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Migrating from Rev.ai to Gladia: what global teams should know
TL;DR: At 10,000 hours of audio per month, Rev.ai's per-hour billing compounds quickly once you add diarization, translation, and sentiment as separate line items. Language coverage gaps surface silently in production when non-English or accented audio degrades without returning an obvious error. This guide gives you the exact API payload mappings, WebSocket transition logic, and a TCO model at realistic scale to make a defensible evaluation of switching. If you decide to migrate, our all-inclusive per-hour pricing bundles every audio intelligence feature at the base rate, and most teams complete the endpoint transition and initial production validation in under 24 hours.
Switching your speech-to-text provider: A migration checklist for note-takers and contact center platforms
TL;DR: Switching your speech-to-text provider is a structural risk only if you skip the pre-migration audit. The real danger is not the cutover itself but continuing to run infrastructure that corrupts CRM entries, breaks LLM summaries, and inflates your per-hour cost with add-on fees you never modeled at scale. The four-stage phased cutover in this guide is designed to reach 100% production traffic without user-visible downtime, the same structural approach that let Aircall cut processing time by 95% and scale to over one million calls per week after adopting Gladia.
How decision intelligence improves customer service consistency in contact centers
TL;DR: Contact centers fail to deliver consistent service when routing infrastructure runs on static rules engines that cannot handle the complexity of real human conversation. Modern speech-to-text infrastructure addresses this by processing raw audio and feeding structured outputs to your CRM, using machine learning to analyze intent, sentiment, and speaker characteristics. Transcription accuracy sets the ceiling for every downstream action: a wrong word silently corrupts a CRM entry, a missed intent misfires a routing decision, and a misread sentiment score delays escalation. This playbook covers how to build and deploy that architecture without blowing your latency budget or your unit economics.
Every speech-to-text vendor claims the lowest word error rate, the lowest latency, and the most transparent pricing. Run the same audio file through five providers and you'll get five different transcripts, five different bills, and at least two marketing pages that can't both be telling the whole story.
Choosing the best speech-to-text API in 2026 depends less on a single accuracy number and more on architecture: multilingual capability, latency characteristics, how pricing is structured, and how each vendor handles your audio data. The leading APIs all offer async and real-time transcription and audio intelligence features but their positioning, deployment, and language strategies differ enough that "best" only makes sense once you know your use case.
This guide compares five leading providers: Gladia, ElevenLabs, AssemblyAI, Deepgram, and Speechmatics, using measurable, current criteria: Word Error Rate (WER), latency, language coverage, feature bundling, pricing, and compliance.
TL;DR
Gladia leads on multilingual code-switching across 100+ languages and diarization accuracy, with audio intelligence features bundled into base pricing rather than sold as add-ons.
AssemblyAI is a solid pick for teams that want LLM-powered transcript analysis via LeMUR rather than just a raw transcript.
Deepgram and ElevenLabs are the two vendors offering integrated text-to-speech alongside STT, with Deepgram also shipping a dedicated voice-agent model, Flux, for turn-taking.
Speechmatics is the only provider here with fully air-gapped, on-premise deployment.
How we evaluated the best speech-to-text APIs
Choosing a speech-to-text API in 2026 isn't about demos. It's about production behavior. Across vendors, five dimensions consistently determine whether an integration survives real-world usage.
1. Accuracy and model design
Accuracy is still the foundation, typically measured through WER, but raw WER alone doesn't tell the whole story. In production, performance on noisy audio matters just as much as clean-benchmark numbers. Overlapping speakers, accents, compression artifacts, and background noise are what actually break models. Entity precision also becomes critical in enterprise workflows. Misrecognizing an email address, phone number, or invoice ID can be more damaging than a minor grammatical error.
2. Real-time latency
Latency splits into two metrics:
Partial latency: time to first transcript token
Final latency: time to a stable, corrected transcript
For conversational AI and voice agents, partial latency is usually the constraint. A system can tolerate slightly slower final stabilization, but it can't tolerate a delayed initial response.
3. Multilingual and code-switching support
Global products don't operate in single-language environments. Modern requirements include broad language coverage, automatic language detection, and native code-switching — the ability to handle a speaker switching languages mid-sentence. The gap between "supports multiple languages" and "handles real-time cross-language conversation" is significant, and it's where most vendors quietly diverge.
4. Audio intelligence capabilities
Transcription alone is rarely enough. Common enterprise requirements include speaker diarization, sentiment analysis, summarization, named entity recognition, translation, and topic detection. The structural difference across vendors is whether these features are bundled into base pricing or billed separately as add-ons. That difference can double your effective hourly rate.
5. Deployment and data privacy
For enterprise buyers, deployment flexibility and data policy often outweigh model differences. This covers cloud vs. on-premise support, data residency, model training policies, and compliance certifications. With those criteria established, here's how the leading providers compare.
Gladia
Gladia is a pure-play speech-to-text API provider focused on transcription and audio intelligence. It stands out for strong multilingual speech recognition, supporting 100+ languages with native code-switching, low-latency real-time streaming, and a bundle of built-in audio intelligence features including diarization, translation, named-entity recognition, and sentiment analysis.
As a European provider, Gladia also emphasizes data sovereignty and enterprise-ready infrastructure, with flexible hosting across EU and US regions. That combination of multilingual performance, integrated audio intelligence, and compliance-friendly deployment makes it well suited for teams building:
Gladia’s two flagship models aren't a replacement/upgrade pair. They are built to complement each other. So you pick based on the job:
Solaria-1 is our breadth model, the most multilingual in the lineup, with 100+ languages and native code-switching across all of them, including 42 languages unavailable elsewhere. It's strong across both async and real-time, with ~103ms partial latency, ~270ms final latency, automatic language detection, and engineering to reduce hallucinations in noisy audio. On our open benchmark, Solaria-1 holds a strong #2 on Switchboard (37.3% WER, behind only Solaria-3). It also leads outright on speaker diarization accuracy: 3x more accurate diarization error rate (DER) than alternatives. It’s a solid pick for multi-speaker async workloads, clean or formal audio, and any use case where language breadth matters most.
Solaria-3 is built specifically for real-world business audio: noisy, fast-paced, and conversational. On production recordings, it ranks #1 across English and core European languages (EN, FR, DE, ES, IT), ahead of AssemblyAI, ElevenLabs, Deepgram, Mistral, and Speechmatics. It's 26% more accurate than Solaria-1 on real English customer calls and ranks #1 on Switchboard, widely considered the hardest conversational benchmark. It's currently async only, so if your audio is call-center recordings, sales calls, or anything genuinely messy, Solaria-3 is the one to reach for; if you need breadth, streaming, or code-switching, stay on Solaria-1.
Supports: Gladia’s audio intelligence features are bundled into base pricing, covering code switching, speaker diarization, sentiment analysis, summarization, named entity recognition, translation, topic detection, custom vocabulary, audio-to-LLM, and a lot more. Check our documentation for the full list.
Pricing: There are three tiers. Starter is the pay-as-you-go entry point, at $0.61/hr for async and $0.75/hr for real-time. Growth is where it gets competitive: rates drop as low as $0.20/hr async and $0.25/hr real-time, up to 67% below Starter, plus flexible concurrency and volume discounts. Enterprise moves to custom pricing, with unlimited concurrency, custom hosting, zero data retention, and dedicated support. All languages and all audio intelligence features are included at every tier, with no add-on math required.
Deployment and data privacy: Gladia stays highly compliant for regulated industries with certifications covering GDPR, HIPAA, SOC 2 Type 2, and ISO 27001. Hosting runs on European cloud providers with US East and West clusters available for teams that need data to stay stateside. Customer audio on paid tiers is never used for model training, and that's the default behavior, not an opt-in you have to remember to configure.
AssemblyAI
AssemblyAI positions itself as a speech understanding platform, pairing transcription with integrated LLM-powered analysis through LeMUR and its LLM Gateway. It's built for teams that want structured insights out of audio, not just a transcript.
Core models: AssemblyAI's current flagship is Universal-3.5 Pro, its most accurate model for both pre-recorded and real-time audio, alongside Universal-2, which trades some accuracy for broader 99-language coverage and a lower price point. On streaming, Universal-Streaming is the cost-effective default.
Supports: speaker diarization, sentiment analysis, entity detection, topic detection, summarization, translation, PII redaction, custom formatting, and natural-language prompting to steer transcription toward domain-specific terms without custom model training.
Pricing: free tier with $50 in credits (~185 hours pre-recorded, ~333 hours streaming); pay-as-you-go beyond that; custom enterprise pricing. Most features beyond base transcription are billed per hour as add-ons. Universal-2 starts at $0.15/hr, but stacking diarization, sentiment, entity detection, and summarization typically lands around $0.30–0.45/hr. Universal-3 Pro runs closer to $0.21/hr batch and $0.45/hr streaming. It's worth budgeting the fully-featured rate when comparing against bundled-pricing competitors.
Deployment and privacy: SOC 2 Type 2, GDPR compliant, HIPAA available, data routed through US infrastructure, model training opt-out available (forgoing a discount).
ElevenLabs
ElevenLabs built its name in voice synthesis, and its speech-to-text product, Scribe, is a newer addition to that stack rather than a ground-up STT platform. That origin shapes what it's good at: teams already using ElevenLabs for voice generation get transcription in the same ecosystem, but STT shares credits and concurrency with the rest of the platform.
Core models: Scribe v2 handles batch transcription across 99 languages with speaker diarization (up to 48 speakers), 56 entity types, keyterm prompting, and non-speech event tagging (laughter, music, applause). Scribe v2 Realtime is the streaming counterpart, built for voice agents and live captioning, with latency around 150ms and "predictive" transcription that anticipates likely next words. However, it drops diarization to hit that latency, and multilingual code-switching isn't a current priority for the realtime model.
Pricing: Scribe (batch) $0.22/hr, Scribe Realtime $0.39/hr, with entity detection at +$0.07/hr and keyterm prompting at +$0.05/hr.
Deployment and privacy: SOC 2, GDPR, and EU data residency / zero-retention modes are available. HIPAA compliance requires the Enterprise tier and a sales-negotiated BAA, it isn't self-serve the way it is with some competitors.
Deepgram
Another respected vendor, Deepgram, positions itself as a full voice AI platform: speech-to-text, text-to-speech, audio intelligence, and a dedicated voice agent API. Its clearest lane is low-latency streaming infrastructure.
Core models: Nova-3 is the general-purpose workhorse for both pre-recorded and streaming transcription. Flux, Deepgram's newer conversational model, is purpose-built for voice agents rather than general transcription. It bakes in model-integrated end-of-turn detection and configurable turn-taking dynamics (knowing when a speaker has actually finished, not just paused) at Nova-3-level accuracy.
Supports: speaker diarization, redaction, keyterm prompting, sentiment, summarization, topic detection, and intent recognition. Entity detection and translation aren't available as direct add-ons. Text-to-speech (Aura-2) offers 40+ voices and sub-200ms time-to-first-byte — Deepgram is still the only provider in this comparison offering integrated TTS alongside STT.
Pricing: Nova-3 runs around $0.0043/min (~$0.26/hr) pre-recorded and $0.0058/min (~$0.35/hr) streaming for the multilingual variant, pay-as-you-go. Flux runs $0.0065/min in English and $0.0078/min multilingual (10 languages). Add-ons stack on top: diarization is +$0.12/hr streaming; sentiment, summarization, topic detection, and intent recognition are billed per token rather than per hour. TTS is $0.03 per 1,000 characters.
Deployment and privacy: SOC 2 Type 2, cloud/VPC/on-premise options. The Model Improvement Program applies a 50% discount across the board, opting out forfeits it.
Speechmatics
Speechmatics positions itself as an enterprise-grade speech recognition provider with strong on-premise and air-gapped deployment — the go-to shortlist entry for regulated industries that can't send audio to a third-party cloud at all.
Core models: Ursa 2 is the current flagship, delivering an 18% WER reduction across 55+ languages compared to its predecessor, sub-1s real-time latency, and code-switching claimed at 35% better than the nearest competitor (per Speechmatics' own benchmark). It also now powers Speechmatics' Flow voice-agent API.
Supports: real-time streaming, batch transcription, speaker diarization, translation, punctuation and formatting, and domain-specific models including medical.
Pricing: free tier of 480 minutes/month (240 real-time, 240 batch); Pro starting around $0.24/hr (tier dependent); custom enterprise pricing including offline licensing.
Deployment and privacy: cloud, on-premise, and fully air-gapped infrastructure; ISO 27001, SOC 2 Type II, GDPR compliant.
Comparison: Best speech-to-text APIs by use case
STT Provider Comparison
Category
Gladia
AssemblyAI
ElevenLabs
Deepgram
Speechmatics
Positioning
Speech-to-text API with multilingual code-switching, best-in-class diarization, and industry-leading WER
Speech Understanding platform with integrated LLM-powered analysis
Voice AI platform (TTS-first) with Scribe STT built in
Full voice AI platform (STT + TTS + Audio Intelligence + Voice Agent API)
Enterprise-grade speech recognition with on-premise and air-gapped deployment
Core model(s)
Solaria-1, Solaria-3
Universal-3.5 Pro, Universal-2
Scribe v2, Scribe v2 Realtime
Nova-3, Flux
Ursa 2
Real-time latency
~103ms partial / ~270ms final (Solaria-1)
~300ms streaming
~150ms first-partial (Scribe v2 Realtime)
Sub-300ms; Flux optimized for turn-detection latency
GDPR, SOC 2 Type 2, HIPAA, ISO 27001, zero data retention option
SOC 2 Type 2, GDPR, HIPAA
SOC 2, GDPR, HIPAA (sales-gated Enterprise + BAA)
SOC 2 Type 2
ISO 27001:2022, SOC 2 Type II, GDPR
Model training policy
No training on paid-tier data, no opt-in by default
Opt-out available (forgoing discount)
Not specified
Model Improvement Program (50% discount); opt-out forfeits it
Not specified
Choosing the best speech-to-text API in 2026 depends on your specific use case. The providers differ not just in accuracy, but in multilingual support, deployment flexibility, pricing structure, and voice AI integration.
For multilingual speech-to-text and code-switching, Gladia is a strong fit: 100+ languages with native code-switching on Solaria-1, plus a second model in Solaria-3 for teams whose top priority is accuracy on noisy European business audio specifically. Audio intelligence is bundled rather than sold as separate add-ons either way.
AssemblyAI is particularly well-positioned for LLM-powered transcript analysis and long-context reasoning — teams that treat transcripts as structured data and need deeper semantic processing across long recordings.
For real-time voice agents, Deepgram and Gladia both post sub-300ms latency, with Deepgram's Flux specifically tuned for turn-taking and Gladia's Solaria-1 for multilingual, code-switching-heavy conversations. ElevenLabs' Scribe v2 Realtime is also competitive on raw latency if you're already building on the ElevenLabs stack for TTS.
Speechmatics remains the strongest choice for regulated industries that need on-premise or fully air-gapped deployment, a deployment model none of the pure-cloud API providers here can fully match.
Pricing models vary significantly too. Gladia and ElevenLabs use largely bundled, per-hour pricing (though ElevenLabs still charges separately for a couple of features); AssemblyAI and Deepgram use modular pricing with add-ons and token-based billing components; Speechmatics applies tiered pricing based on deployment model.
The differences show up in architecture, deployment flexibility, and how predictable your costs will be at scale.
See it for yourself: an unbiased way to compare STT APIs
Every number in the table above, ours included, comes from a vendor benchmark, a documentation page, or a third-party pricing tracker. That's useful for narrowing a shortlist, but it's not the same as knowing how a provider handles your audio: your accents, your background noise, and your industry vocabulary.
That's the gap Compare STT tool is built to close. It's a blind comparison tool covering Gladia, Deepgram, AssemblyAI, ElevenLabs, Speechmatics, and Mistral: you record or upload up to two minutes of audio, the tool runs it through two randomly selected providers, and you see both transcripts side by side without knowing which provider produced which one until after you've picked a winner. Votes feed a live, community-driven ELO leaderboard, and the full methodology is public, so you can see exactly how scores are calculated.
If you're evaluating providers for a production decision, running a handful of your own clips through the leaderboard is a fun experiment that we recommend. Try it and let us know about your results!
FAQs
What is the most accurate speech-to-text API in 2026?
Accuracy depends on the audio type: Gladia's Solaria-3 ranks #1 on noisy, real-world business audio (call centers, sales calls) across English and core European languages, while Solaria-1 leads on clean, formal, or highly multilingual audio.
Which speech-to-text API has the best multilingual support?
Gladia's Solaria-1 supports 100+ languages with native code-switching, including 42 languages unavailable through any other API in this comparison. AssemblyAI's Universal-2 covers 99 languages, and Speechmatics' Ursa 2 covers 55+ with a claimed 35% code-switching improvement over its nearest competitor.
Which STT provider is cheapest?
On a base async rate, AssemblyAI's Universal-2 starts lowest at $0.15/hr, but most teams need diarization, sentiment, or entity detection stacked on top, which typically pushes it to $0.30–0.45/hr. Gladia's Growth tier starts at $0.20/hr async with all audio intelligence features included, making the effective fully-featured price more competitive than the sticker price suggests.
Which speech-to-text API is best for real-time voice agents?
Deepgram and Gladia both report sub-300ms latency for real-time use. Deepgram's Flux is purpose-built for voice-agent turn-taking and end-of-turn detection, while Gladia's Solaria-1 is the stronger fit when the conversation is multilingual or code-switching-heavy.
Do these providers offer text-to-speech as well as speech-to-text?
Only Deepgram (Aura-2) and ElevenLabs (its core product) offer integrated TTS alongside STT in this comparison. Gladia, AssemblyAI, and Speechmatics are speech-to-text only.
Which providers support on-premise or air-gapped deployment?
Speechmatics is the only vendor here offering fully air-gapped deployment, alongside cloud and standard on-premise options. Deepgram offers cloud, VPC, and on-premise. Gladia, AssemblyAI, and ElevenLabs are cloud-only, with EU/US data residency options.
Are audio intelligence features (diarization, sentiment, summarization) included in the price?
It varies by vendor. Gladia bundles all audio intelligence features into base pricing at every tier. AssemblyAI, Deepgram, and ElevenLabs bill most of these as separate add-ons — AssemblyAI per hour, Deepgram per token or per feature, and ElevenLabs per feature.
Final thoughts
The speech recognition market in 2026 isn't about one clear winner; it's about alignment. All five platforms here are mature and technically capable. What separates them isn't basic transcription quality anymore. On clean audio, the top providers are within a percentage point or two of each other. It’s the architectural philosophy and product direction that should dictate your choice: the languages you need, how you deploy, your regulatory exposure, your latency budget, and whether speech-to-text is a background utility or a strategic layer of your product.
The best API is the one that fits your infrastructure. If multilingual code-switching, transparent pricing, industry-leading diarization, and WER are high on your list, it's worth giving Gladia a try at our playground and running a few of your own audio files through the blind comparison tool before you decide.
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Read more
Speech-To-Text
Migrating from Rev.ai to Gladia: what global teams should know
Speech-To-Text
Switching your speech-to-text provider: a migration checklist for note-takers and CCaaS
Speech-To-Text
How decision intelligence improves customer service consistency in contact centers
From audio to knowledge
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