TL;DR: Gladia’s Solaria-1 covers 100+ languages natively, with automatic code-switching detection with automatic code-switching detection that does not require developers to pre-specify languages for each session. On paid plans, Gladia positions languages and core audio intelligence features including diarization, translation, sentiment analysis, NER, and summarization within the pricing model rather than as separate per-feature add-ons.
Your internal tests may look fine on English audio, but production failures usually surface in multilingual traffic first. Support tickets from LATAM and European users tend to expose the gap between clean test conditions and the mixed-language, accented speech your product actually needs to handle. For product teams building global voice applications, that gap between lab performance and production reality is where users churn, manual correction costs accumulate, and a vendor decision made at Series A starts compounding against unit economics at Series B. This article documents exactly where Rev.ai's language gaps create product risk and why teams switch to Gladia for predictable multilingual performance.
Rev.ai's language gaps: a product risk
Support for 58+ languages in asynchronous transcription does not automatically mean consistent production performance across noisy, real-world audio conditions. For teams building global meeting assistants or contact center analytics platforms, the difference tends to show up in WER, user complaints, and the engineering time spent resolving transcription regressions across multilingual traffic. When you're shipping a global meeting assistant or a contact center analytics platform, the distinction shows up directly in WER figures, user complaints, and engineering sprint capacity spent on transcription regressions.
English-first architecture limits multilingual accuracy
Rev.ai's Reverb ASR model represents a new architecture with a reported 10-15% relative improvement in accuracy. That improvement targets English transcription on clean audio, and English is the only language the Reverb model supports. These accuracy gains don't extend to non-English languages. If your product serves LATAM Spanish speakers, Tagalog-speaking BPO agents, or French-English bilingual teams, the headline accuracy gain doesn't apply to your audio.
Which languages Rev.ai misses globally
While Rev.ai supports major South Asian languages like Hindi, Bengali, Tamil, Telugu, Marathi, Kannada, and Urdu, coverage gaps remain for regional variants and emerging markets including Punjabi, Javanese, and languages across the Caucasus and Central Asia.
Solaria-1 covers 100+ languages including 42 not supported by any other STT API, including:
- South and Southeast Asia: Bengali, Punjabi, Tamil, Urdu, Marathi, Javanese, Tagalog
- Middle East and Central Asia: Persian
- Emerging voice frontiers: Haitian Creole, Maori
For CCaaS platforms serving BPO hubs across the Philippines, South Asia, or Latin America, those language gaps aren't edge cases. They're the core of the workflow.
Accent support: Rev.ai vs. Gladia
Models trained on limited regional datasets can struggle with dialects even within languages they technically support. A Spanish transcription model may accumulate WER on various regional accents, and that degradation is invisible until users from those regions file support tickets. Rev.ai's Reverb model was trained on English-heavy datasets, which may explain why its accuracy gains don't transfer as well to accented non-English speech. Gladia’s published benchmark compares Solaria against 8 providers across 7 datasets and 74+ hours of audio, using an open and reproducible methodology designed to surface performance differences across accented speech, broad language coverage, and real-world audio conditions.
Multilingual accuracy regressions in Rev.ai
When an ASR model can't handle a language pair natively, the failure mode isn't always obvious. Transcripts look plausible until a human reviewer catches the errors, or until a user in São Paulo or Helsinki stops using the product.
Rev.ai's Spanish accent WER
Rev.ai's "Multilingual English/Spanish" model is the company's primary answer to Spanish support. According to Rev.ai's own documentation, this model carries explicit limitations:
- Available for async transcription only
- Handles a single two-language combination (English/Spanish)
- Any spoken language outside that pair will not be captured
- Regional LATAM accents push WER higher on models not trained on that diversity
For a product serving LATAM users at scale, that's a meaningful accuracy regression surfacing through customer complaints rather than internal QA.
Rev.ai's single-language architecture
Code-switching is standard behavior in multilingual meetings, customer service calls, and cross-border business conversations. When a speaker shifts languages mid-sentence, the model needs to recognize that shift instantly. A model configured for Spanish applies Spanish sound rules to English words, producing incorrect but plausible-sounding output. The model doesn't know to switch because it wasn't designed to.
Rev.ai's code-switching support is limited to a single English/Spanish model available only for async transcription. Other language pairs with code-switching behavior produce either dropped segments or incorrect transcription of the non-primary language.
Visual Demonstration 1: When you submit a code-switched English/French audio file to Rev.ai's API with English as the primary language, the French segments either disappear from the transcript entirely or appear as incorrectly transcribed English words. The API response contains no language field because the system assumes single-language input throughout. Gladia's response for the same audio shows language detection at the word level, with each transcript segment tagged "en" or "fr" and word-level timestamps preserved across the language transition. This structural difference means Rev.ai requires you to know the exact language distribution before transcription, while Gladia detects and labels language switches automatically.
Rev.ai's Indic language accuracy gaps
Rev.ai's HIPAA-supported language list includes Hindi, Kannada, Marathi, Tamil, Telugu, and Urdu. Having a language in a compliance list doesn't tell you the WER on noisy telephony audio from a Mumbai call center, which is the condition that matters for a CCaaS platform.
Gladia's BPO use case documentation addresses this directly. Solaria-1 was built to handle Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, and Marathi at production accuracy levels, because BPO hubs in Southeast Asia and South Asia represent real, high-volume workloads where these languages are spoken continuously, often with code-switching into English.
How Gladia handles 100+ languages natively
Solaria-1 was built for the reality of global voice products: accented speakers, noisy telephony audio, mid-conversation language switches, and developers who need consistent accuracy across every language their users actually speak.
Building for 100+ languages natively
Gladia’s benchmark framework evaluates Solaria against 8 providers across 7 datasets and 74+ hours of audio, with an open, reproducible methodology designed to surface performance differences across multilingual, accented, and real-world audio conditions.
Automatic code-switching detection
Gladia detects language changes automatically without requiring a language parameter for each session. When a speaker shifts from French to English mid-sentence, the transcript captures both language segments. Our code-switching documentation covers both real-time and async modes.
Visual Demonstration 2: Gladia dashboard showing a transcript with seamless French-to-English transitions, speaker diarization labels maintained throughout, and language tags updating automatically per segment.
For a deeper look at how diarization integrates with multilingual transcription, our pyannoteAI webinar walks through how speaker attribution fits into Gladia’s async pipeline.
Real-world accent and dialect accuracy
Claap reached 1-3% WER in production using Gladia, with one hour of video transcribed in under 60 seconds. That's production data, not a benchmark on clean studio audio. Aircall reduced transcription time by 95% after switching from a self-hosted solution, freeing engineering capacity for product features rather than infrastructure maintenance.
Migration outcomes by language
For teams evaluating a move from Rev.ai to Gladia, three outcome categories matter most: fewer multilingual accuracy regressions, more predictable infrastructure costs, and faster integration timelines. Those expected gains come from differences in language coverage, code-switching support, and pricing structure rather than from a single universal migration outcome.
LATAM Spanish accuracy: Switching to Solaria-1 replaces the English/Spanish two-language model with native support for Spanish across regional accents, including LATAM variants.
European code-switching: European business meetings typically involve speakers alternating between their native language and English. Rev.ai requires selecting one language. Gladia’s code-switching support handles this natively.
Indic language production accuracy: BPO and contact center platforms serving South Asian markets run high-volume workloads in Hindi, Tamil, Urdu, Bengali, and Punjabi, often with English code-switching throughout calls. For teams running these workloads, the difference between a model that lists a language and a model trained on real telephony audio in that language is a measurable WER gap that shows up in every 1,000 calls processed.
How poor multilingual support inflates costs
Accuracy isn't just a quality metric. It determines how much of your infrastructure cost gets absorbed by manual review, correction workflows, and support ticket resolution.
Manual correction costs per language
When a transcript includes errors from poor accent handling, the downstream cost falls in one of three places: the end user corrects it manually, your team builds a post-processing pipeline, or the error propagates into a CRM record, sentiment score, or summary that a human eventually fixes. None of these costs appear on the API bill, but all of them compound at scale.
Rev.ai's impact on time-to-market
Rev.ai's add-on model separates translation, sentiment analysis, and diarization as separate line items. Gladia's pricing page includes every feature at the base hourly rate. Here's what that looks like at production volumes with translation enabled:
| Volume |
Rev.ai foreign-language async base rate |
Gladia Starter async |
Gladia Growth async |
| 100 hours |
$30 |
$61 |
as low as $20 |
| 1,000 hours |
$300 |
$610 |
as low as $200 |
| 10,000 hours |
$3,000 |
$6,100 |
as low as $2,000 |
Rev.ai’s public foreign-language async base rate is $0.30 per hour. Translation is separately priced at $0.12/hour for the standard model or $1.50/hour for the premium model, while sentiment analysis is priced per 10 words.
Gladia’s public pricing currently starts at $0.61/hour async on Starter and as low as $0.20/hour async on Growth, with paid plans positioned around included languages and audio intelligence rather than per-feature add-ons. Public diarization pricing is not broken out as a separate standalone rate on Rev.ai’s pricing page, so any direct diarization cost comparison should be framed carefully.
For compliance and data governance requirements, visit our compliance hub for information on data handling, data residency, and deployment options. On paid plans, customer data is not used for model training by default.
Spot multilingual language gaps before launch
Testing on clean studio audio is the most common reason product teams get surprised by production accuracy. Here's how to run an evaluation that reflects what your users actually send.
Use your own production audio for testing
Pull a representative sample of your actual user audio: noisy environments, telephony compression, natural speaking pace, and the specific accents your product serves. Benchmark on that audio, not on a dataset the vendor provides. The Gladia playground walkthrough accepts your own audio files so you can test language detection and code-switching on recordings your team already has.
How to test code-switching quality
Record or generate a short audio clip where a speaker switches languages mid-sentence at least twice, across the language pair your users speak most often. Send that clip through the API without specifying a language parameter and observe: does the model detect both languages, does it drop one, or does it produce garbled output on the switch? Gladia’s automatic language detection handles this without a parameter. Rev.ai’s transcription workflow still expects language to be specified at transcription time, and its separate Language Identification API is meant to detect the language before submission.
Evaluating multilingual AI: your key questions
Test your own multilingual audio to get a production-grade answer. Start with 10 free hours per month and run your actual user audio through the API to evaluate automatic language detection, accent-heavy speech, and code-switching in the conditions your product actually faces. For the full benchmark methodology comparing Solaria against 8 providers across 7 datasets and 74+ hours of audio, see our published benchmark results.
FAQs
Which languages does Rev.ai underperform on?
Rev.ai's Reverb ASR model supports English only, with its documented 10-15% accuracy improvement applying specifically to English transcription. Its only multilingual code-switching option covers English and Spanish in async mode only, with documented limitations on any other language pair.
Does Gladia charge extra for non-English languages?
Non-English languages are included within Gladia’s paid plans rather than priced separately by language. For the latest rates, see the pricing page.
How long does it take to switch to Gladia from Rev.ai?
Integration is typically quick and straightforward. Claap reached 1-3% WER in production using Gladia’s standard REST API with no custom model work. For implementation details, see the getting started guide.
How does Gladia handle code-switching compared to Rev.ai?
Gladia detects language changes automatically at the model level across 100+ languages, without requiring a language parameter per session. Rev.ai’s code-switching support is limited to a narrower async English/Spanish workflow rather than automatic code-switching across broad multilingual coverage.
What datasets does Gladia use to measure multilingual accuracy?
Gladia’s published benchmark compares Solaria against 8 providers across 7 datasets and 74+ hours of audio, with an open and reproducible methodology. The evaluation includes multilingual and accented audio conditions relevant to production use cases.
Is customer audio used to train Gladia's models?
Gladia’s plan terms differ by tier. Growth includes automatic model-training opt-out, and Enterprise includes default model-training opt-out, with stronger protections such as zero data retention and stricter residency options. For specific details about data usage policies and protection options, please refer to our compliance documentation.
Key terms glossary
Word Error Rate (WER): The percentage of words in a transcript that differ from the ground truth, calculated as insertions plus deletions plus substitutions divided by total reference words. Lower is better.
Code-switching: When a speaker shifts languages mid-sentence or mid-conversation, common in multilingual meetings and contact center calls. Requires a model trained on multilingual audio to transcribe correctly.
Diarization: The process of attributing transcript segments to individual speakers. Diarization runs as part of Gladia’s async transcription pipeline and is powered by pyannoteAI’s Precision-2 model.
Async transcription: Batch processing of pre-recorded audio files, as opposed to real-time streaming. Gladia’s async pipeline processes approximately one hour of audio in under 60 seconds in documented production examples.
Reverb ASR: Rev.ai's English-only ASR architecture with a documented 10-15% relative accuracy improvement over its previous model. Applies to English transcription only.
TCO (total cost of ownership): The full cost of running a speech-to-text workflow including base API rates, per-feature add-ons, infrastructure overhead, and manual correction or QA costs.
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