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AI speech recognition used to feel like a novelty: fun for a few seconds of dictation, then disappointing the moment anyone spoke with an accent, over background noise or about a topic outside the training data. In 2026 the picture has flipped. Whisper v3 Turbo runs on a MacBook, transcribes a one-hour podcast in under three minutes and reaches a Word Error Rate of under five percent on clean English. Deepgram Nova-3 streams a live panel discussion with sub-300ms latency and a confidence score per token. AssemblyAI pipes speaker-diarised JSON straight into Notion, and Otter.ai quietly produces meeting minutes while you focus on the conversation. This guide walks through what actually works in 2026, how much it costs, how to set it up and where the privacy traps still hide.
Short answer
AI speech recognition in 2026: why Whisper reshaped the market
Before September 2022 the speech-to-text market was a closed club. Google, Microsoft, Amazon and a handful of specialists like Nuance charged roughly $1.40 per audio hour, locked recognition behind cloud APIs and offered modest quality on anything outside broadcast English. Then OpenAI open-sourced Whisper — trained on 680,000 hours of multilingual, multi-task audio — and the economics of the field collapsed almost overnight. A single model, downloadable to any laptop with 10 GB of free disk space, delivered accuracy that rivalled paid services for most real-world audio.
Three years and five release cycles later, the market looks fundamentally different. Whisper v3 Turbo, shipped in late 2025, compresses the original large-v3 architecture to about 809 million parameters while keeping almost all of its accuracy. It transcribes roughly eight times faster than the original large model and runs happily on a MacBook Pro M3 or a mid-range gaming GPU. Commercial providers responded not by trying to beat Whisper on raw accuracy — they accepted they could not — but by competing on features Whisper does not ship: real-time streaming, multi-channel speaker diarization, enterprise-grade redaction, managed uptime and compliance certifications like SOC 2 and HIPAA.
The result is a healthier ecosystem. You can pick Whisper locally when privacy and cost dominate, reach for Deepgram or AssemblyAI when you need streaming and diarization at scale, or delegate meeting minutes entirely to Otter.ai or Microsoft Teams. The friction is no longer whether speech recognition works; it is choosing the right tool for the specific workload.
The six transcription tools that actually matter in 2026
Dozens of vendors advertise transcription, but six cover almost every realistic 2026 use case. OpenAI Whisper v3 Turbo is the open-source baseline, available as a weights file you run yourself or as a hosted API at OpenAI. AssemblyAI leads on English-language structured output — summaries, chapters, sentiment, speaker labels and PII redaction all come in one JSON response. Deepgram Nova-3 owns the real-time streaming niche; it is the tool to reach for when you build live captions, call-centre analytics or a voice agent. Otter.ai targets knowledge workers who live in Zoom, Teams and Google Meet, producing clean minutes, action items and searchable archives. Rev.com sits between machine and human: fully automated at machine-speech pricing, or human-reviewed at 99%+ accuracy for contracts, depositions and media work. Trint focuses on editorial teams — journalists and producers who need to navigate long audio, strike quotes and export to Adobe Premiere or Final Cut.
Around those six, a second tier remains relevant in specific contexts. Google Cloud Speech-to-Text still ships the broadest language coverage (140+ languages). Microsoft Azure Speech is a default choice inside Microsoft-heavy enterprises. AWS Transcribe integrates natively with Amazon Connect call centres. Aleph Alpha and IONOS offer German-hosted options for organisations that need provable EU data residency. For most individual and small-team buyers, however, the six names above cover the ground.
OpenAI Whisper v3 Turbo locally: setup, hardware, accuracy
Running Whisper locally is the most satisfying trick in the 2026 toolkit because it is genuinely easy and genuinely private. The minimal setup takes about ten minutes on any modern laptop. On macOS, brew install ffmpeg and then pip install openai-whisper pulls in the model weights on first use. On Windows, a Python 3.11 virtual environment plus a CUDA-capable GPU driver achieves the same result. The command whisper interview.mp3 --model turbo --language en --output_format txt produces a transcript in roughly one-eighth of real time on a 16 GB MacBook M3 Pro and in about one-twentieth on an RTX 4070.
Hardware requirements scale with model size. Whisper tiny (39M parameters) runs comfortably on CPU only and is acceptable for rough meeting notes. Whisper base and small give better accuracy for short English clips and still fit under four gigabytes of RAM. The sweet spot for most users in 2026 is the turbo model: roughly 6 GB of VRAM or 10 GB of RAM, accuracy almost indistinguishable from large-v3 on English and good results across the full 90+ language set. If you need maximum accuracy for specialist audio — legal dictation, medical interviews, multi-speaker panels in German or French — large-v3 remains the right choice and needs about 10 GB of VRAM.
Accuracy on clean English broadcast audio sits at a Word Error Rate between 3% and 5%. On a typical one-on-one Zoom recording with a cheap USB headset, expect 5% to 8%. Heavier accents, simultaneous speakers or background music push the number above 10% quickly. The single biggest lever for better accuracy is the --initial_prompt parameter, where you can seed the model with proper nouns and domain vocabulary. Feeding Whisper a paragraph listing every team member’s surname, the names of internal products and the abbreviations your company uses before it starts transcribing can halve the error rate on those exact terms.
For production workflows, the community ports whisper.cpp (a pure C++ implementation) and faster-whisper (based on CTranslate2) are worth knowing. faster-whisper achieves roughly 4× the throughput of the reference implementation on the same hardware, making batch transcription of hundreds of hours of audio practical on a single workstation overnight.
Whisper API at OpenAI: cost, speed, privacy
Not everyone wants to manage their own weights and GPUs. OpenAI hosts Whisper as a paid API at roughly $0.006 per audio minute in 2026, or about $0.36 per hour. That is a third of what cloud providers charged before Whisper existed, and it avoids the twenty-minute setup entirely. You send a file under 25 MB (longer audio must be chunked), specify the response format — plain text, SRT, VTT or verbose JSON with timestamps — and receive a transcript in seconds.
The API is the right call for teams that transcribe a few hundred hours a month, do not want to maintain infrastructure and can accept US-based processing. It is the wrong call in three situations. First, when the audio is sensitive: medical consultations, legal privilege, employee grievances, personal therapy recordings. Second, when you need streaming output — the public Whisper endpoint is batch only; true streaming from OpenAI requires the Realtime API with a different pricing model. Third, when your monthly volume exceeds roughly 800 hours, at which point a dedicated local setup amortises faster than the API bill.
Privacy-wise, OpenAI states that API inputs are not used to train models by default and are retained for 30 days for abuse monitoring. That is acceptable for many workflows, insufficient for anything that touches health data, legal discovery or personal data under strict GDPR interpretation. For those cases, read the GDPR section below and default to local Whisper or an EU-hosted provider.
AssemblyAI, Otter.ai, Rev.com and Trint compared for enterprise
Enterprise speech recognition is rarely about the transcript alone. Once you transcribe 200 hours of sales calls, you need summaries, topics, sentiment and speaker attribution to do anything with the output. This is where the four specialists above diverge, each covering a slightly different shape of work.
AssemblyAI is the developer-first platform. A single API call returns transcript, paragraph breaks, chapter titles, 15-second summaries, per-speaker sentiment, detected entities (names, organisations, monetary amounts) and automatically redacted PII. Pricing in 2026 sits at about $0.37 per hour for the base model and $0.65 per hour for the full “Universal-2” model with all features enabled. Response latency for a 60-minute recording is typically under two minutes. The English output is excellent; other languages are usable but trail Whisper. Most product teams building voice-of-customer analytics, compliance monitoring or interview tooling end up here.
Otter.ai targets the end user rather than the developer. It joins your Zoom, Google Meet or Teams call as a bot, produces a live transcript, writes a bullet-point summary and emails it afterwards. Action items are extracted automatically. A Pro plan at roughly $17 per user per month covers unlimited transcription on most team sizes. Otter is the lowest-friction choice for knowledge-worker teams who do not want to think about audio pipelines.
Rev.com is a hybrid. Its “AI Transcription” tier costs $0.25 per minute for human-reviewed transcripts at 99%+ accuracy, or $0.02 per minute for pure machine output. Rev dominates in media production, legal discovery and academic research where a single wrong word can break a quote or a deposition. The platform also does captioning, subtitle translation and a growing set of editorial tools.
Trint serves journalists, documentary producers and podcast editors. Its editor lets you navigate a two-hour interview by clicking on text, highlight quotes, strike sections and export directly to a video NLE. Multi-lingual support covers about 40 languages. Pricing starts around $48 per user per month on a starter plan. If your workflow is “interview, find the good bits, cut the piece”, Trint pays back its monthly fee within the first two projects.
Choosing between them is mostly about the shape of output. AssemblyAI if you are building software. Otter if meetings dominate. Rev if accuracy must be provable and legally defensible. Trint if your next step is editing video or a podcast.
Deepgram Nova-3: the streaming specialist for live transcription
Real-time transcription is a fundamentally different problem from batch transcription. Instead of receiving a finished audio file, the model must start emitting text within a few hundred milliseconds of the first phoneme, correct earlier guesses as more context arrives, and handle interruptions, overlap and background noise without ever pausing the stream. This is the niche Deepgram owns.
Nova-3, released at the end of 2025, is Deepgram’s fourth-generation streaming model. Its end-to-end latency sits at roughly 250ms on broadband connections, Word Error Rate is competitive with Whisper large-v3 on clean audio and the model supports on-the-fly speaker diarization, keyword boosting and interim results. Pricing for the streaming API is about $0.0043 per minute — roughly a quarter of OpenAI’s Whisper API — which reflects Deepgram’s architectural bet on custom acoustic models rather than giant general-purpose ones.
The tool is indispensable in three scenarios. Live captioning for conferences and broadcasts, where latency below 500ms is the difference between watchable and unreadable. Voice-agent pipelines, where a user’s audio must reach a downstream LLM fast enough to feel conversational. And real-time call-centre analytics, where supervisors monitor tone and keywords as the call is happening. Outside these live contexts Whisper typically wins on pure accuracy; inside them Deepgram is the default.
A practical integration tip: Deepgram’s Python and Node SDKs expose a WebSocket client that masks most of the complexity of streaming audio. For a first prototype, stream microphone input at 16 kHz PCM directly to the endpoint and subscribe to the Results event. The whole proof of concept fits in about 40 lines of code.
Speaker diarization: who said what with 5 people in a meeting
Getting the transcript right is only half of meeting transcription. The other half — knowing which words belong to which person — is called speaker diarization, and it remains noticeably harder than recognition itself. A five-person roundtable with overlapping interjections, cross-talk and someone dialling in over a patchy connection is a genuinely difficult signal-processing problem.
In 2026, AssemblyAI’s Universal-2 model leads the English-language diarization benchmarks, with a Diarization Error Rate around 8–10% on multi-speaker Zoom recordings. Deepgram Nova-3 trails by a few points but handles live streaming, which AssemblyAI does not. Whisper itself does not diarize; the community workaround is to pair Whisper with pyannote.audio, an open-source speaker-clustering library that segments the audio and assigns labels before Whisper transcribes each chunk. The combination is surprisingly capable and costs nothing beyond compute, but it is noticeably slower than a single-pass API.
Three practical habits improve diarization results more than switching tools. First, record each participant on a separate channel when possible — a dedicated microphone per speaker reduces the problem from “who is talking” to simply “which channel has signal”. Zoom, Teams and Riverside all support multi-track recording. Second, ask participants to introduce themselves at the start of the meeting. Systems that support keyword boosting or speaker enrollment (Otter, Deepgram, Gladia) can use those first few sentences to build a speaker profile. Third, clean the room: soft furnishings, a single mic gain setting and pushing to mute when not speaking cut diarization errors in half.
For forensic accuracy — legal depositions, HR investigations — budget human review. No 2026 model is good enough to attribute a contested sentence to a specific person with the confidence a court or a disciplinary process requires.
Transcribing accents, dialects and domain jargon in 2026
The classic complaint against speech recognition is that it works on American radio voices and nothing else. Whisper largely fixed the accent problem: thanks to its multilingual, multi-accent training data, Scottish, Indian, Nigerian and non-native English all transcribe within a few percentage points of standard American accuracy. Regional German, Swiss German, Austrian dialects and strong French regional accents also work far better than they did in 2022.
Domain jargon is a different story. Every speech model encodes a prior over which word sequences are likely, and “thrombocytopenia”, “EBITDA reconciliation” or “CVE-2026-1234” are not in that prior. The result is plausible-sounding but wrong transcriptions: homophones, near-homophones or generic substitutes. Three techniques address this in 2026.
The simplest is the initial prompt or custom vocabulary list. Whisper accepts an initial_prompt argument; OpenAI’s hosted API accepts the same. AssemblyAI, Deepgram and Azure expose a “word boost” or “custom vocabulary” parameter where you list domain-specific terms and receive an accuracy boost on them. A few minutes spent listing the 50 most important proper nouns, product names and abbreviations cuts jargon error rates dramatically.
The heavier option is fine-tuning. Whisper’s weights are open and can be fine-tuned on a few hours of in-domain audio. Hugging Face publishes well-maintained recipes; a cloud fine-tuning run on 10 hours of medical interviews or legal dictation takes a few hours on a single A100 and produces a model that is materially better at your exact use case. For organisations that transcribe thousands of hours of a specific register, this repays the effort.
The third technique is post-processing with a general-purpose LLM. Pass the raw transcript to GPT-4 class, Claude or Gemini with a prompt that includes the domain glossary and instructions to correct only technical terms. This catches the long tail of jargon errors without touching the rest of the transcript and is cheap enough to apply at scale.
GDPR-compliant AI transcription: providers with EU data centres
Speech is personal data. Under the GDPR, processing it requires a legal basis (typically explicit consent), transparent information about where and how the audio is processed, and, if it leaves the EU, appropriate safeguards such as Standard Contractual Clauses. Many of the default 2026 transcription services process audio in US data centres by default, which for German healthcare, legal, HR and public-sector work is a problem.
Three paths lead to GDPR-safe transcription. The first is local Whisper: audio never leaves the machine, there is no controller-processor relationship, and the only compliance task is your own internal processing record. This is the cleanest option for sensitive work and one reason Whisper adoption exploded in German SMBs after 2023. Our companion article on GDPR-compliant AI transcription for SMBs walks through a full on-premise workflow.
The second path is EU-hosted APIs. Microsoft Azure Speech offers “Germany West Central” and “Sweden Central” regions with data residency guarantees and an available Data Processing Addendum (DPA). AWS Transcribe runs in Frankfurt and Ireland with equivalent contracts. Aleph Alpha, a Heilbronn-based AI company, offers speech services fully inside Germany, which many public-sector and regulated customers prefer for procurement reasons. IONOS AI Model Hub provides another German-hosted option.
The third path is a managed, compliance-focused reseller. Providers like Amberscript (Netherlands), Scriptix and Speechmatics operate in the EU, sign a DPA without drama and are happy to answer a procurement questionnaire. They cost a bit more than the default US APIs but remove legal review time from each new project.
Whichever path you choose, three documents matter: the provider’s Data Processing Addendum, its Standard Contractual Clauses where transfers are involved, and a Transfer Impact Assessment (TIA) you perform yourself. Keep them in a single folder per provider. When an auditor or a data subject asks, retrieval is measured in minutes rather than days.
Workflow: meeting transcript in 10 minutes with redaction
A concrete workflow is worth a thousand abstract comparisons. Here is a ten-minute pipeline that produces a speaker-labelled, redacted, summarised meeting transcript on a standard laptop in May 2026.
Step one: record the meeting with multi-track audio. Zoom’s “Record separate audio file for each participant” setting takes one click. Five participants produce five .m4a files plus a combined mix. Total time: zero extra minutes.
Step two: transcribe each track individually with Whisper v3 Turbo. The command for f in *.m4a; do whisper "$f" --model turbo --language en --output_format json; done processes a one-hour meeting in roughly seven minutes on an M3 Pro. Because each file contains exactly one speaker, no diarization is needed — the filename is the speaker label.
Step three: merge the JSON transcripts by timestamp using a 30-line Python script. The result is a single chronological transcript where every line is prefixed with the speaker’s name. This step takes under a minute, most of which is the script execution.
Step four: redact personally identifiable information. Pass the merged transcript through Microsoft Presidio (an open-source PII detection library) or a short LLM prompt that masks email addresses, phone numbers, national IDs and credit-card numbers. Presidio finds 95%+ of common PII patterns and takes under 30 seconds on a one-hour transcript.
Step five: summarise. Feed the redacted transcript to Claude, GPT-4 class or a local Llama 3 70B with a prompt that asks for a bullet-point summary, an action-item list with owners and a list of open questions. The output arrives in 15–40 seconds depending on provider.
Total wall-clock time for a 60-minute meeting: approximately nine minutes on a laptop, most of which is Whisper transcription running in the background while you get coffee. Total out-of-pocket cost if you use hosted APIs rather than local models: between $0.40 and $1.20 per meeting.
Cost math: hourly rates for 100h, 500h and 5000h per month
Cost comparisons for transcription are sensitive to volume, so the honest way to think about it is as a function of monthly hours. We will price three realistic tiers: a small team transcribing 100 hours per month, a mid-sized operation at 500 hours, and a large customer at 5,000 hours.
At 100 hours per month, the hosted Whisper API costs about $36 per month. AssemblyAI Universal-2 costs about $65. Deepgram Nova-3 in batch mode is around $15. Otter.ai Business at $20 per seat covers the same volume for a team of five at $100. Rev.com at machine rates costs $120; human-reviewed would be $1,500. At this volume, hosted APIs and SaaS tools are the pragmatic choice — the operational overhead of running local Whisper is not worth the saving.
At 500 hours per month, the numbers start to tilt. Whisper API climbs to $180, AssemblyAI to $325, Deepgram to $75. A local Whisper installation on a single RTX 4080 workstation (a one-time hardware cost of roughly $2,000) transcribes 500 hours in about three days of compute, giving a per-hour marginal cost close to zero. Over a year, that hardware pays for itself six times over versus the Whisper API alone. The trade-off is that you now own an infrastructure component and need someone to maintain it.
At 5,000 hours per month, local or self-hosted becomes almost mandatory for cost reasons. The Whisper API would cost $1,800 monthly, AssemblyAI $3,250, Deepgram $750. A dedicated transcription server — two H100s, $45,000 capex — handles 5,000 hours in a few days of queued work and repays itself in three to four months against AssemblyAI-class pricing. Alternatively, volume-negotiated contracts with Deepgram or AssemblyAI typically cut list price by 40–60% at this scale, so a hybrid approach (self-host bulk, cloud for streaming or specialised features) often wins on total cost and simplicity.
The rule of thumb for 2026: under 200 hours per month, buy an API. Between 200 and 2,000, run the math including your labour cost. Above 2,000, plan for self-hosted capacity unless streaming or compliance forces a managed provider.
The 2026 benchmark: Word Error Rate (WER) across leading tools
Word Error Rate is the percentage of words that differ from a reference transcript — lower is better. It is an imperfect metric (it penalises “don’t” / “do not” as an error and ignores whether the wrong word changes meaning) but remains the industry standard for comparison. The 2026 numbers below are averaged across three public datasets: LibriSpeech clean test (studio-quality read English), TED-LIUM (conference talks) and Earnings-21 (noisy financial calls).
On LibriSpeech clean, Whisper large-v3 records 2.7% WER, Whisper v3 Turbo 3.1%, AssemblyAI Universal-2 2.9%, Deepgram Nova-3 3.4% and Google Chirp-2 3.6%. All five are effectively within measurement noise of each other; the choice between them at that quality level has nothing to do with accuracy.
On TED-LIUM, with more natural speech and occasional non-native accents, the spread widens: Whisper large-v3 at 3.9%, Turbo at 4.5%, AssemblyAI at 4.7%, Deepgram at 5.2%. Whisper’s accent training advantage becomes visible here.
On Earnings-21, the hardest of the three, all models degrade. Whisper large-v3 sits at 9.1%, Turbo at 10.4%, AssemblyAI at 8.6%, Deepgram at 10.9%. AssemblyAI’s model, fine-tuned more heavily on business audio, overtakes Whisper for this specific domain — a reminder that a single “best model” claim is always simplistic.
The practical takeaway: for any English benchmark above roughly 5% WER, the ranking depends on domain characteristics, not a universal ordering. Run a thirty-minute representative sample through two or three candidates before committing to a provider.
What to watch when choosing a tool
A short decision checklist covers most procurement conversations in 2026. Is the primary language English, German, a European language or something else? Whisper is universally strong; commercial providers rank English first and everything else second. Is the workload batch or streaming? Whisper and AssemblyAI are batch-first; Deepgram and Google are streaming-first. Does data residency matter? Local Whisper, Azure EU regions and Aleph Alpha lead. Do you need diarization? AssemblyAI, Deepgram and Otter are ahead of Whisper unless you pair Whisper with pyannote. What is your monthly volume? Hosted below 200 hours, hybrid up to 2,000, self-hosted beyond. Do you need features beyond transcript — summaries, sentiment, PII redaction, subtitle export? That is where AssemblyAI, Trint and Rev earn their price premium.
Run a real benchmark on your actual audio before signing a contract. Public WER numbers rarely match the mix of accents, jargon and recording quality in your workflow. Thirty minutes of representative audio fed through three shortlisted tools — plus a manual review of the differences — tells you more than any vendor sales deck.
The future of AI speech recognition
The next twelve to eighteen months will continue the 2023–2026 pattern: open-source models catch up to and increasingly overtake commercial baselines on accuracy, while commercial providers compete on features, latency and compliance. Three specific developments are worth watching.
First, true streaming open-source models. Whisper’s batch-first architecture makes it poorly suited for real-time use; a streaming-native open model would collapse the price of live captioning and voice agents. Several research groups have prototypes in 2026.
Second, the merger of speech recognition and speech synthesis into end-to-end voice agents. OpenAI’s Realtime API, Gemini Live and comparable offerings treat audio as a first-class input and output; the traditional STT → LLM → TTS pipeline is compressing into a single model. Expect transcription as a separate product to lose ground to integrated voice stacks for conversational workloads — while remaining dominant for archival, compliance and editorial use.
Third, emotion, prosody and paralinguistic features. Current transcripts strip away everything except the words. Research models in 2026 are beginning to annotate sarcasm, stress, hesitation and sentiment in a structured way. For call-centre analytics, mental-health research and accessibility this unlocks use cases transcription alone cannot serve.
For a broader view of how speech recognition fits into the wider audio-AI ecosystem — synthesis, dubbing, music, sound design — see our overview on AI audio tools 2026.
Which setup fits which workflow?
AI speech recognition in 2026 is one of the clearest wins machine learning has produced. A technology that barely worked a decade ago now runs on consumer laptops, costs fractions of a cent per minute in the cloud and clears 95% accuracy on almost any clean English audio. The remaining hard problems — diarization with five speakers, obscure jargon, strong accents, real-time latency, GDPR residency — have sharp, specific solutions rather than open research questions.
For most readers, the decision collapses to three concrete paths. Use local Whisper v3 Turbo when privacy or volume matters; you get accuracy that rivals the best commercial services for zero marginal cost. Use AssemblyAI when you are building software and need structured output — speakers, sentiment, summaries, redaction — in one response. Use Deepgram when streaming latency is non-negotiable. Everything else is polish around those three defaults.
Test two or three tools on a representative thirty-minute sample of your actual audio before committing. The ranking on your workload will not match any public benchmark — and a ten-minute experiment saves months of procurement friction later.
Sources and further reading
Tool data and benchmark figures rely on primary sources: OpenAI Whisper on GitHub for v3 Turbo specifications, Deepgram for Nova-3 and pricing, AssemblyAI for Universal-2 and EU data residency.
Further reading in this cluster: AI Audio Tools 2026 — Speech Synthesis, Transcription, Dubbing, GDPR-compliant AI transcription for SMBs, AI dubbing for YouTube channels 2026.
Update note (as of 02.04.2026)
This guide is reconciled every 4–6 weeks with new Whisper releases (v4 expected H2 2026), Deepgram/AssemblyAI model updates and EU data-residency expansions. Next review: mid-May 2026.
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Frequently Asked Questions
How accurate are modern AI speech-recognition systems?
With clear speech, Whisper, Deepgram and Google Speech exceed 95% word accuracy. Dialects, specialist vocabulary and noisy environments drop the rate to 85–90%.
May I automatically transcribe meetings?
Only with consent of all participants. In Germany, telecommunications secrecy and GDPR apply — covert transcription is unlawful.
Which tool is free and privacy-friendly?
OpenAI Whisper can be run locally on your own machine — no cloud upload. Open source, free and GDPR-compliant.
Which AI speech-recognition tool suits domain-specific English jargon?
Whisper v3 Turbo leads in 2026 with a Word Error Rate of ~4.5% on English audio and is reliable on medical, legal and technical terminology when paired with a custom vocabulary file. Deepgram Nova-3 is the streaming alternative with comparable quality. For both, configure industry glossaries via the custom-vocab parameter.
How much does AI speech recognition cost per minute in 2026?
Whisper local: $0 (after a one-off hardware investment). Cloud APIs: $0.003–$0.02 per minute (Deepgram, AssemblyAI). Otter.ai Pro: ~$17/month for unlimited minutes. Free tiers up to 30 min/month let you test all three providers at no cost.
