I tested this the way I test everything: on my worst recording, not my easiest one. A 45-minute call from an old telecom project, three people talking over each other, a handful of internal acronyms nobody outside the project would recognize. Two years ago I would have run it through TurboScribe or Alter.ai and spent the rest of the afternoon correcting jargon. This time, with GPT-4o's audio support live, I dropped the file straight into ChatGPT and asked it to transcribe.
Short answer: yes, ChatGPT can transcribe audio, and it does a genuinely decent job on clean, simple recordings. It runs on OpenAI's Whisper model under the hood, so you're getting real speech-to-text, not a gimmick. But there are limits that show up the moment your recording looks like actual work instead of a clean voice memo — and Claude, if you're wondering, doesn't really compete here at all.
TL;DR: ChatGPT transcribes audio up to 25MB per file, reasonably accurately on clear single-speaker audio, with no reliable speaker labels and no timestamps. Claude isn't built for this at all — its file support is documents and images, not audio transcription. Neither one is the right tool once you need decisions extracted, terminology preserved, or non-English audio handled properly.
Yes, but here's exactly how it works
ChatGPT doesn't have some proprietary in-house transcription engine. When you upload an audio file, the request goes through OpenAI's Whisper-based models — whisper-1, or the newer gpt-4o-transcribe and gpt-4o-mini-transcribe. You drop the file in, ChatGPT sends it through that pipeline, and hands you back text.
The catch is the same one that applies to the API directly: OpenAI's own Audio API FAQ confirms a 25MB file size limit per request. A 45-minute call at normal recording quality often sits right at or above that line, depending on the format — MP3 usually fits, a raw WAV frequently doesn't. If your file is too big, you're compressing or splitting it before ChatGPT will even look at it.
Supported formats are the common ones: MP3, WAV, M4A, and a few others. Nothing exotic required. For a full breakdown of every way to get audio into text — including manual and offline options — I covered the whole landscape in my guide to transcribing audio to text.
The limits that actually matter
On a short, clear, single-speaker recording, ChatGPT's transcription is solid — comparable to a dedicated tool. That's not a knock. Whisper is a genuinely strong model. But three specific gaps show up the moment the recording resembles real work:
- No reliable speaker labels. ChatGPT sometimes attempts to distinguish speakers, but it doesn't consistently tag "Speaker 1," "Speaker 2" the way a purpose-built diarization feature does. For a three-person call, that's the difference between a usable transcript and a wall of text you have to manually attribute yourself.
- No timestamps. You get a block of text, not a transcript you can click through to jump back to the exact moment something was said.
- Accuracy drops fast outside ideal conditions. Clean audio with one or two speakers holds up well. Add background noise, overlapping speakers, accents, or dense technical jargon, and the errors start compounding — and ChatGPT won't flag which parts it's unsure about. It writes the wrong word down with exactly the same confidence as the right one.
That last point is the one I actually care about. A tool that guesses confidently and wrong is worse than one that flags uncertainty, because you won't know to double-check it.
Claude handles this differently — mostly by not really trying
If you're asking the same question about Claude: no, not in any meaningful sense. Anthropic's own file-upload documentation lists the supported formats — PDF, DOCX, TXT, RTF, ODT, HTML, EPUB, JSON, CSV, XLSX, PPTX, and common image formats. Audio isn't on that list. Claude's audio-related feature is Voice Mode — a live spoken conversation with the assistant — not batch transcription of a pre-recorded file you already have sitting on your laptop.
If you specifically need Claude in your workflow for other reasons, you'd be routing through a third-party integration to get transcription done at all, not using a native capability.
Transcription alone was never the actual problem
Here's the opinion I keep coming back to, because I've watched it play out on every project I've worked on: a raw transcript of a two-hour meeting is just as overwhelming as the meeting itself. The value was never "convert audio to text." It's what happens after — decisions extracted, jargon resolved, action items pulled out instead of buried on page fourteen. That gap matters most in dense, jargon-heavy meetings, which is exactly where a raw ChatGPT transcript stops being useful.
I learned this the slow way. A senior architect once spent forty-five minutes explaining, off the cuff, how three versions of the same data object fed into a rules engine. I retained maybe forty percent of it by the next morning. By the following week I was asking the same question again, in a different meeting, and getting noticeably less patience the second time. That's what pushed me to start transcribing everything properly — not for the words themselves, but to extract the actual decision, the reasoning behind it, and the terms that would otherwise get mistranscribed every time.
That last part is where a generic model shows its limits fastest. Drop in a recording where someone says "ODM" and means Operational Decision Manager — an internal rules-engine term — and a general-purpose model will confidently transcribe it as if you meant Original Design Manufacturer, because that's the far more common meaning in its training data. It won't ask. It'll guess, and it'll guess the same wrong way every time, because it has no memory of your last recording and no way to learn your team's terms in advance.
That's the specific gap a custom Vocabulary Library closes — you add the terms once, and every future transcript uses them correctly instead of guessing.
Where both of them fall apart: Arabic and mixed-language audio
Neither ChatGPT nor Claude is built for a very specific, very common problem in the Middle East and North Africa: a meeting where a single sentence blends Arabic, French, and English — code-switching mid-thought, not politely alternating between languages. This isn't a rare edge case here. It's what a normal Monday meeting sounds like.
The research backs up why this is genuinely hard, not just under-supported: studies on Arabic-English code-switched speech recognition point to the scarcity of code-switching training data and the unstandardized orthography of dialectal Arabic as the core reasons accuracy drops sharply the moment a model has to handle blended-language audio instead of one language at a time. A model trained mostly on clean single-language audio guesses badly the moment someone switches mid-sentence — and "supports 99 languages" on a spec sheet almost always means switching between languages, not handling them mixed together in the same breath.
Solving it properly usually means running more than one model and merging the results, rather than betting one model handles everything. That's a genuinely more expensive way to process the audio — but the alternative is a transcript that's wrong exactly where it mattered most.
When ChatGPT is genuinely all you need
I'd rather tell you this straight than pretend otherwise: if you have one short, clear recording — a five-minute voice memo, a single-speaker note to yourself, nothing that needs timestamps or multiple speakers attributed — dropping it into ChatGPT is a perfectly reasonable choice. It's already open in a tab, it's free to try, and for that specific case it does the job. Don't pay for a dedicated tool to solve a problem that small. I compared it against the rest of the free options directly in the best free transcription tools roundup, if you want the fuller picture.
The moment you're dealing with more than one speaker, a recording that runs long, terminology that needs to stay consistent, or audio that isn't cleanly one language start to finish — that's when the gap starts to cost you actual time.
What this actually costs, compared honestly
If you do need a dedicated tool, here's what Audiomaktube costs — flat, no "starting at":
- Free ($0/mo): 2 transcriptions a day, up to 20 minutes per file. Speaker detection and 99-language support included.
- Starter ($5/mo): Up to 45 minutes per file, 20 hours a month, quick AI summaries, one-click translation into 14 languages.
- Pro ($10/mo): Up to 2 hours per file, 45 hours a month, task extraction, the Vocabulary Library, and Ask Your Audio for asking questions about a specific recording.
- Unlimited ($15/mo): Up to 6 hours per file, unlimited transcription, and Ask Across Recordings — chatting with the AI across several meetings at once, which nothing in ChatGPT's file-upload flow does, since it has no memory that persists between separate audio uploads.
Every new account also gets a 3-day Pro trial, so you can test the parts ChatGPT doesn't do before deciding whether you need them.