The 8 best MAI-Transcribe-2 alternatives in 2026

Why look past MAI-Transcribe-2 in the first place

Let me be fair to the incumbent first, because it earns it. MAI-Transcribe-2 sits at the top of Microsoft's FLEURS accuracy comparison, transcribes an hour of audio in about ten seconds, and at $0.10 per hour it undercuts everyone. When I read the early developer chatter, the reactions were the kind you cannot fake. One indie builder ran a hard real-world clip through it:

"I tried running a church homily with lots of echo in Slovak language through this... It did much better than ElevenLabs at 45% of the price."

Vojtech Rinik (@_vojto), X

Noisy, echoey, non-English audio is exactly what breaks lesser models, so that is a real signal. So why look elsewhere at all? Three concrete reasons, none of them a knock on the model itself.

First, the price is a moving target. That $0.10 is a launch promo running "until the end of the year," and Microsoft has not said what happens next. For reference, the older MAI-Transcribe-1.5 still lists at $0.36 per hour, so the standard v2 rate landing in that range would surprise nobody. A Hacker News commenter asked the exact question Microsoft left open:

"is this usable for streaming or only batch? do they give the price after the launch-discount (till end of year) goes away?"

jauntywundrkind, Hacker News

Second, it is a public preview shipped without an SLA, which Microsoft flags as not for production, and the main API is batch only with a 300 MB file cap. Live audio needs a separate Voice Live setup. Third, it is an Azure model, so you reach it through Azure Speech, a developer playground, or third-party routers, not a standalone app. If you are building somewhere else, that ecosystem gravity is a real cost.

None of that makes MAI-Transcribe-2 a bad choice. It just means "cheapest and most accurate today" is not the same as "the right fit for your project," and the gap is where these alternatives live.

How I compared them

I have spent a lot of time inside these APIs, reading the docs, the pricing pages, and the parameter lists, so I picked the dimensions that actually change your architecture, not the marketing bullet points. Five things matter:

• Accuracy, measured as word error rate (WER, the percentage of words the model gets wrong). Lower is better.
• Price per hour of audio, normalized so you can compare like for like.
• Batch vs real-time, because a batch-only model cannot power a live phone agent no matter how accurate it is.
• Diarization and timestamps, the speaker labels and word-level timing that turn a wall of text into something usable.
• Deployment, meaning whether you can run it on your own hardware or must send audio to the vendor's cloud.

On raw accuracy, here is where the field lands on Microsoft's own FLEURS comparison. It is worth internalizing one thing from this chart: the top is a near-tie, and the real spread is between the leaders and the older open Whisper.

The best MAI-Transcribe-2 alternatives at a glance

Before the deep dives, here is the whole field in one table. Prices are per hour of audio for standard batch transcription, converted from each vendor's published per-minute or per-token rate.

Paid batch pricing clusters far tighter than most people expect. Once MAI-Transcribe-2's promo lifts, the whole field sits within a few cents of each other.

1. Google Gemini 3.5 Transcribe

Best for: teams who want the accuracy closest to MAI-Transcribe-2 and are already building on Google.

Google is the alternative that matches MAI-Transcribe-2 head to head. On Microsoft's own FLEURS table, Gemini 3.1 Pro posts 5.3%, a tenth of a point behind, and Google also ships a dedicated speech-to-text model, gemini-3.5-transcribe, plus a gemini-3.5-transcribe-live streaming variant. That split matters: the file model handles audio up to one hour per request (30 minutes if you turn on diarization or word-level timestamps), while the live model does low-latency streaming over WebSockets.

What you get is a full feature set: 85+ languages with mid-sentence code-switching, speaker diarization up to eight speakers, word-level timestamps, and custom vocabulary biasing of up to 1,000 terms. It reads more like a full audio-understanding stack than a bare transcription endpoint.

On pricing, the dedicated model is token-based: audio input runs $2.00 per million tokens and text output $12.00 per million, which works out to a blended rate of roughly $0.005 per minute, or about $0.30 per hour. There is a free tier for testing, which is a nice on-ramp.

Our take: if accuracy is your single deciding factor and you are not wedded to Azure, this is the pick. It is the only model that trades blows with MAI-Transcribe-2 on the benchmark, and having a first-party streaming variant closes the one big gap MAI leaves open. The catch is that Google has not published a headline WER of its own, so you are trusting Microsoft's comparison for the exact numbers.

2. ElevenLabs Scribe v2

Best for: media, podcast, and dubbing teams who need rich speaker labels and audio-event tagging.

ElevenLabs made its name in voice generation, and Scribe is its speech-to-text side. The current flagship is scribe_v2, with a scribe_v2_realtime variant clocking around 150 ms latency. On Microsoft's FLEURS comparison it lands at 6.2%, a step behind the leaders but still in strong company, and ElevenLabs itself claims over 98% transcription accuracy with per-language WER breakdowns.

Where Scribe pulls ahead is the richness of the output. It covers 90+ languages, diarizes up to 32 speakers (the highest here), tags dynamic audio events like laughter and music, and recognizes 65 entity types. For anyone cutting captions or transcribing multi-speaker recordings, that detail is the whole point.

Pricing is billed per hour: Scribe v2 is $0.22 per hour for batch and $0.39 for realtime, with add-ons like entity detection (+$0.070/hr) and keyterm prompting (+$0.050/hr). The free tier gives 10,000 credits a month, though without a commercial license. One builder summed up the practical appeal on Hacker News:

"ElevenLabs' Scribe model is also particularly great with accuracy, and I use it for high-quality transcriptions or manually upload files to their API to get diarization and timestamps."

braden-w, Hacker News

Our take: Scribe v2 is the one to beat for media work, where 32-speaker diarization and audio tagging earn their keep. It is a touch behind on raw WER and pricier than the cheapest options, but for content teams the output quality is worth it. For plain support-call transcription, cheaper models do the same core job.

3. Deepgram

Best for: real-time voice agents, phone systems, and any product streaming live audio.

Deepgram is the developer favourite for real-time transcription, and it is the clearest answer to the one thing MAI-Transcribe-2 does not do well out of the box: live streaming. It shows up on most roundups of AI voice companies for good reason. Its flagship Nova-3 model handles both batch and streaming, and a newer model called Flux is purpose-built for voice agents, with turn detection baked in. If you are wiring speech into a phone tree or a live agent, this is where builders start.

The pricing is aggressive and unusually transparent. Nova-3 runs $0.0043 per minute for pre-recorded audio (about $0.26 per hour) and $0.0048 per minute for streaming, billed to the second with no rounding. You get a $200 free credit, roughly 750 hours, to test. It covers 40+ languages, does diarization and word timestamps, and supports keyterm prompting for your jargon.

The other thing worth flagging is deployment. Deepgram offers self-hosted containers on its Enterprise tier, with SOC 2, HIPAA, GDPR, and EU data residency, so regulated teams can keep audio on their own infrastructure. That is a genuine differentiator over the cloud-only APIs here.

Our take: for anything real-time, Deepgram is my default recommendation over MAI-Transcribe-2. Fewer languages than Gemini or Whisper, but its streaming latency, per-second billing, and self-hosting option make it the practical choice for live voice products. If your audio is all recorded files, the accuracy leaders may edge it.

4. AssemblyAI

Best for: teams who want transcription plus summaries, sentiment, and redaction in one API call.

AssemblyAI's pitch is that transcription is only step one. Its flagship Universal-3.5 Pro model does the speech-to-text, and then a whole Audio Intelligence layer sits on top: summarization, sentiment analysis, topic detection, entity detection, PII redaction, and an LLM Gateway for running language-model prompts over the audio. If you would otherwise stitch three services together, AssemblyAI does it in one.

Pricing is per hour and among the lowest here. Universal-3.5 Pro is $0.21 per hour async and $0.45 realtime, while the older Universal-2 drops to $0.15 per hour. The intelligence features are priced separately per hour on top, so you only pay for what you switch on. The free tier is generous: 185 hours of pre-recorded and 333 hours of streaming, no card required.

One honest note: AssemblyAI markets "industry-leading accuracy on real-world audio" but does not print a single FLEURS number on its main pages, so it does not appear on Microsoft's comparison. You are choosing it for the intelligence layer and the price, not for a headline WER you can check.

Our take: if your product needs the analysis, not just the transcript, such as call summaries or sentiment scoring, AssemblyAI saves you from bolting on a second vendor. For raw transcription alone it is competitive but not obviously better than the accuracy leaders. The huge free tier makes it easy to trial.

5. OpenAI GPT-Transcribe

Best for: teams already running an OpenAI pipeline who want transcription in the same SDK.

OpenAI has quietly refreshed its transcription lineup. gpt-transcribe is now the recommended default, with gpt-4o-transcribe, gpt-4o-mini-transcribe, and the legacy whisper-1 still available. On Microsoft's FLEURS table, GPT-Transcribe lands at 10.4%, well behind the leaders, though real-world reviews are warmer than that number suggests:

"Utter uses GPT-4o Transcribe by default for cloud transcription, and in my experience it's best in class. The gap is most obvious on names, niche terminology, and technical vocabulary."

Hacker News comment

Pricing is per audio minute: gpt-transcribe is $0.0045 per minute (about $0.27 per hour), gpt-4o-transcribe is $0.006, and gpt-4o-mini-transcribe is a cheap $0.003. Files cap at 25 MB, and around 99 languages are supported with auto-detection.

The one gotcha to plan for: the plain models do not do speaker labels. Diarization lives in a separate model, gpt-4o-transcribe-diarize, and word-level timestamps only come through the legacy whisper-1. So the tidy "one API" story has a couple of asterisks depending on the metadata you need.

Our take: the reason to pick GPT-Transcribe is integration, not accuracy. If you are already calling OpenAI, adding transcription is a one-line change and the practical quality on names and jargon is well-liked. If accuracy across many languages is the priority, the benchmark says look at Gemini or MAI first.

6. Speechmatics

Best for: the widest language and accent coverage, and teams that need on-prem deployment.

Speechmatics is the enterprise specialist, built around accent-independent accuracy and a "global-first" language philosophy. It covers 55+ transcription languages and dialects plus 69 translation pairs, and its multilingual Melia model handles mid-conversation language switching, which is a real edge for mixed-language audio.

Pricing is per hour and credit-based, with clear tiers. Batch runs from $0.129 per hour on the Melia model up to $0.40 for the Enhanced model, with the Standard tier at $0.24; real-time is $0.24 to $0.43. There is a $100 free credit and an automatic 20% discount above 500 hours a month. It scores 4.7 out of 5 on G2 across 70 reviews.

The standout is deployment. Speechmatics offers true self-hosting: Private Cloud, Container, Virtual Appliance, and even On-Device options, all Enterprise-tier, with SOC 2 Type II, ISO 27001, GDPR, and HIPAA. Adobe Premiere and Stenograph reportedly run it on-device. If your audio legally cannot leave your walls, this is one of the few models here that can accommodate that.

Our take: Speechmatics is the answer when compliance and language breadth outrank a rock-bottom price. It is not the cheapest and not the FLEURS champion, but the on-prem story and accent-independent accuracy make it the safe pick for regulated, multilingual enterprises. Smaller teams chasing the lowest bill will look elsewhere.

7. Amazon Transcribe

Best for: teams whose data and infrastructure already live in AWS.

Amazon Transcribe is the incumbent-cloud option, and its whole value proposition is that it is native to AWS. Batch jobs read and write S3, it slots into Lambda event pipelines, and it feeds Bedrock and Amazon Connect Contact Lens for generative summaries. If you are already an AWS shop, there is no new vendor, no separate billing, and no fresh security review.

Pricing is per second, tiered so it drops at volume. Standard batch is $0.006 per minute (about $0.36 per hour) and streaming is $0.01 per minute, with diarization, custom vocabulary, and language ID bundled in. The free tier is 60 minutes a month for the first 12 months. There is also a HIPAA-eligible Transcribe Medical tier at roughly $0.075 per minute that auto-identifies protected health information.

It covers 100+ languages and does the standard diarization, custom vocabulary, PII redaction, and Call Analytics insights. What it does not publish is a headline accuracy number, so it is absent from Microsoft's comparison, and its real-world accuracy has a mixed reputation next to the newer model-first vendors.

Our take: the case for Amazon Transcribe is operational, not accuracy-driven. Inside AWS it is the path of least resistance and the unified billing and IAM are worth real money. Outside AWS, there is little reason to choose it over the sharper, cheaper specialist models, and you pay a premium at the standard rate.

8. OpenAI Whisper (open source)

Best for: teams with their own GPUs who want zero per-hour cost and full control.

Whisper is the model that made open-source transcription mainstream, and it is still the reference point for self-hosting. It is MIT-licensed across both code and weights, ships in six sizes from tiny (39M parameters) up to large-v3 (1550M) plus an 809M turbo build, and installs with a single pip install. There is no API bill at all, you pay only for the compute, roughly 10 GB of VRAM to run large-v3.

The honest trade-off is accuracy and effort. On Microsoft's FLEURS comparison, whisper-large-v3 sits at 22.8%, far behind the modern models, which is the clearest illustration of how fast this field has moved. It also has no built-in diarization (you add pyannote or WhisperX) and no native streaming. But for many workloads the quality is plenty, as one self-hoster put it:

"I've been running whisper large-v3 on an m2 max through a self-hosted endpoint and honestly the accuracy is good enough that i stopped bothering with cleanup models. The bigger annoyance for me was latency on longer chunks."

Hacker News comment

If you want Whisper without running servers, OpenAI also hosts whisper-1 as an API at $0.006 per minute.

Our take: Whisper is the pick when data control and zero marginal cost beat top-tier accuracy. At real scale, self-hosting can undercut every paid API here. Just budget for the GPU time, the diarization add-on, and the accuracy gap versus a 2026 model like MAI-Transcribe-2 or Gemini.

Which one should you actually pick?

The honest answer is that transcription has become a commodity layer, so the choice comes down to the job, not a single "best" model, much like picking AI helpdesk software comes down to fit over a leaderboard. Here is the shortcut I would use.

Chase the lowest bill for multilingual batch and MAI-Transcribe-2 or Deepgram win. Building live voice agents, go Deepgram or Speechmatics. Need summaries and sentiment out of the box, AssemblyAI. Bound by compliance or on-prem rules, Speechmatics or Whisper. Already living in one cloud, use Amazon Transcribe or Gemini and skip the integration work.

The part the leaderboard does not show

Here is the reframe I kept coming back to while researching this, and it is the thing that actually matters if you work in support. Transcription used to be the hard, expensive part. It is not anymore. MAI-Transcribe-2 and every model on this list have turned it into a fast, cheap, near-solved commodity. That is great, but it means the value has moved.

An accurate transcript of a support call is not the thing anyone wanted. The text still has to be read, the answer still has to be found in your docs and past tickets, and the reply still has to be written and the ticket resolved. The model gives you the ears. It does not give you the teammate.

I have watched this play out at eesel for years. We have run AI on live support queues across thousands of real tickets, and the lesson that stuck is that the transcription accuracy was never the bottleneck; the resolution was. It is also why we simulate every rollout against a team's real past tickets before it ever replies to a customer, so you see how it would have handled your queue rather than trusting a benchmark. A confident-sounding model that gives wrong answers is worse than no model, and that risk sits in the resolution layer, not the transcription one.

Try eesel to turn transcripts into resolutions

Every model on this list is infrastructure. eesel is the employee that works on top of it. We build AI teammates you hire for a specific job, and the AI helpdesk teammate is the one that picks up exactly where a transcription model stops: it reads the text, pulls the answer from your help center and past tickets, and drafts or sends the reply right inside Zendesk, Freshdesk, or Slack. Pair a model like MAI-Transcribe-2 with eesel and a transcribed voice ticket becomes a resolved one, not just a tidy paragraph.

And because these are all API-first models, the developers reaching for them tend to want the same control over their support automation. eesel has a public CLI for exactly that: it drives the same teammate and workspace from a terminal, so a person can run it by hand, a script can automate it, and coding agents like Claude Code or Cursor can operate it headlessly, no dashboard required. You wire transcription into the front of the pipeline and eesel into the resolving end. It is free to try, plugs into your helpdesk in a few minutes, and simulates against your real past tickets before it ever replies to a customer.