A speech-to-text API is a web service that turns spoken audio into written text: you send a recording or a live stream, and it returns the words, usually with timestamps and confidence scores. Finding the best speech to text API means matching those words, their price and the vendor's data terms to your audio, because no vendor wins all three.

Most ranking lists for this search are written by companies selling one of the products on the list, and they tend to put themselves first. Deepgram's 2026 guide ranks Deepgram first of 10 and says it "leads speech-to-text accuracy benchmarks". On the largest independent leaderboard we could find, Nova-3 currently sits behind most of the table on accuracy, though it's among the fastest.

This list is also written by a vendor. Sully sells a clinical speech API, so we don't rank it here: general audio isn't what it's built for. We ran no test of our own. Every fact below was read on a vendor's page or a third party's benchmark on 3 November 2026.

Key Takeaways

• There's no single winner. The right pick depends on whether your audio is recorded or live, whether you need speaker labels, and whether audio can leave your network.
  
  

• Among the eight APIs compared, ElevenLabs Scribe v2 has the best independent accuracy score on recorded English audio, at 2.2% on Artificial Analysis. That benchmark tells you very little about clinical speech or non-English audio.
  
  

• Live audio with speaker labels rules out three of the eight as documented. For those that can do it, 1,000 hours a month runs from about $270 to $1,300 at list price.
  
  

• Benchmarks build a shortlist. They don't pick a winner. Run the finalists on your own recordings before you commit.
  
  

• For clinical audio, use our HIPAA medical transcription API comparison instead. It compares the vendors on what healthcare buyers need to check.

How We Compared Each Speech-to-Text API

We used six criteria. Each can rule out an API before accuracy matters.

1. Price. Vendors quote per second, per minute or per hour, so we converted everything to US dollars per audio hour at pay-as-you-go list price. Volume discounts aren't included.
   
   

2. Streaming. Whether the API takes live audio, with which model and at what premium.
   
   

3. Speaker labels (diarization). Whether they work on recordings, on live audio, or both, and whether they cost extra.
   
   

4. Languages. Counted per model where vendors publish it that way.
   
   

5. Self-hosting. Whether you can run the model inside your own network.
   
   

6. BAA. Whether the vendor says it will sign a HIPAA Business Associate Agreement. This matters only if your audio contains health information, but then it can end a shortlist early.

Accuracy isn't a column. Vendor accuracy claims are measured on private test sets that can't be compared, so we cover independent benchmarks separately below.

We kept the table to eight. Gladia, Mistral, Soniox and Rev AI are left out for space, not because they're weak. Mistral scores well on the leaderboard below.

Speech-to-Text API Comparison Table

Prices are as of November 2026, in US dollars per audio hour. They're our conversions from each vendor's published rate, so follow the link before you budget. "None listed" means we found no such option on the pages we read.

The table hides five things worth knowing.

Speechmatics' page shows lower numbers than these. It advertises $0.12 an hour for Melia 1 and $0.38 for Enhanced, but those rates assume two discounts: 33% for letting it use your audio and transcripts to improve its models, and 25% for a top-tier annual commitment. The table uses the base rates from the same page, because that's what you pay without both.

Language counts belong to models, not vendors. AssemblyAI's "99 languages" is Universal-2. Its more expensive flagship for recordings, Universal-3.5 Pro, covers 18. Check the model you'd actually run.

Live audio with speaker labels is where the list shrinks. Google documents Chirp 3 diarization for batch only. OpenAI says speaker labeling isn't supported in Realtime sessions. ElevenLabs lists diarization for Scribe v2 but not for Scribe v2 Realtime. Here's what the rest cost for 1,000 hours a month at list price:

That's nearly a 5x spread ($1,300 / $270) for what looks like the same feature, before anyone has measured accuracy. The cheapest row is English only, so it isn't a free win. Our pricing article goes further into add-ons and worked costs.

OpenAI's recommended model has two catches. Its only speaker-labeling model, gpt-4o-transcribe-diarize, is deprecated and shuts down on February 26, 2027. Uploads are also capped at 25 MB. Uncompressed 16 kHz, 16-bit mono WAV uses 32,000 bytes a second, so 25,000,000 / 32,000 = 781 seconds, about 13 minutes. Longer recordings need compressing or splitting.

Published prices go stale quickly. Voice Writer's leaderboard, discussed below, still shows AWS at $1.44 an hour. AWS's own pricing page works from $0.006 a minute for batch and $0.01 for streaming, which is $0.36 and $0.60. Any list without a date, this one included, should be checked against the vendor.

What Independent Benchmarks Say About the Best Speech Recognition API

Two independent benchmarks are worth reading. Neither measures clinical speech in any depth.

Artificial Analysis

Artificial Analysis scores models on word error rate (AA-WER), speed and price. Today's top five are Fun-Realtime-ASR-preview (1.7%), Microsoft's MAI-Transcribe-2 (2.0%), Mistral's Voxtral Small Transcribe 3 (2.1%), ElevenLabs Scribe v2 (2.2%) and Voxtral Mini Transcribe 3 (2.2%).

For the APIs in our table, it lists Scribe v2 at 2.2%, AssemblyAI Universal-3.5 Pro at 3.0%, OpenAI GPT Transcribe at 3.3%, Speechmatics Enhanced at 4.0%, Amazon Transcribe at 4.1%, Google Chirp 3 at 4.3% and Deepgram Nova-3 at 5.2%. Nova-3's speed factor is 582x, second only to a hosted Parakeet model.

Here's what it can't tell you, based on how it's built:

• It's barely about your jargon. Half the score comes from AA-AgentTalk, and "industry jargon" is 15% of that dataset. So all jargon is at most 50% x 15% = 7.5% of the index, and healthcare is one of six subcategories inside it.
  
  

• Part of it is read aloud. AA-AgentTalk's samples are scripted prompts read by participants. That isn't two people talking over each other in a room.
  
  

• A quarter of the score rests on six recordings. Earnings22-Cleaned-AA has 6 samples and carries 25% of the weight.
  
  

• The answer key can move the score as much as the model does. When Artificial Analysis corrected errors in its reference transcripts, average scores improved by 3.5 points on VoxPopuli and 5.6 on Earnings22. The gap between first place and Nova-3 today is also 3.5 points. A vendor's private test set carries the same risk, and you can't inspect it.
  
  

• It says little about other languages. Its normalizer builds on Whisper's English normalizer, and its VoxPopuli subset is English.
  
  

• It ages fast. February's top five were Scribe v2, Gemini 3 Pro, Voxtral Small, Gemini 2.5 Pro and Gemini 3 Flash. Only Scribe v2 is still there less than nine months later.
  
  

• Its price column is an estimate. It shows MAI-Transcribe-2 at $1.67 per 1,000 minutes, about $0.10 an hour. Azure's retail price data shows a $0.54 per hour meter for the same model. We can't tell you which applies to your account, so ask.

Its speed factor also measures batch jobs, not how fast words appear during a live call. Artificial Analysis runs a separate streaming board for that.

Voice Writer

The benchmark that circulates on Reddit's r/speechtech comes from the developer of Voice Writer, an app that buys speech recognition rather than sells it. That removes the most obvious conflict. It tests four conditions: clean, noisy, accented and specialist speech.

Its top result is GPT-4o Transcribe at 5.4%, followed by Gemini 2.5 Pro at 5.6%. It's a useful check on vendor claims, with limits:

• It was last updated in October 2025. Its winner, gpt-4o-transcribe, is now deprecated and shuts down in February 2027.
  
  

• Its hard audio is partly constructed. "Noisy" is clean TED clips with hospital noise added, and "specialist" is machine learning and physics abstracts read by a text-to-speech system. Neither is a recorded exam room.
  
  

• It can count formatting. Its formatted word error rate includes punctuation and capitalization, which suits a writing app. That's one more reason its numbers can't be set beside Artificial Analysis's normalized ones.

One finding is useful regardless: the same vendor does worse live than on recordings. On its overall view, AssemblyAI goes from 6.8% to 8.1%, AWS from 8.3% to 9.9% and Deepgram from 7.6% to 9.7%. Budget for that if you're choosing on a batch score.

A benchmark tells you which APIs deserve a test. It can't tell you which one to ship.

Word error rate also treats every word equally, which is where clinical audio gets into trouble. Whatever your domain, take 20 to 50 of your own recordings, run your finalists on the same files, and count errors on the terms that matter to your product.

Best Speech-to-Text API by Use Case

These are our picks from the facts above, not from a test. A speech to text API that suits one job can be a poor fit for the next.

• Recorded English audio, accuracy first: ElevenLabs Scribe v2. It's $0.22 an hour and has the best independent score in our table. Also try Mistral's Voxtral Small Transcribe 3. A 0.1-point gap on this benchmark isn't a meaningful difference.
  
  

• Live audio with speaker labels: Deepgram Nova-3 or AssemblyAI Universal-3.6 Pro Realtime. They cost almost the same at $0.58 and $0.57 an hour. Deepgram lists more languages, 60+ against 32. AssemblyAI's recorded-audio model outscores Nova-3 at Artificial Analysis, but that's a different model from its live one, so compare them on the streaming board. If you only need English on a budget, AssemblyAI Universal-Streaming is $0.27 with labels. Our real-time transcription guide covers the mechanics.
  
  

• Voice agents: start with Deepgram Flux. Deepgram recommends it over Nova-3 for this job, at the same $0.46 an hour for English. That's the vendor's advice, so check the latency figures on the streaming board.
  
  

• Many languages at a low price: AssemblyAI Universal-2 or Speechmatics Melia 1. Universal-2 costs $0.17 an hour with labels and covers 99 languages. Melia 1 costs $0.24 with labels included, covers 56 and can switch mid-sentence. Melia scores 4.9% on Artificial Analysis, against 4.0% for Speechmatics' $0.75 Enhanced model. Its advertised $0.12 requires letting Speechmatics use your audio for training, so don't budget on it for sensitive audio.
  
  

• Already committed to a cloud: use its API first. Procurement, access control and your existing agreements are real savings. Amazon Transcribe is the simplest price, at $0.36 and $0.60. Azure's batch rate is low at $0.18, but real-time costs $1.00. Google is $0.96 unless dynamic batch fits your model and your wait.
  
  

• Audio can't leave your network: Speechmatics, Deepgram or AssemblyAI self-hosted. Expect a sales process. Azure containers still need a connection for metering unless you're approved for disconnected use. Open-weight models have no per-minute fee, and Artificial Analysis names Voxtral Small as its most accurate one. You then own the GPUs, plus the diarization and streaming bolt-ons.
  
  

• Short clips in an OpenAI stack, no speaker labels: gpt-transcribe. At $0.27 an hour and 3.3% on Artificial Analysis, it's a sensible default. If you later need labels, our speaker diarization guide covers other routes.

Where Sully Fits: Clinical Audio Only

Sully is the clinical option, not a general one. If your audio is meetings, podcasts, sales calls or voice agents, one of the APIs above is the better choice.

It belongs in the conversation when the audio is a patient visit. Sully's transcription API takes files up to 100 MiB and 24 hours long. Its streaming API runs sessions of up to 2 hours and documents word_boost for terms like drug names. Responses can include word-level speaker labels, and the docs list 53 language values across 33 base languages. The trust page says Sully operates as a HIPAA Business Associate, "executing BAAs with every covered entity", and it also signs BAAs with software companies building on the API.

The main difference is what comes after the transcript: Sully also has Note Generation and Text to JSON APIs, so one vendor can carry the audio through to a note in your template.

Sully isn't in the table above because this comparison covers general-purpose APIs. Test it on your own recordings, the same way you'd test anyone else.

If you're building for healthcare, start with our comparison of HIPAA medical transcription APIs and our guide to how medical transcription APIs work.

FAQ

What is the best speech to text API?

There isn't one for every job. Among the eight compared here, ElevenLabs Scribe v2 has the best independent score on recorded English audio. Deepgram Nova-3 and AssemblyAI Universal-3.6 Pro Realtime are sensible starting points for live audio with speaker labels. AssemblyAI Universal-2, at $0.15 an hour, is the cheapest base rate here for recordings.

Which speech recognition API is the most accurate?

On Artificial Analysis today, Fun-Realtime-ASR-preview leads at 1.7%, followed by Microsoft's MAI-Transcribe-2 at 2.0%. That test has little domain vocabulary, and the order has changed substantially since February, so test on your own audio before relying on it.

Is there a free speech to text API?

Not at production volume from these vendors, but you can try them free. Deepgram gives $200 in credit, Speechmatics gives $100, Azure includes 5 free real-time hours a month, and AWS offers 60 minutes a month for your first 12 months. Open-weight models have no per-minute fee, though you pay for the hardware and engineering.

What's the difference between a speech recognition API and a speech to text API?

In practice, nothing. Vendors use both terms for the same service. Voice recognition is sometimes used to mean identifying who is speaking, which is a different task, so check what a product actually does.

Can I use a general speech-to-text API for medical audio?

Sometimes, but check two things first: whether the vendor will sign a BAA on your plan, and how it handles drug names on your recordings. A general accuracy score won't answer the second question. Our word error rate article explains why.

If your audio is clinical, you can get an API key from Sully and run it on your own recordings, starting with the transcription docs.

Sources

All read on 3 November 2026.

1. Artificial Analysis: Speech to Text leaderboard

2. Artificial Analysis: AA-WER v2.0 methodology article

3. Voice Writer: Real-World Speech-to-text API Leaderboard

4. Deepgram: Best speech-to-text APIs in 2026

5. Deepgram: Pricing

6. AssemblyAI: Pricing

7. AssemblyAI: Security

8. ElevenLabs: API pricing

9. ElevenLabs: Speech to Text capabilities

10. Speechmatics: Pricing

11. OpenAI: API pricing

12. OpenAI: Speech to text guide

13. Google Cloud: Speech-to-Text pricing

14. Google Cloud: Chirp 3 model documentation

15. Google Cloud: HIPAA compliance

16. Microsoft Azure: Speech pricing. Rates read from Azure's public Retail Prices API, US East.

17. Microsoft: Speech containers overview

18. Microsoft: HIPAA and HITECH offering

19. Amazon Transcribe: Pricing

20. Amazon Transcribe: Partitioning speakers

21. AWS: HIPAA eligible services reference

22. Sully.ai: API, Trust, Transcription docs, Streaming docs, Note Generation docs, Text to JSON docs

23. Sully.ai articles: Deepgram alternatives, OpenAI Whisper alternatives, Word error rate