With dozens of speech-to-text tools available in 2026, choosing the right one can feel overwhelming. Each promises accuracy and speed, but real-world performance varies significantly based on your audio quality, language needs, and budget. This article is part of our comprehensive guide to speech-to-text, and here we'll cut through the marketing to compare what actually matters.
What Makes a Good Speech-to-Text Tool?
Before diving into specific tools, it's worth understanding the key factors that separate good transcription from great transcription:
- Accuracy (Word Error Rate): The percentage of words transcribed incorrectly. Top tools achieve 3-8% WER in ideal conditions.
- Speaker diarization: The ability to identify and label different speakers in a recording—critical for meetings and interviews.
- Language support: How many languages are supported and how well non-English languages perform.
- Real-time capability: Whether the tool supports live streaming transcription or only batch processing.
- Pricing model: Pay-per-minute, subscriptions, or free tiers with limitations.
Top Speech-to-Text Tools Compared
ElevenLabs Scribe v2
ElevenLabs launched Scribe v2 in late 2025, and it has quickly become a top contender. The model achieves approximately 3.5% WER on English with 93.5% accuracy across 30+ languages on the FLEURS benchmark. Scribe v2 handles up to 48 speakers with excellent diarization, includes word-level timestamps, and offers native entity detection for PII redaction.
Key features: Speaker diarization (up to 48 speakers), audio tagging (laughter, pauses), entity detection, multi-language in single file, real-time mode with 150ms latency.
Pricing: Starting at $0.28 per hour of audio.
Best for: Teams needing accurate transcription with robust speaker identification and compliance features (SOC 2, HIPAA, GDPR).
AssemblyAI Universal-2
AssemblyAI has emerged as a strong enterprise choice, with Universal-2 achieving around 6.7-8.4% word error rate across various benchmarks. It handles up to 50 unique speakers in a single recording and supports over 100 languages.
Key features: Speaker diarization (up to 50 speakers), sentiment analysis, content moderation, auto chapters, PII detection—all via single API.
Pricing: Around $0.15-0.27 per hour, with audio intelligence features at additional cost.
Best for: Teams needing comprehensive audio intelligence features beyond basic transcription.
Deepgram Nova-3
Deepgram's Nova-3 model introduced real-time multilingual transcription with impressive latency under 300ms. The company claims a 54% reduction in word error rate for streaming compared to previous versions.
Key features: Real-time streaming, custom vocabulary training, per-second billing, 50+ language support.
Pricing: Starting at $0.0043 per minute (~$0.26/hour) for pre-recorded audio, with per-second billing that benefits short clips.
Best for: Developers building real-time applications where speed matters as much as accuracy.
OpenAI Whisper
Whisper remains the gold standard for open-source speech recognition. With 1.55 billion parameters and support for 99+ languages, it handles diverse accents and acoustic environments well. The large-v3 model achieves around 9.2% WER.
Important limitation: Whisper does not include native speaker diarization. You'll need to combine it with additional tools like WhisperX or Pyannote for speaker identification, which requires significant engineering effort.
Pricing: Free to self-host, or around $0.006 per minute through OpenAI's API.
Best for: Developers with ML expertise who want full control and can handle the engineering overhead of adding diarization separately.
Google Cloud Speech-to-Text (Chirp)
Google's Chirp model supports over 100 languages with robust speaker diarization and word-level timestamps. Batch processing accuracy sits around 11.6% WER.
Key features: Deep GCP integration, speaker diarization, word-level timestamps, 125+ languages.
Pricing: $0.016 per minute standard (~$0.96/hour), $0.004 per minute for batch processing, plus infrastructure costs.
Best for: Organizations already using Google Cloud who need deep ecosystem integration.
Feature Comparison: Beyond Just Accuracy
Comparing tools by WER alone is misleading—a tool with 9% WER but no diarization isn't comparable to one with 3.5% WER and full speaker identification. Here's a more honest comparison:
| Tool | WER | Diarization | Languages | Real-time | Price/Hour |
|---|---|---|---|---|---|
| ElevenLabs Scribe v2 | ~3.5% | Yes (48 speakers) | 90+ | Yes (150ms) | ~$0.28 |
| AssemblyAI Universal-2 | ~6.7-8.4% | Yes (50 speakers) | 100+ | Yes | ~$0.15-0.27 |
| Deepgram Nova-3 | ~6.8% | Yes (limited) | 50+ | Yes (300ms) | ~$0.26 |
| OpenAI Whisper | ~9.2% | No* | 99+ | No | Free (self-host) |
| Google Chirp | ~11.6% | Yes | 125+ | Yes | ~$0.96+ |
*Whisper requires WhisperX or similar tools for diarization, adding engineering complexity.
The real cost consideration: At 8% WER, expect roughly 15 minutes of editing per hour of audio. At 20%+ WER, that jumps to 90 minutes—potentially costing more in labor than you saved on transcription. But if you need speaker labels and your tool doesn't support diarization, you'll spend even more time manually identifying who said what.
Features Beyond Basic Transcription
Modern speech-to-text tools offer more than raw transcription:
- Speaker diarization: ElevenLabs and AssemblyAI lead here with 48-50 speaker support; Whisper requires additional tooling.
- Timestamps: Word-level timestamps enable precise subtitle generation and navigation.
- Summarization: AssemblyAI and ElevenLabs include AI-powered summaries.
- Entity detection: ElevenLabs Scribe v2 offers native PII detection across 56 categories.
- Custom vocabulary: Deepgram and AssemblyAI allow domain-specific training for technical terminology.
- Real-time streaming: ElevenLabs (150ms), Deepgram (300ms), and Azure excel at low-latency live applications.
Making Your Choice
The right tool depends on your specific situation:
- Need accurate diarization: ElevenLabs Scribe v2 or AssemblyAI—both handle multi-speaker recordings well out of the box.
- Budget-conscious with ML expertise: OpenAI Whisper is free but requires engineering effort to add diarization via WhisperX.
- Building real-time applications: Deepgram's fast latency and per-second billing make it ideal.
- Processing interviews and meetings: AssemblyAI's speaker diarization and summarization features save significant post-processing time.
- Compliance requirements: ElevenLabs Scribe v2 offers SOC 2, HIPAA, GDPR compliance with entity detection.
For those who want solid transcription with speaker identification without the complexity of enterprise tools, lightweight options like Scriby offer a straightforward pay-as-you-go approach—upload your file, get your transcript with speaker labels, pay only for what you use.
Conclusion
The speech-to-text landscape in 2026 offers genuine choices, but comparing tools requires looking beyond headline WER numbers. A tool that's "more accurate" on paper might lack critical features like speaker diarization that you actually need.
ElevenLabs Scribe v2 has raised the bar with its combination of accuracy, diarization quality, and compliance features. AssemblyAI remains strong for teams wanting comprehensive audio intelligence. And while Whisper is technically free, the engineering cost of adding diarization makes it less "free" than it appears for production use.
Focus on matching features to your actual workflow—and test with your typical audio before committing.