Version 2026-07-08-28f9fbe2591d
AI Speech to Text
Scoring data for transcription and speech recognition tools.
Compares transcription quality speed speaker handling export options resilience and cost.
| Rank | Provider | Overall | Best for | Highest metric | Lowest metric | Breakdown |
|---|---|---|---|---|---|---|
| 1 | OpenAI Whisper | 9.2 | Open-source high accuracy | Accuracy 9.6/10 | Speed 8.8/10 | Score details |
Score breakdown for OpenAI WhisperAccuracy9.6/10; 30%; allocated to published score 2.87 Noise Robustness9.4/10; 14%; allocated to published score 1.31 Diarisation9.2/10; 10%; allocated to published score 0.91 Real-time Streaming8.8/10; 10%; allocated to published score 0.88 Speed8.8/10; 10%; allocated to published score 0.87 Speaker Detection9.4/10; 8%; allocated to published score 0.75 Cost Efficiency8.8/10; 8%; allocated to published score 0.70 Punctuation9.2/10; 6%; allocated to published score 0.55 Export Formats9.0/10; 4%; allocated to published score 0.36 Whisper (v4/Turbo) is the gold standard for accuracy and hallucination reduction in 2026. | ||||||
| 2 | Deepgram | 9.1 | Real-time & AI Agents | Speed 9.9/10 | Diarisation 8.9/10 | Score details |
Score breakdown for DeepgramAccuracy9.3/10; 30%; allocated to published score 2.73 Noise Robustness9.0/10; 14%; allocated to published score 1.23 Diarisation8.9/10; 10%; allocated to published score 0.87 Real-time Streaming9.9/10; 10%; allocated to published score 0.97 Speed9.9/10; 10%; allocated to published score 0.97 Speaker Detection9.0/10; 8%; allocated to published score 0.70 Cost Efficiency9.2/10; 8%; allocated to published score 0.72 Punctuation9.4/10; 6%; allocated to published score 0.55 Export Formats9.2/10; 4%; allocated to published score 0.36 Deepgram Nova-3 leads with sub-150ms latency, making it the top choice for voice AI agents. | ||||||
| 3 | AssemblyAI | 9.0 | Speech Intelligence | Speaker Detection 9.6/10 | Cost Efficiency 8.5/10 | Score details |
Score breakdown for AssemblyAIAccuracy9.2/10; 30%; allocated to published score 2.75 Noise Robustness8.8/10; 14%; allocated to published score 1.23 Diarisation9.1/10; 10%; allocated to published score 0.90 Real-time Streaming8.9/10; 10%; allocated to published score 0.89 Speed8.9/10; 10%; allocated to published score 0.88 Speaker Detection9.6/10; 8%; allocated to published score 0.76 Cost Efficiency8.5/10; 8%; allocated to published score 0.68 Punctuation9.2/10; 6%; allocated to published score 0.55 Export Formats9.0/10; 4%; allocated to published score 0.36 AssemblyAI Universal-2 excels at built-in LLM-on-audio and actionable insight extraction. | ||||||
| 4 | Speechmatics | 8.8 | Global Accents | Accuracy 9.4/10 | Cost Efficiency 8.2/10 | Score details |
Score breakdown for SpeechmaticsAccuracy9.4/10; 30%; allocated to published score 2.76 Noise Robustness9.2/10; 14%; allocated to published score 1.26 Diarisation9.3/10; 10%; allocated to published score 0.91 Real-time Streaming8.5/10; 10%; allocated to published score 0.83 Speed8.5/10; 10%; allocated to published score 0.83 Speaker Detection8.8/10; 8%; allocated to published score 0.69 Cost Efficiency8.2/10; 8%; allocated to published score 0.64 Punctuation9.0/10; 6%; allocated to published score 0.53 Export Formats8.8/10; 4%; allocated to published score 0.35 Speechmatics remains the global leader for non-native English and diverse regional dialects. | ||||||
| 5 | Azure AI Speech | 8.7 | Microsoft Enterprise | Speaker Detection 9/10 | Cost Efficiency 8.4/10 | Score details |
Score breakdown for Azure AI SpeechAccuracy8.9/10; 30%; allocated to published score 2.65 Noise Robustness8.6/10; 14%; allocated to published score 1.20 Diarisation8.8/10; 10%; allocated to published score 0.87 Real-time Streaming8.7/10; 10%; allocated to published score 0.86 Speed8.7/10; 10%; allocated to published score 0.86 Speaker Detection9.0/10; 8%; allocated to published score 0.72 Cost Efficiency8.4/10; 8%; allocated to published score 0.67 Punctuation8.8/10; 6%; allocated to published score 0.52 Export Formats8.8/10; 4%; allocated to published score 0.35 Azure AI Speech is the secure choice for air-gapped enterprise and Microsoft-native stacks. | ||||||
| 6 | Google Gemini Flash STT | 8.6 | Multimodal Integration | Speaker Detection 9.2/10 | Noise Robustness 8.4/10 | Score details |
Score breakdown for Google Gemini Flash STTAccuracy8.8/10; 30%; allocated to published score 2.60 Noise Robustness8.4/10; 14%; allocated to published score 1.16 Diarisation8.5/10; 10%; allocated to published score 0.84 Real-time Streaming9.0/10; 10%; allocated to published score 0.88 Speed9.0/10; 10%; allocated to published score 0.88 Speaker Detection9.2/10; 8%; allocated to published score 0.72 Cost Efficiency8.5/10; 8%; allocated to published score 0.67 Punctuation8.6/10; 6%; allocated to published score 0.51 Export Formats8.6/10; 4%; allocated to published score 0.34 Google Gemini Flash integrates video context for industry-leading context-aware transcription. | ||||||
| 7 | Suno/Bark (STT) | 8.2 | Creative Audio | Accuracy 8.5/10 | Punctuation 8/10 | Score details |
Score breakdown for Suno/Bark (STT)Accuracy8.5/10; 30%; allocated to published score 2.53 Noise Robustness8.2/10; 14%; allocated to published score 1.14 Diarisation8.0/10; 10%; allocated to published score 0.79 Real-time Streaming8.2/10; 10%; allocated to published score 0.81 Speed8.2/10; 10%; allocated to published score 0.81 Speaker Detection8.5/10; 8%; allocated to published score 0.68 Cost Efficiency8.0/10; 8%; allocated to published score 0.64 Punctuation8.0/10; 6%; allocated to published score 0.48 Export Formats8.0/10; 4%; allocated to published score 0.32 Suno's entry into the STT market focuses on emotional prosody and creative audio workflows. | ||||||
| 8 | Otter.ai | 8.1 | Meetings & Collaboration | Speaker Detection 8.8/10 | Cost Efficiency 8.1/10 | Score details |
Score breakdown for Otter.aiAccuracy8.4/10; 30%; allocated to published score 2.42 Noise Robustness8.2/10; 14%; allocated to published score 1.10 Diarisation8.7/10; 10%; allocated to published score 0.84 Real-time Streaming8.5/10; 10%; allocated to published score 0.82 Speed8.5/10; 10%; allocated to published score 0.82 Speaker Detection8.8/10; 8%; allocated to published score 0.68 Cost Efficiency8.1/10; 8%; allocated to published score 0.62 Punctuation8.4/10; 6%; allocated to published score 0.48 Export Formats8.2/10; 4%; allocated to published score 0.32 Otter.ai remains the user favorite for collaborative meeting notes and automated summaries. | ||||||
| 9 | AWS Transcribe | 8.0 | Enterprise AWS Pipelines | Cost Efficiency 8.5/10 | Speaker Detection 8/10 | Score details |
Score breakdown for AWS TranscribeAccuracy8.4/10; 30%; allocated to published score 2.42 Noise Robustness8.2/10; 14%; allocated to published score 1.10 Diarisation8.3/10; 10%; allocated to published score 0.80 Real-time Streaming8.4/10; 10%; allocated to published score 0.81 Speed8.4/10; 10%; allocated to published score 0.81 Speaker Detection8.0/10; 8%; allocated to published score 0.62 Cost Efficiency8.5/10; 8%; allocated to published score 0.65 Punctuation8.2/10; 6%; allocated to published score 0.47 Export Formats8.4/10; 4%; allocated to published score 0.32 AWS Transcribe is the robust workhorse for contact centers and large-scale AWS data pipelines. | ||||||
| 10 | Rev AI | 7.9 | Human-verified Hybrid | Export Formats 8.6/10 | Noise Robustness 7.8/10 | Score details |
Score breakdown for Rev AIAccuracy8.2/10; 30%; allocated to published score 2.40 Noise Robustness7.8/10; 14%; allocated to published score 1.06 Diarisation8.2/10; 10%; allocated to published score 0.80 Real-time Streaming8.3/10; 10%; allocated to published score 0.81 Speed8.3/10; 10%; allocated to published score 0.81 Speaker Detection7.9/10; 8%; allocated to published score 0.61 Cost Efficiency7.8/10; 8%; allocated to published score 0.61 Punctuation8.0/10; 6%; allocated to published score 0.47 Export Formats8.6/10; 4%; allocated to published score 0.33 Rev AI remains the top choice for those needing a hybrid of AI speed and human-verified precision. | ||||||
Scoring methodology
| Metric | Weight | Why it matters | Tie-break |
|---|---|---|---|
| Accuracy | 30% | The transcript must reliably reflect the spoken words | 1 |
| Noise Robustness | 14% | Maintains transcription quality outside studio conditions | 2 |
| Diarisation | 10% | Identifies who said what in multi-speaker recordings | 3 |
| Real-time Streaming | 10% | Enables live captions and interactive speech applications | 4 |
| Speed | 10% | Supports practical turnaround for batch and live pipelines | 5 |
| Speaker Detection | 8% | Detects changes between speakers reliably | 6 |
| Cost Efficiency | 8% | Enables transcription at scale without runaway cost | 7 |
| Punctuation | 6% | Improves readability and downstream language processing | 8 |
| Export Formats | 4% | Supports subtitles timestamps and structured output formats | 9 |
Known limitations
Scores reflect a point-in-time editorial assessment and accuracy varies with audio language and environment.
Recent changes
Initial release with 10 providers.
Machine-readable access
This release is available in structured JSON and CSV. The versioned URLs are immutable and are the preferred targets for retrieval, citation and reproducible analysis.
Version history
- 2026-07-08-28f9fbe2591d — Initial release with 10 providers.