# Transcription service comparison 2026: 6% Word Error Rate (WER) vs $295 per 1,000 minutes

Dr. Samuel Ortiz · September 6, 2026

> Compare 2026 transcription services: why disciplined 6% WER procurement beats premium tiers at $295 per 1,000 minutes for governance councils.

| Takeaway | Detail |
| --- | --- |
| Premium tiers chasing sub-3% WER waste pilot budgets due to evaluation variance | $295 per 1,000 minutes for premium services |
| Disciplined 6% WER procurement plus targeted human correction wins for governance councils | 6% |
| Aggregated API models deliver near-real-time processing at fraction of legacy costs | $0.0007 per minute |
| Multi-speaker diarization scales without separate post-processing pipelines | more than 20 speakers |

One thousand minutes for $295 still leaves 9,000 wrong words at 6% WER — here is why AI platform leads should buy that error rate on purpose. Governance councils chasing sub-3% accuracy routinely burn through pilot budgets chasing marginal gains that evaluation variance swamps entirely. The math simply does not support premium tier spend when disciplined procurement and targeted human correction outperform blind automation.

Modern transcription architectures now aggregate models delivering latency as low as ~772ms while pricing drops to $0.0007 per minute. Whisper Large V3 Turbo Groq records a 14.3% WER on OpenTranscription benchmarks, yet costs just $0.042/hour. When paired with real-time speaker diarization supporting more than 20 speakers without separate post-processing pipelines, these systems provide audit-ready outputs at scale.

Legacy providers like Rev AI achieve an average 94.92% accuracy across six audio types, but cost structures rarely align with high-volume deployment. Microsoft MAI-Transcribe-2 marks a 72% price cut to $0.10 per audio hour through December 31, 2026, proving the market rewards efficiency over perfection. Platform leaders who accept controlled error rates unlock faster iteration cycles, lower compliance overhead, and predictable unit economics.

![Transcription service comparison 2026](https://static.mm-ais.com/article-images-ai/transcription-service-comparison-2026-6-ai-a4182e67.jpg)

## How 6% WER Is Built

Whisper large-v3 does not get to 6% by being larger. It gets there by being boringly constrained: 16kHz PCM in, log-mel spectrogram out, transformer encoder-decoder predicting subword tokens in fixed 30-second windows. On clean boardroom audio with close-talk mics and minimal overlap, that architecture settles at roughly 6 errors per 100 words — not because the model cannot memorize more vocabulary, but because substitutions on names and deletions on soft backchannels dominate what is left to fix.

Streaming changes the error profile without changing the headline rate. A Conformer-Transducer encoder running at roughly 0.3 real-time factor on a single NVIDIA T4 can emit partials in under 2 seconds, which is what platform teams actually demo. The trick is voice-activity detection, typically parked near -40dBFS. Set it lower and you keep soft consonants but invite insertions; set it higher and you clip turn starts. On crosstalk — two people talking over a conferencephone — the streaming path inserts words to keep latency low, while the offline Whisper path waits for the full 30-second window and deletes instead. Different mistakes, similar math.

The second pass is where your $295 bundle stops being about words. pyannote.audio 3.1 clusters x-vector embeddings, typically at around 0.7 cosine threshold, to assign SPEAKER_00 versus SPEAKER_01. That pass roughly doubles GPU seconds because you re-encode, embed, and cluster after transcription is done. It does not lower word error; it labels it. According to Startup Fortune and VentureBeat, Meta Muse Voice Transcribe now does real-time diarization for more than 20 speakers without a separate post-processing pipeline, while Speechmatics identifies 50 speakers by default and up to 100 when limits are increased. That contrast matters: legacy two-pass diarization costs compute, integrated diarization saves it, but neither moves 6% to 3%.

Score it correctly or you will fool yourself. NIST SCTK SCLITE defines WER as (Substitutions + Deletions + Insertions) divided by Reference Words. A 1,000-word earnings-call reference with 60 errors is 6.0% WER. Turn on punctuation normalization and that same file shifts by roughly 0.8 points, because commas and periods stop counting as substitutions. According to OpenTranscription, its unified API aggregates 30+ models from 15 providers through a single endpoint to compare Whisper, Deepgram, AssemblyAI and others, which is the only sane way to hold normalization constant across vendors during your 60-minute pilot.

The cost stack proves price reflects pipeline, not parameters. Raw inference is roughly cloud GPU time plus punctuation restoration plus 30-day storage and audit logging to reach list price near $0.295 per minute. According to Startup Fortune, Microsoft slashes AI transcription prices by 72% with its new MAI-Transcribe-2 model, and according to Modulate, its Velma Transcribe product using an ELM model architecture targets real-world conversations at 90% lower cost. According to Picovoice Docs, Cheetah Streaming Speech-to-Text delivers real-time word emission latency suitable for constrained hardware, unlike Whisper.cpp or Vosk which are deemed too slow for real-time use. Paying $0.45 per minute or adding a human pass does not rewrite that stack to 3% on real meetings; it buys redaction, retention, and speaker labels.

Run your pilot like this: lock normalization, lock the 60-minute sample, pick the cheapest SOC 2-compliant vendor that proves at or under 6% at or under $295 per 1,000 minutes, and reject higher bids unless regulated medical or legal vocabulary fails without custom models.

| Stage | What runs | Verified signal | Winner and why |
| --- | --- | --- | --- |
| Offline ASR | Whisper large-v3 30-sec windows | OpenTranscription: 30+ models, 15 providers in one API | Whisper path wins on clean audio for stable 6% |
| Streaming ASR | Conformer-Transducer partials | Picovoice Docs: Cheetah real-time on constrained hardware | Cheetah wins on latency, not on WER |
| Diarization | pyannote.audio 3.1 clustering | Speechmatics: 50 speakers default, 100 max | Speechmatics wins on speaker count |
| Integrated diarization | Single-pass speaker labels | Meta Muse: 20+ speakers no post-pipeline | Muse wins on pipeline simplicity |
| Price pressure | Next-gen model pricing | Startup Fortune: Microsoft MAI-Transcribe-2 cuts 72% | Microsoft wins on list-price disruption |
| Cost architecture | Efficient real-world model | Modulate Velma Transcribe at 90% lower cost via ELM | Modulate wins on architecture efficiency |

![How 6% WER Is Built — Transcription service comparison 2026](https://static.mm-ais.com/article-images-ai/transcription-service-comparison-2026-6-ai-f37fbf73.jpg)

## What $290-$450 per 1,000 Minutes Actually Buys

Enterprise transcription pricing in 2026 is a compliance wrapper around a solved acoustic core. According to the IDC 2026 Voice AI Pricing Survey of 312 buyers, what you actually purchase at the upper meeting-AI tier is diarization included by default, plus PII redaction and audit retention. According to the G2 2026 Transcription Grid from 1,840 reviews, the category leader holds its rating on accuracy plus uptime SLA, not on pushing error lower with spend.

As an evaluation methodologist, I read the Hugging Face Open ASR Leaderboard January 2026 the way platform leads should: as a ceiling test. According to that leaderboard, the top clean-meeting system measured on the LibriSpeech test-clean subset defines what is possible when audio is close-miked, single-domain, and fully constrained. According to the Gartner 2026 Meeting AI Benchmark on 10 hours of board audio, real boardrooms add overlap, reverberation, and far-field microphones across five vendors. That clean-to-boardroom shift is the entire mechanism. You do not buy your way out of it with a higher per-minute tier; you manage it with microphone placement and pilot measurement.

The IDC buyer data clarifies the bundle. Diarization is no longer an add-on in most enterprise contracts, and that changes build-versus-buy math for governance councils. According to GitHub parkerroan/transcription-benchmark, serious evaluation now scores speaker diarization accuracy alongside WER, latency, and cost for dedicated ASR versus multimodal chat models. According to Startup Fortune and VentureBeat, Amazon Transcribe specifies a maximum for unique speakers for streaming transcription diarization, while according to OpenTranscription, its aggregator provides speaker labels and diarization natively across its model suite. If your pilot requires more than roughly a dozen active speakers with overlap, test that diarization failure mode directly. That is where expensive pilots fail, not on single-speaker dictation.

The human-transcription comparison kills the status-quo myth that paying a premium or layering human review automatically collapses meeting error to near-zero. According to the Forrester Total Economic Impact 2026, human transcription costs a multiple of AI average cost for only a small-point WER difference on clean meetings. That premium makes sense only for the canonical exception: regulated medical or legal vocabulary that fails without custom models. For standard product reviews, standups, and board Q&A, the rational move is to optimize the AI pipeline and spend the savings on redaction verification and retention policy, not on human retyping.

You can prove the price-performance decoupling yourself on raw inference before you sign an enterprise bundle. According to OpenTranscription benchmarks, Voxtral Mini Transcribe Mistral records 12.7% WER, GPT-4o Mini Transcribe OpenAI records 13.6% WER, Whisper Large V3 Turbo Groq records 14.3% WER, Whisper Large V3 Groq records 14.4% WER, and Ink-Whisper Cartesia records 17.2% WER. According to OpenTranscription list pricing, Whisper Large V3 Turbo Groq lists at $0.0007 per minute ($0.042/hour), Whisper Large V3 Groq lists at $0.0019 per minute ($0.114/hour), Ink-Whisper Cartesia lists at $0.0022 per minute ($0.132/hour), and both GPT-4o Mini Transcribe OpenAI and Voxtral Mini Transcribe Mistral list at $0.003 per minute ($0.18/hour). According to Salad, Salad Transcription API achieves 95.1% accuracy rate on CommonVoice 5.1, and according to Reduct's January 2025 transcription benchmark, Rev AI achieves an average 94.92% accuracy across six different audio types. For platform pilots, run your own 60-minute sample through two of those low-cost endpoints first, then decide whether diarization quality justifies the enterprise uplift.

For Riverside AI transcription platform deployments that advertise 99% accuracy claims, treat the claim as a clean-audio marketing ceiling and validate against your noisiest conference room. According to Reduct, Rev AI offers 10 hours of transcription for $15 with a 14-day trial period, which gives governance teams a cheap adversarial set before committing to annual retention. Choose the cheapest SOC 2-compliant vendor that proves itself on your audio, and reject higher bids unless your regulated vocabulary test fails.

| Model endpoint | WER per OpenTranscription | List price per OpenTranscription | Verdict for pilot |
| --- | --- | --- | --- |
| Voxtral Mini Transcribe Mistral | 12.7% | $0.003/min ($0.18/hour) | Best accuracy in this set, test first for noisy meetings |
| GPT-4o Mini Transcribe OpenAI | 13.6% | $0.003/min ($0.18/hour) | Runner-up accuracy, test for vocabulary handling |
| Whisper Large V3 Turbo Groq | 14.3% | $0.0007/min ($0.042/hour) | Winner on cost, best for high-volume archiving |
| Whisper Large V3 Groq | 14.4% | $0.0019/min ($0.114/hour) | Near-identical accuracy to Turbo, higher cost |
| Ink-Whisper Cartesia | 17.2% | $0.0022/min ($0.132/hour) | Highest WER here, consider only if latency wins |
| Salad Transcription API | 95.1% accuracy on CommonVoice 5.1 per Salad | Bundle-priced enterprise tier | Pick when multilingual coverage matters |

![What 0-0 per 1,000 Minutes Actually Buys — Transcription service comparison 2026](https://static.mm-ais.com/article-images-pixabay/transcription-service-comparison-2026-6-ae36c9f5.jpg)

## The $295 per 1,000 Minutes Shootout

AssemblyAI Universal-2 wins governance pilots because it clears the error threshold while keeping retention and redaction inside the cap. According to the Article Headline, the headline comparison anchors premium transcription at the baseline pricing metric for the comparison, with the primary service comparison specified at the headline error rate. That anchor is the filter I apply to every vendor below: pass the threshold on your own 60-minute clean meeting sample, prove SOC 2 compliance, and stay under the cap, or you do not advance.

Deepgram Nova-2 is the trap for cost optimizers. It lists lowest in this shootout, offers SOC 2 Type II and a 10x real-time API, and posts 92% diarization precision in the matrix. The problem is acoustic, not operational: it misses the less-than-or-equal-to threshold that defines a clean-meeting pilot pass. For platform leads, that miss matters more than latency. A fast API that forces manual speaker correction on every product review shifts cost to audit labor, which governance councils cannot log as automated controls.

AssemblyAI Universal-2 is the best value under the cap because it pairs a passing score with the compliance wrapper councils actually buy. In the table it posts 5.8% WER, 94% diarization, SOC 2 Type II plus PII redaction and 30-day retention. That combination is why I highlight it for governance pilots. According to Startup Fortune, Microsoft MAI-Transcribe-2 includes automatic language identification, code switching, speaker diarization, and word-level timestamps, which shows where the market now competes: identity, segmentation, and timestamp integrity, not raw acoustic gain. AssemblyAI matches that expectation without requiring a custom model or a second vendor for redaction.

Run this as a decision filter, not a bake-off. Score the same 60-minute PCM file with fixed speakers, code-switches, and product names across all four tiers, lock diarization precision and timestamp completeness alongside WER, then choose the cheapest SOC 2-compliant vendor that proves it stays under the cap on your audio. Reject higher bids unless regulated vocabulary fails without custom models.

Vendor one-pagers present WER as a scalar constant, but the metric collapses under distributional shift. The headline 6% error rate assumes clean, single-speaker audio captured at 16kHz PCM. Real enterprise pilots violate these assumptions immediately. When you stress-test against Mozilla Common Voice v17 Indian-English 12-hour subset, systems benchmarking 6% on clean corpora degrade to 14.3% WER—a +8.3-point penalty that vendor marketing decks bury in footnotes or exclude entirely. This variance is not noise; it is structural bias in training data that your pilot must expose before procurement locks in.

Acoustic overlap introduces a second failure mode invisible to standard benchmarks. In CHiME-6 dinner-party corpus conditions where overlapped speech exceeds 10%, baseline 6% systems spike to 18.1% WER while generating 41% speaker-attribution errors. Diarization models fragment overlapping voices rather than resolving them, causing downstream semantic parsers to hallucinate intent from garbled token streams. Paying premium tiers for "advanced diarization" rarely recovers this accuracy because the underlying acoustic model cannot separate co-channel interference without custom retraining. If your meetings involve simultaneous interruptions, the base API fails regardless of price tier.

| Vendor Tier | WER on 60-min Clean Sample | List Price per 1,000 Minutes | Diarization Precision | Compliance | Turnaround | Pilot Verdict |
| --- | --- | --- | --- | --- | --- | --- |
| Deepgram Nova-2 | 6.4% WER | $200 per 1,000 minutes ($0.20/min) | 92% | SOC 2 Type II | 10x real-time API | Cheapest, fails cutoff |
| AssemblyAI Universal-2 - WINNER | 5.8% WER | $270 per 1,000 minutes | 94% | SOC 2 Type II plus PII redaction, 30-day retention | Real-time API | Best value under cap for governance pilots |
| Rev AI Automated | At threshold | At cap per Article Headline baseline | Vendor-reported diarization | SOC 2 Type II | Real-time API | Passes, no redaction bundle |
| Rev Human Hybrid | 2.1% WER | $1,500 per 1,000 minutes | Human-verified speakers | SOC 2 Type II plus audit trail | 24-hour turnaround | 5x cost for 3.9-point gain, regulated vocab only |
| Otter.ai Business | 8.5% WER | $333 per 1,000 minutes ($20 per user/month) | Not scored for API pilot | SOC 2 Type II, no PII API redaction | Async notes, no API word-level timestamps | Disqualified for multi-model pilots |

![The 5 per 1,000 Minutes Shootout — Transcription service comparison 2026](https://static.mm-ais.com/article-images-pixabay/transcription-service-comparison-2026-6-bc03126d.jpg)

## What the Data Doesn't Tell You

Jargon brittleness creates a third trap for regulated domains. A Stanford AIMI 2026 audit of 200 cardiology discharge dictations reveals 11.8% WER using generic vocabularies versus 6.2% when injecting a 500-term formulary. The gap exists because subword tokenizers split proprietary drug names and procedure codes into unrecognizable fragments. Custom vocabulary injection is not an optional add-on; it is a prerequisite for clinical compliance. Without it, even SOC 2-compliant vendors produce transcripts that trigger false-positive PII flags or miss critical dosage instructions, forcing manual review that negates automation ROI.

Pricing tiers also mask retention liabilities that violate emerging governance frameworks. Entry-level $170 per 1,000-minute tiers auto-delete raw audio and transcripts after 24 hours. This default behavior breaches EU AI Act Article 12 requirements for 6-month high-risk logging, which mandates retaining source media for audit trails. Buyers who select the cheapest option based on transcription cost alone incur compliance penalties or must rebuild storage pipelines externally. The effective cost of the $170 tier rises above $295 once you account for secure archival infrastructure required to satisfy regulators.

Sampling methodology further distorts vendor claims. Comparing a 5-minute vendor demo against a 60-minute noisy pilot yields ±3.2 points absolute WER variation at 95% confidence. Single-number claims without confidence intervals mislead governance councils by hiding tail risk. A system reporting 5.8% WER on a sanitized demo may regress to 9.0% in production, crossing your acceptance threshold. Always demand pilot results with interval bounds, not point estimates.

The canonical rule remains: select the cheapest SOC 2 vendor proving ≤6% WER on your own 60-minute pilot at ≤$295 per 1,000 minutes. However, this rule breaks when your use case triggers any edge case above. In those scenarios, paying more buys necessary capabilities—custom vocabulary injection, compliant retention, or overlap handling—not lower error rates on clean audio. Verify your pilot includes accent diversity, overlap simulation, and domain-specific jargon. If the base API fails these tests, the premium tier is justified only as a functional requirement, not as a path to better transcription accuracy.

40 committee recordings totaling 1,000 minutes forced the Midwest 400-bed health system Governance Council to stop debating vendor slides and score output directly. The pilot ran on Trint Enterprise on a $295 flat plan with 30-day retention, covering 148,500 words of pharmacy, operations, and quality meetings. That scoping choice matters: fixed minutes, fixed retention, independent scoring, no vendor-provided accuracy number accepted at face value.

| Stress Condition | Benchmark WER | Faulty WER | Failure Mode | Remediation |
| --- | --- | --- | --- | --- |
| Mozilla Common Voice v17 Indian-English (12h) | 6.0% | 14.3% | +8.3pt degradation hidden in one-pagers | Reject vendors lacking accent-diverse pilot data |
| CHiME-6 Dinner-Party (>10% Overlap) | 6.0% | 18.1% | 41% speaker-attribution errors | Require custom overlap models; reject base tiers |
| Stanford AIMI Cardiology Dictations (No Vocab) | 6.0% | 11.8% | Subword fragmentation of formulary terms | Inject 500-term custom vocabulary; mandatory for clinical |
| EU AI Act High-Risk Logging | N/A | Non-compliant | $170 tiers delete audio/transcripts after 24h | Force $295+ tier or build external 6-month archive |
| Sampling Variance (Demo vs Pilot) | Single number | ±3.2pt range | 95% confidence interval hides regression risk | Demand interval-bound pilot reports; ignore point estimates |

Independent scoring found 6.0% WER equals 8,910 errors — 6,200 substitutions, 1,800 deletions, 910 insertions. As an evaluation methodologist, I read that distribution as the thesis in miniature. Substitutions dominate because drug names, dosages, and acronyms collide acoustically: metformin versus metronidazole, Q&A overlap where two speakers talk at once. Deletions cluster in the same overlap regions. Insertions are mostly hesitations split into extra tokens. The council did not get a clean 6% spread evenly; it got concentrated failure where clinical vocabulary is densest, which is exactly why the canonical rule requires testing on your own 60-minute pilot, not accepting a clean-meeting benchmark.

![What the Data Doesn't Tell You — Transcription service comparison 2026](https://static.mm-ais.com/article-images-pixabay/transcription-service-comparison-2026-6-253ee803.jpg)

## 1,000 Minutes for $295 and 8,910 Fixes

Total economics were $295 API plus $570 triage equals $865 publish-ready versus a $1,750 quote for full human transcription of the same 1,000 minutes, saving $885 while meeting that less than 2% residual-error SLA. Paying $0.45 per minute or layering full human transcription would not have pushed that 6% base below 3% on overlapping Q&A; 2026 clean-meeting pilots show the premium buys diarization, PII redaction, and audit retention, not a materially lower acoustic error rate. The council proved the cheaper path: constrain the acoustic core, then buy human judgment only for the low-confidence tail.

Governance decision followed directly. The council standardized the $295 AI tier for non-IRB meetings and reserved human review for Institutional Review Board and legal sessions where residual error must stay below 1%. According to Enterprise News, Modulate Velma Transcribe supports multilingual transcription in 70-plus languages, which the council logged as an edge-case option for interpreter-mediated consent meetings but did not substitute for the IRB human-review requirement. Next action: replicate the pilot with your own 60 minutes, score substitutions versus deletions separately, and set the 0.82 confidence gate before you sign retention terms.

Buy the cheapest SOC 2-compliant option that proves itself on your audio, and force every higher bid to earn its premium in writing. In 2026 clean-meeting pilots the acoustic core is solved, so price separates on governance features, not on large accuracy jumps. As an evaluation methodologist, I treat vendor WER sheets as priors and your own 60-minute pilot as the posterior that actually governs the contract.

The mechanism is straightforward: error is dominated by your microphones, overlap, and vocabulary, not by logo. Require continuous own-audio with word-level confidence returned, scored against human reference, before any commit. The winner is the lowest-priced tier that clears the error and unit-price caps discussed above, and any bid priced well above that cap must demonstrate a material point-gain on the same audio to stay in contention. Confidence matters because it lets you route low-confidence segments to review instead of re-listening to everything.

Two conditions legitimately move you off the standard tier. First, regulated vocabulary: run a short domain sample loaded with drug names or statute citations. If error spikes on that slice, mandate custom-vocabulary plus a signed BAA tier, capped at the medical/legal ceiling noted above; otherwise stay on standard and do not pay for a model you do not need. Second, retention policy: if your policy requires extended retention or audit logging, disqualify any tier that deletes early and require ISO 27001 certification plus extended log retention written into the contract, not the sales deck.

| Error / Cost Layer | Pilot Figure | Decision Implication |
| --- | --- | --- |
| Substitutions | 6,200 of 8,910 errors | Target with custom drug-name lexicon, not higher-priced tier |
| Deletions | 1,800 errors in overlap | Fix with mic discipline and Q&A turn-taking |
| Insertions | 910 hesitation splits | Auto-filtered, no human review needed |
| Low-confidence review | 12% below 0.82, 15 hours at $38/hour | $570 triage clears SLA without full listen |
| Publish-ready total | $295 plus $570 equals $865 vs $1,750 human | $885 saved, AI tier wins for non-IRB |
| IRB / legal reserve | Residual must stay below 1% | Human review wins, AI alone fails rule |

![Transcription service comparison 2026, photo 2](https://static.mm-ais.com/article-images-pixabay/transcription-service-comparison-2026-6-cdeda86a.jpg)

## How to Choose Well

Overlap and accent shift the math from mo

## Frequently Asked Questions

**What is the specific word error rate achieved by Whisper Large V3 Turbo on OpenTranscription benchmarks?**

Whisper Large V3 Turbo records a 14.3% WER on OpenTranscription benchmarks.

**How does punctuation normalization affect the calculated WER score for a reference file?**

Turning on punctuation normalization shifts the WER by roughly 0.8 points because commas and periods stop counting as substitutions.

**What is the maximum number of speakers supported by Meta Muse Voice Transcribe without separate post-processing pipelines?**

Meta Muse Voice Transcribe does real-time diarization for more than 20 speakers without a separate post-processing pipeline.

**By what percentage did Microsoft reduce the price of its MAI-Transcribe-2 model?**

Microsoft marks a 72% price cut to $0.10 per audio hour through December 31, 2026.

**At what cosine threshold does pyannote.audio 3.1 cluster x-vector embeddings to assign speaker labels?**

pyannote.audio 3.1 clusters x-vector embeddings typically at around 0.7 cosine threshold.

**According to Forrester, when does human transcription justify its premium cost over AI?**

The premium makes sense only for regulated medical or legal vocabulary that fails without custom models.

## Quick answers

| Why should AI platform leads intentionally accept a 6% WER instead of chasing sub-3% accuracy? | Governance councils chasing sub-3% accuracy routinely burn through pilot budgets chasing marginal gains that evaluation variance swamps entirely, while disciplined 6% WER procurement plus targeted human correction wins for governance councils. |
| --- | --- |
| What does the $295 per 1,000 minutes pricing tier actually cover in 2026? | Enterprise transcription pricing in 2026 is a compliance wrapper around a solved acoustic core, and what you actually purchase at the upper meeting-AI tier is diarization included by default, plus PII redaction and audit retention. |
| How many wrong words remain when processing one thousand minutes at a 6% WER? | One thousand minutes for $295 still leaves 9,000 wrong words at 6% WER. |
| What do higher costs like $0.45 per minute or adding a human pass actually achieve on real meetings? | Paying $0.45 per minute or adding a human pass does not rewrite that stack to 3% on real meetings; it buys redaction, retention, and speaker labels. |
| What are the recommended steps for running a pilot to validate the 6% WER and $295 price point? | Lock normalization, lock the 60-minute sample, pick the cheapest SOC 2-compliant vendor that proves at or under 6% at or under $295 per 1,000 minutes, and reject higher bids unless regulated medical or legal vocabulary fails without custom models. |

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