A 65.39 MB crosstalk file was rejected with HTTP 413, while the 8.58 MB and 22.19 MB files were accepted.

**Q: Does it return word-level timestamps?**

Yes. The requests used `response_format=verbose_json` with `timestamp_granularities[] = word`, and the successful runs returned timed word arrays.

**Q: How accurate was it on the medical jargon sample?**

It scored 5.17% WER on the medical-jargon clip, with 88.9% jargon recall. The highlighted miss was "trabeculae," which was transcribed incorrectly.

**Q: How did it do on the bilingual code-switching sample?**

It scored 26.12% WER and 46.2% Spanish token recall. The transcript was mostly English, so this is weak evidence for balanced multilingual performance.

**Q: What did it cost in this benchmark?**

The published list price is $0.006 per minute, or $0.36 per audio-hour. The successful benchmark runs cost $0.1124 and $0.19385 at list price.

## Similar Tools

AI tools similar to OpenAI:

- [Gladia](https://aidemos.com/tools/gladia) — Batch STT with rich JSON metadata and strong jargon recall, but weak overlap handling and a channel-duplication caveat on bilingual audio.
- [Deepgram](https://aidemos.com/tools/deepgram) — Batch speech-to-text with rich metadata, but weak on crosstalk and code-switching.
- [AssemblyAI](https://aidemos.com/tools/assemblyai-speech-to-text) — Fast batch STT with strong metadata and mixed-language performance, but overlap-heavy meetings can drop too many words.
- [Google Cloud Speech-to-Text](https://aidemos.com/tools/google-cloud-speech-to-text) — Batch transcription with word timestamps, but uneven accuracy on overlap and code-switching.
- [AWS Transcribe](https://aidemos.com/tools/aws-transcribe) — Batch speech-to-text with timestamps and speaker labels, but weak on multilingual audio.
- [ElevenLabs Scribe](https://aidemos.com/tools/elevenlabs-scribe) — Fast batch transcription with rich metadata, strongest on jargon and weaker on overlap and code-switching.
- [Rev AI](https://aidemos.com/tools/rev-ai) — Low-cost batch speech-to-text with structured JSON, word timestamps, and speaker labels, but mixed accuracy on hard audio and weak multilingual recall.
- [GroqCloud](https://aidemos.com/tools/groqcloud) — Fast batch transcription for under-cap audio, with strong jargon handling but a hard 25 MB ceiling.

## Need a custom AI solution for this use case?

If you are looking to build a custom speech-to-text, audio transcription, or word timestamping system for your business or internal workflow, email us at [contact@futuresmart.ai](mailto:contact@futuresmart.ai).

### Found something inaccurate or missing?

We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at [collaborate@aidemos.com](mailto:collaborate@aidemos.com).