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description=Universal-3.5 Pro benchmarks across pre-recorded and streaming speech-to-text accuracy, entity recognition, diarization, multilingual support, latency, and cost.;

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te build and ship with assemblyai from api reference to step by step guides resources documentation api reference cookbooks support changelog status resources latest release universal 3 5 pro our new flagship speech to text model handles real world audio the way it actually happens available for real time and pre recorded audio resources blog partners research benchmarks security playground pricing contact us get started log in platform enterprise customers developers resources playground pricing contact us log in get started get started platform platform overview models apis and infrastructure in one place build voice into any product on any stack transcribe pre recorded speech to text api realtime speech to text api sync speech to text api understand speech understanding api guardrails and safety llm gateway build voice agents inference self hosted voice ai cloud use cases voice agents ai notetakers call analytics medical transcription dictation agent assist ai scribes languages english spanish portuguese french german russian hindi dutch japanese italian all 99 languages customers all customer stories top voice ai companies are building with assembly see all stories zoom leverages assemblyai to help advance its ai research and development read the story siro achieves a 90 reduction in customer complaints and support tickets watch the video resources documentation api reference cookbooks support changelog status latest release universal 3 5 pro our new flagship speech to text model handles real world audio the way it actually happens available for real time and pre recorded audio realtime pre recorded resources blog partners research benchmarks security benchmarks assemblyai universal 3 5 pro benchmarks benchmarks for universal 3 5 pro across pre recorded audio and universal 3 5 pro realtime across streaming radio_button_checked pre recorded audio radio_button_unchecked streaming word error rate missed entity rate diarization multilingual code switching price performance word error rate wer is calculated as substitutions insertions deletions total words in the reference transcript it is the standard metric for evaluating speech to text accuracy average normalized wer across selected datasets lower is better 4 35 5 24 5 34 5 50 5 87 5 91 6 47 6 66 7 02 7 16 17 39 assemblyai universal 3 5 pro mistral voxtral mini openai gpt 4o transcribe cohere transcribe elevenlabs scribe v2 qwen3 asr gladia deepgram nova 3 azure batch grok soniox wer by dataset expand_more dataset assemblyai universal 3 5 pro elevenlabs scribe v2 mistral voxtral mini openai gpt 4o transcribe cohere transcribe qwen3 asr gladia deepgram nova 3 azure batch grok soniox synthetic medical 0 33 0 41 1 25 0 55 1 33 1 15 1 23 0 51 1 55 1 38 0 72 accented english india 5 19 5 92 6 44 6 49 6 39 6 61 6 78 7 77 8 04 6 60 53 48 general speech 6 24 7 19 6 64 7 45 8 35 7 01 11 08 8 81 8 42 9 76 7 55 webinar speech 5 63 9 96 6 65 6 87 5 91 8 85 6 79 9 55 10 07 10 90 7 79 average 4 35 5 87 5 24 5 34 5 50 5 91 6 47 6 66 7 02 7 16 17 39 missed entity rate missed entity rate measures errors on named entity categories including names organizations locations medical terms money occupations temporal expressions and urls unlike aggregate wer it isolates the tokens that carry the most semantic weight in downstream applications missed entity rate by provider lower is better name organization location medical money occupation temporal url 24 1 19 0 8 7 13 9 43 7 9 0 16 3 54 4 25 9 20 8 10 0 19 8 39 2 9 4 6 2 65 0 29 2 23 4 12 2 16 1 37 3 13 0 13 8 65 5 20 9 16 6 11 5 10 8 76 9 9 1 20 4 47 1 25 6 19 3 13 6 15 7 76 8 9 7 19 4 54 8 36 1 31 6 24 3 31 1 80 4 10 6 29 7 98 1 assemblyai universal 3 5 pro mistral voxtral mini openai gpt 4o transcribe elevenlabs scribe v2 deepgram nova 3 nvidia canary 1b mer by entity type expand_more entity type assemblyai universal 3 5 pro mistral voxtral mini openai gpt 4o transcribe elevenlabs scribe v2 deepgram nova 3 nvidia canary 1b name 24 08 25 89 29 23 20 87 25 58 36 08 organization 18 99 20 80 23 40 16 57 19 25 31 60 location 8 72 9 98 12 21 11 54 13 57 24 27 medical 13 87 19 78 16 07 10 80 15 69 31 07 money 43 70 39 22 37 30 76 88 76 82 80 40 occupation 9 01 9 43 13 01 9 12 9 67 10 59 temporal 16 33 6 21 13 78 20 42 19 42 29 70 url 54 37 65 05 65 53 47 09 54 79 98 06 diarization diarization segments audio by speaker we report cpwer concatenated minimum permutation word error rate which jointly evaluates transcription and speaker assignment average cpwer across dipco callhome notsofar and ami lower is better 30 17 30 35 35 26 36 60 36 88 37 52 37 93 42 58 50 64 114 81 assemblyai universal 3 5 pro azure elevenlabs scribe v2 speechmatics gladia mistral voxtral deepgram grok google soniox cpwer by dataset expand_more dataset assemblyai universal 3 5 pro azure elevenlabs scribe v2 speechmatics gladia mistral voxtral deepgram grok google soniox dipco 33 48 33 23 48 78 36 88 41 90 38 04 38 31 41 60 56 40 126 82 callhome dev eval 17 18 20 28 14 82 21 41 20 06 19 50 23 70 36 85 29 89 103 85 callhome train 17 78 20 14 16 18 20 61 21 02 19 36 25 47 33 68 31 12 102 55 notsofar test 37 02 35 68 44 37 56 53 47 81 51 91 48 24 52 35 63 83 111 86 notsofar dev 48 22 45 38 51 29 63 38 56 12 61 42 62 55 58 92 75 63 116 41 ami 27 36 27 39 36 14 20 82 34 34 34 86 29 28 32 10 46 96 127 36 average 30 17 30 35 35 26 36 60 36 88 37 52 37 93 42 58 50 64 114 81 multilingual global wer across the evaluated multilingual benchmark suite with language level breakdowns where available global multilingual wer lower is better 7 84 8 22 9 52 10 50 11 13 13 36 14 39 15 71 assemblyai universal 3 5 pro speechmatics enhanced openai gpt 4o transcribe mistral voxtral mini elevenlabs scribe v2 cohere transcribe openai whisper 1 deepgram nova 3 global wer and language breakdown expand_more model global wer evaluated languages german spanish french italian portuguese assemblyai universal 3 5 pro 7 84 5 20 9 07 6 57 10 17 6 74 6 65 speechmatics enhanced 8 22 5 20 10 30 5 84 10 09 7 88 7 01 openai gpt 4o transcribe 9 52 15 20 mistral voxtral mini 10 50 10 20 elevenlabs scribe v2 11 13 19 20 cohere transcribe 13 36 openai whisper 1 14 39 deepgram nova 3 15 71 code switching code switching benchmarks test common voice derived english paired with german spanish french italian and portuguese average code switching wer lower is better 7 60 10 07 12 42 12 83 21 88 22 72 22 72 28 13 36 05 39 38 42 71 46 19 46 39 51 24 52 40 assemblyai universal 3 5 pro elevenlabs scribe v2 soniox deepgram nova 3 qwen3 asr mistral voxtral mini vibevoice cohere transcribe speechmatics grok openai gpt 4o transcribe gladia aws transcribe azure batch gemini wer by language pair and condition lower is better en es long en de short en fr short en it short en pt short 5 9 5 9 7 7 6 9 11 7 5 5 10 6 11 4 11 6 11 3 8 5 11 8 14 8 11 6 15 5 10 1 12 7 14 2 12 5 14 6 9 1 24 9 27 0 22 0 26 4 7 8 27 1 29 6 27 1 21 9 11 4 24 4 27 4 22 8 27 6 28 8 27 4 32 5 27 3 24 6 27 3 32 8 35 8 43 0 41 5 12 5 46 7 47 3 42 5 47 9 34 9 42 9 45 5 46 6 43 7 49 7 47 1 42 4 49 4 42 3 43 0 46 0 51 2 47 7 44 1 48 5 55 6 54 7 50 8 46 5 47 5 53 2 56 9 55 4 49 0 assemblyai universal 3 5 pro elevenlabs scribe v2 soniox deepgram nova 3 qwen3 asr mistral voxtral mini vibevoice cohere transcribe speechmatics grok openai gladia aws transcribe azure batch gemini price performance choose a metric to see how each provider s accuracy compares at their price point bottom left is best price per hour against the selected metric bottom left is best wer mer diarization cpwer 0 15 0 20 0 25 0 30 0 35 0 40 0 45 3 4 5 6 7 8 9 best value assemblyai universal 3 5 pro openai gpt 4o transcribe elevenlabs scribe v2 deepgram nova 3 azure batch pricing from artificial analysis demo listen to the difference play the preloaded sample and compare the transcripts side by side word error rate missed entity rate diarization multilingual code switching medication name changes ramipril becomes a different medication which can change the patient record ramipril medication metformin medication 0 00 0 00 import assemblyai as aai aai settings api_key your_api_key config aai transcriptionconfig speech_model universal 3 5 pro transcript aai transcriber transcribe patient intake mp3 config config print transcript text truth i take this medication called i think it s like a lipitor and then i take ramipril for the blood pressure assemblyai i take this medication called like it s like a lipitor and then i take um ramipril for the blood pressure competitor competitor deepgram nova 3 openai whisper 1 elevenlabs scribe v2 i take this medication called like it s like a lipitor and then i take olanopril for the blood pressure email address changes jonjay freemail com becomes variants that would bounce or route to the wrong inbox email address business email card number alphanumeric code order number 0 00 0 00 import assemblyai as aai aai settings api_key your_api_key config aai transcriptionconfig speech_model universal 3 5 pro format_text true transcript aai transcriber transcribe support call mp3 config config print transcript text truth just use my personal email it s jonjay freemail com that s j o n j a y freemail com so jonjay freemail com assemblyai just use my personal email it s jonjay freemail com that s j o n j a y freemail com so jonjay freemail com competitor competitor deepgram nova 3 openai whisper 1 just use my personal email it s johnjay female com that s jonjay freemail com so johnj freemail com multiple speakers collapse into one label distinct turns from chris sally donald and jenny are merged under speaker 0 county fair planning service menu discussion 0 00 0 00 import assemblyai as aai aai settings api_key your_api_key config aai transcriptionconfig speech_model universal 3 5 pro speaker_labels true transcript aai transcriber transcribe meeting mp3 config config for u in transcript utterances print f speaker u speaker u text truth chris yeah sally mmm donald i was thinking in about uh four months they re having the county fair perhaps you can set a booth up at the county fair chris that s also a great idea um we would have to coordinate that with the city would anyone um be willing to take care of that jenny yes i can take care of that and talk to the city coordinate it with them i think it s a great idea that we have it at the fair like uh donald mentioned chris um ok so uh what else uh does anybody have any ideas of what assemblyai a yeah i was thinking about uh 4 months they re having the county fair perhaps we could set a booth up at the county fair b that s also a great idea um we would have to coordinate that with the city would anyone be willing to take care of that c yes i can take care of that and talk to the city coordinate it with them i think it s a great idea that we have it at the fair like donald mentioned b um okay so uh what else uh does anybody have any ideas of what competitor deepgram diarization 0 yeah mhmm i was thinking that in about four months they re having the county fair perhaps you could set a booth up at the county fair that s also a great idea we would have to coordinate that with the city would anyone be willing to take care of that yes i can take care of that and talk to the city coordinate it with them i think it s a great idea that we have it at the fair like donald mentioned 0 okay so what else does anybody have any ideas of what a band name changes meaning nofx becomes no effect turning a name into a phrase portuguese band names italian literature portuguese reporter name 0 00 0 00 import assemblyai as aai aai settings api_key your_api_key config aai transcriptionconfig speech_model universal 3 5 pro language_detection true transcript aai transcriber transcribe interview mp3 config config print transcript text truth pennywise nofx mc red bad religion são as minhas favoritas assemblyai pennywise no fx mc red bad religion são as minhas favoritas competitor elevenlabs scribe v2 pennywise no effect mc red bad religion são as minhas favoritas airline name changes nigeria airways becomes ligera airways inside a spanish sentence nigeria airways quiet thoroughfare san river 0 00 0 00 import assemblyai as aai aai settings api_key your_api_key config aai transcriptionconfig speech_model universal 3 5 pro language_detection true transcript aai transcriber transcribe travel show mp3 config config print transcript text truth la aerolínea vino a reemplazar a nigeria airways assemblyai la aerolínea vino a reemplazar a nigeria airways competitor elevenlabs scribe v2 la aerolínea vino a reemplazar a ligera airways benchmarks run your own benchmark test on your own audio want to do it right read our benchmarking guide in the docs learn more get a custom benchmark word error rate semantic wer missed entity rate latency ttct price performance word error rate wer is calculated as substitutions insertions deletions total words in the reference transcript streaming models transcribe with less context than pre recorded accuracy evaluated on final transcripts average wer lower is better 5 53 8 87 9 39 10 10 10 64 assemblyai universal 3 5 pro realtime deepgram flux deepgram nova 3 cartesia deepgram nova 3 multi semantic word error rate semantic wer applies text normalization before scoring equivalent forms like fifteen vs 15 or dr vs doctor are treated as matches this is the key metric for voice agent pipelines where meaning matters more than surface formatting semantic wer lower is better 1 18 1 29 1 34 1 62 1 75 2 85 3 06 3 12 azure stt soniox assemblyai universal 3 5 pro realtime deepgram nova 3 aws transcribe google stt openai gpt 4o transcribe elevenlabs scribe v2 source pipecat stt benchmark missed entity rate missed entity rate measures wer on specific entity types during real time transcription proper nouns alphanumerics email addresses and postal codes missed entity rate proper nouns alphanumerics emails addresses lower is better 5 38 7 18 8 47 18 32 assemblyai universal 3 5 pro realtime deepgram nova 3 deepgram nova 3 multi openai gpt 4o transcribe medical missed entity rate normalized average lower is better 10 46 14 33 15 65 21 62 25 72 assemblyai universal 3 5 pro realtime deepgram nova 3 azure deepgram nova 3 multi openai gpt 4o transcribe time to complete turn ttct the interval from when a speaker stops talking to when the final transcript segment arrives the latency that determines how responsive a voice agent feels in a turn based conversation measured as the median across all test utterances median ttct ms lower is better 247ms 249ms 266ms 281ms 335ms 495ms 637ms 878ms 1016ms 1136ms deepgram nova 3 soniox cartesia ink elevenlabs scribe v2 assemblyai universal 3 5 pro realtime speechmatics openai gpt 4o transcribe google stt azure stt aws transcribe source pipecat stt benchmark time to final segment for production voice agents p95 latency is often more telling than the median universal 3 5 pro realtime measures 534ms at p95 price performance c...