This guide compares speech-to-text routes for teams choosing where to send meetings, support calls, interviews, podcasts, live captions, and media archives. The real question is not which model transcribes audio. It is which route gives a specific workflow the right words, speaker ownership, latency, privacy posture, and unit economics.<\/p>
Scope and evidence:<\/strong> this is a docs-verified selection guide, not a new lab benchmark. It does not report firsthand benchmark runs, unpublished audio samples, or paid vendor tests. Provider facts are from the official sources listed at the end, each last checked on 2026-04-24. No vendor paid for inclusion, and there are no affiliate links.<\/p> Jump links:<\/strong> comparison table<\/a> | evaluation method<\/a> | accuracy<\/a> | latency<\/a> | diarization<\/a> | cost<\/a> | sources<\/a><\/p> The comparison usually produces more than one winner. A product may use streaming transcription for live agent assist, batch transcription for nightly QA, and a higher-accuracy diarized route only for disputed calls or executive meetings. Routing by workflow beats standardizing on one vendor because the expensive failure changes by use case.<\/p> Use the table above to shortlist, then run a controlled bake-off. A credible test set should include 10-20 real files per workflow and at least 30 minutes of human-labeled audio per major audio shape; Microsoft recommends 30 minutes to 5 hours of representative audio for accuracy testing[1]<\/sup>. Include clean single-speaker audio, two-person calls, same-room meetings, compressed 8 kHz calls, accents, crosstalk, late joiners, domain vocabulary, and files near your expected size limits.<\/p> Report what was tested, not just who won. State languages, sample size, average duration, microphone conditions, file formats, speaker counts, channel layout, and whether the result came from streaming, synchronous file transcription, batch transcription, or local inference. A 4-minute clean English podcast clip says almost nothing about a 55-minute support call with hold music and account numbers.<\/p> Score six things: WER, high-severity entity accuracy, speaker ownership accuracy, time to first usable partial, time to final transcript, and human correction minutes per audio hour. For diarization, count a speaker-owned statement as correct only when the words and owner are both right. The summary is not safe if the transcript correctly says approve the refund but assigns it to the wrong person.<\/p> Word error rate is useful because it is measurable: insertions plus deletions plus substitutions divided by the human reference word count. Microsoft describes 5-10% WER as good quality, 20% as acceptable but worth improving, and 30% or more as poor quality requiring customization or training[1]<\/sup>. Those thresholds are a screen, not a contract.<\/p> The practical insight: a model with slightly worse WER can still win if it makes fewer critical errors. In many business workflows, one wrong account number or speaker-owned decision costs more than twenty harmless filler-word mistakes.<\/p> Speed is not one metric. For live captions, measure time to first readable partial, how often visible text is rewritten, and how quickly segments finalize. For voice agents, measure whether the transcript arrives early enough to decide on interruption, tool calls, or clarification. For archives, measure queue throughput and retry behavior; no one benefits from paying for real-time speed when the transcript is not read until tomorrow.<\/p> Do not compare a streaming partial transcript against a polished batch transcript. They serve different jobs. A streaming route can look worse on punctuation while still being the right answer for agent assist. A slower batch route can be the better engineering choice for compliance review if it reduces correction time and produces stable timestamps.<\/p> Diarization answers who spoke when. It does not automatically identify real people by name. Mapping Speaker 1 to Priya from finance requires known-speaker references, channel metadata, meeting roster context, login state, or a separate speaker-identification process.<\/p> For call centers with clean stereo, channel-based attribution may beat diarization. For laptop-microphone meetings, diarization is unavoidable. For hybrid meetings, test both: remote participants may be easy to identify while people in the room collapse into one speaker label after 20 minutes.<\/p> Speech-to-text vendors do not price the same thing. OpenAI publishes token pricing with estimated minute costs. Deepgram prices per minute with paid add-ons. AssemblyAI prices per hour with feature add-ons. AWS and Azure meter audio duration in seconds. Google prices audio minutes and bills each channel separately. Local models shift the bill to hardware, engineering, and operations.<\/p> A simple archive example shows why the unit matters. On Google V2 standard recognition, 1,000 mono calls of 30 minutes each equal 30,000 billable minutes, or $480 at the listed first-tier $0.016\/min rate. The same 30,000 minutes on Google Dynamic Batch at $0.003\/min would be $90, assuming the workload can accept lower urgency[10]<\/sup>. But a 30-minute, 4-channel recording becomes 120 billable channel-minutes on Google, while AWS says a two-channel conversation is not charged separately by channel[8]<\/sup>.<\/p>Answer First: Which Route Should You Shortlist?<\/h2>
Head-to-Head Speech-to-Text Comparison<\/h2>
Route<\/th> Best use case<\/th> Diarization<\/th> Latency mode<\/th> Pricing unit<\/th> Notable limits<\/th> Tradeoffs<\/th><\/tr><\/thead> OpenAI<\/td> File transcription that will feed an OpenAI summarization, extraction, or agent pipeline<\/td> gpt-4o-transcribe-diarize<\/code> returns speaker segments and can use known-speaker references[2]<\/sup><\/td>File API, streaming transcript events, and separate Realtime paths; diarized model is not supported in Realtime[2]<\/sup><\/td> Token-priced with estimated minute pricing: gpt-4o-transcribe<\/code> and diarized transcription at $0.006\/min; mini at $0.003\/min[3]<\/sup><\/td>25 MB file upload limit; diarized audio over 30 seconds needs chunking_strategy<\/code>; known-speaker clips are 2-10 seconds[2]<\/sup><\/td>Strong fit when transcript and downstream LLM steps live together; test long-meeting chunking and speaker drift before using it for minutes or compliance records.<\/td><\/tr> Deepgram<\/td> Low-latency voice agents, live captions, and speech products that need streaming-first controls<\/td> diarize=true<\/code> labels speakers, with word-level speaker values in output[5]<\/sup><\/td>Streaming and pre-recorded endpoints<\/td> Per minute; Flux and Nova-3 monolingual list at $0.0077\/min pay-as-you-go, Nova-3 multilingual at $0.0092\/min, and diarization as a paid add-on[4]<\/sup><\/td> Concurrency and add-on choices affect the real bill; diarization, redaction, and keyterm prompting can change the route economics[4]<\/sup><\/td> Often a strong first test for real-time UX; do not compare its streaming partials against another provider’s polished batch output.<\/td><\/tr> AssemblyAI<\/td> AI notetakers, conversation intelligence, and diarization-rich pre-recorded workflows<\/td> speaker_labels<\/code>, speaker-count hints, and speaker_options<\/code>; multichannel settings apply per channel[7]<\/sup><\/td>Pre-recorded and streaming products<\/td> Per hour; Universal-2 is listed at $0.15\/hr, Universal-3 Pro at $0.21\/hr, streaming options at $0.15-$0.45\/hr, with diarization add-ons[6]<\/sup><\/td> Language and feature support differs by model; add-ons should be priced with the base transcript[6]<\/sup><\/td> Rich transcript features reduce application code, but model selection and add-ons need explicit testing against your correction budget.<\/td><\/tr> AWS Transcribe<\/td> AWS-centered call centers, regulated enterprise workloads, and stereo call recordings<\/td> Speaker partitioning can distinguish up to 30 unique speakers and emits speaker labels[9]<\/sup><\/td> Batch and streaming services<\/td> Audio seconds with 15-second minimum per request; tiered per-minute examples; up to two channels are included without separate channel billing[8]<\/sup><\/td> Feature, region, language, and add-on behavior should be checked for the exact service path<\/td> Procurement and cloud integration are often the reason to shortlist it; quality still needs a same-audio bake-off.<\/td><\/tr> Google Cloud Speech-to-Text<\/td> Large offline archives, search indexing, and Google Cloud data pipelines<\/td> Speaker diarization uses min\/max speaker counts and speaker tags in word output[11]<\/sup><\/td> Synchronous, long-running, and Dynamic Batch<\/td> Per minute; V2 standard recognition lists $0.016\/min for the first 500,000 minutes, Dynamic Batch at $0.003\/min, and 1-second rounding[10]<\/sup><\/td> Each audio channel is billed separately; lower-cost Dynamic Batch means lower urgency[10]<\/sup><\/td> Very strong cost lever for offline work; multichannel files can erase the advantage if channel-minutes multiply.<\/td><\/tr> Azure AI Speech<\/td> Microsoft-stack enterprises, mono meeting files, and workflows that may need custom speech evaluation<\/td> Fast Transcription diarization supports a maximum expected speaker count, is mono-only, and labels each phrase[12]<\/sup><\/td> Real-time, Fast Transcription, and batch transcription<\/td> Audio hours measured by audio sent and billed in second increments; batch diarization is included in standard\/custom pricing[13]<\/sup><\/td> Fast Transcription requires newer REST API versions; diarized files should be under the documented duration guidance[12]<\/sup><\/td> Good enterprise fit when governance and Microsoft integration matter; versioning and channel constraints need review before rollout.<\/td><\/tr> Local<\/td> Offline transcription, strict data-control requirements, or very high volume with predictable utilization<\/td> Depends on the chosen stack; diarization is often a separate component from transcription<\/td> Hardware, queue, and model dependent<\/td> GPU\/CPU, storage, engineering time, monitoring, and review cost<\/td> You own updates, scaling, fallback logic, quality drift, security patching, and observability<\/td> Local is not automatically cheaper or more private if transcripts later go to a cloud summarizer or analytics model.<\/td><\/tr><\/tbody><\/table><\/figure> A Better Evaluation Method Than Vendor Demos<\/h2>
Accuracy: WER Is Only the First Screen<\/h2>
Latency: Measure the Moment the Product Can Act<\/h2>
Diarization: Speaker Labels Are a Product Feature, Not Metadata<\/h2>
Question<\/th> Best signal<\/th> Common trap<\/th><\/tr><\/thead> Do I only need agent vs customer?<\/td> Clean channel separation, if available<\/td> Using speaker clustering when stereo channels already provide stronger ownership<\/td><\/tr> Do I need same-room meeting ownership?<\/td> Diarization tested on long files, interruptions, similar voices, and late joiners<\/td> Testing on a short clean clip, then deploying on 60-minute meetings<\/td><\/tr> Do I need named speakers?<\/td> Known-speaker references, roster context, or explicit identity workflow<\/td> Assuming diarization equals authentication<\/td><\/tr> Do I need trustworthy action items?<\/td> Speaker-owned decision accuracy<\/td> Scoring WER while ignoring who said the important sentence<\/td><\/tr><\/tbody><\/table><\/figure> Cost: Normalize Minutes, Hours, Tokens, Channels, and Review Time<\/h2>