Speech-to-Text APIs in 2026: How to Compare Accuracy, Latency, and Cost

Choosing a speech-to-text API feels straightforward until you realize that every provider claims "best-in-class accuracy" and "industry-leading performance." The benchmarks they cite are rarely comparable, and the demo audio they use bears little resemblance to your actual use case.

This guide cuts through the marketing to help you evaluate STT providers on what actually matters for your application.

The major players

The STT market has several distinct tiers:

Cloud platform APIs

Google Cloud Speech-to-Text, Amazon Transcribe, Microsoft Azure Speech — deep integration with their cloud ecosystems, reliable scaling, and solid accuracy. Good default choices for teams already on these platforms.

Specialist transcription services

AssemblyAI, Deepgram, Rev.ai — companies focused specifically on transcription. Often lead on accuracy or specific features like diarization and sentiment.

Open source / self-hosted

Whisper (OpenAI), Vosk, wav2vec — run on your own infrastructure for privacy and control. Quality varies; Whisper is competitive with commercial options.

Embedded / on-device

Picovoice, Rhino, SpeechBrain — optimized for running locally on devices. Lower accuracy ceiling but no network dependency or privacy concerns. We explore the tradeoffs between on-device and cloud processing in a separate guide.

Accuracy: beyond the WER number

Word Error Rate (WER) is the standard metric, but raw numbers hide important details. For a deeper dive into what WER actually measures and its limitations, see our [WER explainer](/blog/posts/word-error-rate-wer-explained-the-metric-behind-speech-recognition-accuracy).

What the benchmark doesn't tell you

• What audio was tested? Clean studio recordings? Noisy phone calls? WER varies 2-3x between these conditions.
• What vocabulary? General speech vs. your specific domain can be dramatically different.
• What accents? Many models are trained primarily on American English; performance degrades for other accents—a topic we cover in depth in our piece on bias and fairness in speech recognition.

What to actually test

Run your own evaluation with audio that matches your real use case:

1. Collect 20-50 samples from your target environment
2. Create ground truth transcripts (manually verified)
3. Calculate WER for each provider on your data
4. Note qualitative issues: Does it miss names? Mangle technical terms? Struggle with specific speakers?

A provider with 5% WER on benchmarks might hit 15% on your audio. Recent research comparing open source and paid STT services confirms that real-world performance varies dramatically from published benchmarks. The only way to know is to test.

Latency: real-time vs. batch

Different applications have radically different latency requirements. We cover the architectural tradeoffs between real-time and batch transcription in detail elsewhere.

Real-time transcription

For voice control, live captions, or interactive applications:

• Time to first word: How quickly does text start appearing?
• Streaming stability: Does partial recognition flip-flop as more audio arrives?
• End-to-end delay: Total time from speech to final transcript

Target: Sub-second for most interactive applications.

Batch transcription

For processing recordings after the fact:

• Turnaround time: How long until you get results?
• Queue times: Does the service back up under load?
• Cost vs. speed tradeoffs: Many providers offer faster processing at higher prices

Target: Depends on workflow. Minutes might be fine; hours probably isn't.

Cost: understanding pricing models

STT pricing varies significantly in structure:

Per-minute pricing

Most common. Typically $0.006-0.024 per minute of audio. Simple to calculate, scales linearly.

Per-request pricing

Some providers charge per API call plus time. Watch for minimum charges that penalize short utterances.

Feature-based pricing

Premium features (diarization, sentiment, punctuation) often cost extra. A basic transcript might be cheap; a fully-featured one might be 2-3x more.

Volume discounts

High-volume users can often negotiate significant discounts. Don't pay list price if you're processing thousands of hours.

Hidden costs

• Storage fees for keeping recordings
• Data transfer charges for large audio files
• Support tiers for guaranteed response times

Privacy and compliance

Voice data is sensitive. Key questions:

• Where is audio processed? What countries/regions?
• Is audio stored? For how long? Can you opt out?
• Who can access recordings? Are they used for model training?
• What certifications? HIPAA, SOC 2, GDPR compliance?

For sensitive applications (healthcare, legal, financial), privacy requirements may eliminate some providers regardless of accuracy or cost. The NIST Privacy Framework provides useful guidance for evaluating data handling practices.

Making the decision

A practical evaluation framework:

Factor	Weight it higher if...
Accuracy	Transcripts go to humans or are legally significant
Latency	Real-time or interactive application
Cost	High volume, thin margins
Privacy	Sensitive data, regulated industry
Integration	Deep ties to a specific cloud platform

No provider wins on every dimension. Prioritize what matters for your specific use case, test with real audio using datasets like LibriSpeech or TED-LIUM as benchmarks, and plan to re-evaluate as the market evolves.

For noisy environment testing specifically, see our breakdown of what CHiME benchmarks teach us about real-world performance.