This guide breaks down ElevenLabs Scribe v2 and Gladia’s Solaria models across accuracy, latency, audio intelligence, and pricing. The comparison gives you the numbers to make that call rather than relying on vendor marketing.
TL;DR:
- Accuracy: On Hugging Face's independent Open ASR Leaderboard’s private data track, Gladia's Solaria-3 ranks #2 overall (8.26% avg WER) against ElevenLabs Scribe v2 at #14 (9.11%).
- It's audio-dependent, not universal: While ElevenLabs performs well on clean, scripted speech, Gladia is clearly ahead on conversational and accented audio (14.18% vs. 15.76%, and better on both US and non-US accents), the type most production voice AI processes.
- The open-methodology benchmark backs the same pattern: Gladia’s Solaria models are #1 on real customer support audio, Switchboard conversational speech, and curated Earnings22 calls — consistent with the independent HF result — while Scribe v2's advantage shows up on cleaner, scripted datasets like VoxPopuli and LibriSpeech.
- Pricing: At the lowest tier, ElevenLabs' pay-as-you-go pricing can undercut Gladia's Starter tier even with add-ons factored in, but that gap closes and reverses as soon as volume commitments kick in with Gladia’s Growth plan.
- They're not mutually exclusive: many teams run Gladia for STT and ElevenLabs for TTS in the same pipeline, treating them as complementary layers rather than competing ones.
How we verified this comparison
Most STT comparisons lean on a single vendor's self-published benchmark, numbers that are closed-source and unverifiable by design. For this update, we sorted our sources into two tiers based on the following criterion only: independence from the vendor being measured and open-source, reproducible and verifiable benchmark.
Independent sources:
- Hugging Face's Open ASR Leaderboard, Private data track: run by Hugging Face on held-out audio contributed by Appen and DataoceanAI, with no individual data provider or split exposed to prevent gaming. This is the only source we found that currently scores both companies' current-generation models (Solaria-3 and Scribe v2) on identical, non-public audio. It's a live, continuously updated board, so treat these as a snapshot rather than a permanent ranking.
- Artificial Analysis: a third party with no stake in either vendor, running a standardized eval across the market; as of this writing it scores Gladia's Solaria-1 rather than Solaria-3, so it's a different comparison than the HF result above (see footnote in the accuracy section).
Vendor-run, open methodology:
- Gladia's open-source benchmark: what earns it a place alongside the independent sources is that the methodology is genuinely transparent: normalization code is open-sourced on GitHub, datasets are documented, and results are reproducible by a third party who wants to check them. It's also the only source with per-dataset granularity (Switchboard, VoxPopuli, Common Voice, Earnings22 individually) that the independent aggregate scores don't break out. Where it disagrees with the independent sources, we say so rather than picking the one that tells the cleaner story.
Vendor documentation for anything that isn't a comparative accuracy claim, including pricing, feature limits, compliance certifications, verified directly against ElevenLabs and Gladia as of July 2026.
At a glance: ElevenLabs Scribe v2 vs. Gladia Solaria
Gladia vs ElevenLabs Comparison
| Feature |
Gladia (Solaria-1 / Solaria-3) |
ElevenLabs (Scribe v2) |
| Primary use case |
Dedicated STT and audio intelligence API |
TTS-first platform with STT built in |
| Real-time partial latency |
<103ms (Solaria-1; Solaria-3 is async-only) |
~150ms end-to-end (excludes network) |
| WER — HF Open ASR Leaderboard, Private track (independent) |
8.26% avg, #2 overall (Solaria-3) |
9.11% avg, #14 overall |
| Conversational speech |
14.18% (Gladia clearly ahead) |
15.76% |
| Scripted/clean speech |
3.12% |
3.00% (essentially tied) |
| WER: open benchmark (vendor-run, open methodology) |
Leads on real customer audio, Switchboard, Earnings22 (Solaria-3) |
Leads on VoxPopuli, Common Voice |
| Language support |
100+ languages on Solaria-1; Solaria-3 focuses on EN + core European (FR, DE, ES, IT) |
90+ languages |
| Code-switching |
Native on Solaria-1, optional language list for precision. Automatic language detection |
Automatic mid-conversation detection; single-language specification via API |
| Diarization in batch |
Yes, industry-leading pyannoteAI Precision-2 |
Yes, up to 32 speakers |
| Diarization in real-time |
No |
No |
| Keyterm / custom vocabulary |
Included at all rates |
Up to 1,000 keyterms, billed as add-on |
| Entity detection |
Included at all rates |
65 entity categories, billed as add-on |
| Pricing model |
Per-hour, all features included in the rate |
Per-hour base rate, entity detection and keyterm prompting billed separately |
| Compliance |
SOC 2 Type II, ISO 27001, HIPAA, GDPR, HDS hosting |
SOC 2 Type II, ISO 27001, PCI DSS Level 1, HIPAA (BAA via Enterprise), GDPR |
| Deployment |
Managed cloud clusters in the EU and US. No on-prem or air-gapped option |
Cloud, with enterprise data residency and Zero Retention Mode |
A note on the table structure: ElevenLabs currently ships one STT model family, Scribe v2, in batch and realtime variants. Gladia ships two separately-trained models with different design targets — Solaria-1 for broad multilingual coverage and code-switching, Solaria-3 for accuracy on core-European real-world audio.
Transcription accuracy: what the independent data actually shows
Accuracy on clean studio audio isn't the evaluation that matters for production systems. Your audio has background noise, overlapping speech, accents, domain vocabulary, and inconsistent recording quality. The independent data confirms the two platforms genuinely diverge on exactly that axis.
On Hugging Face's Open ASR Leaderboard Private data track, scored on audio neither vendor can see or tune against, Gladia's Solaria-3 posts an average WER of 8.26%, #2 among every model on the board. ElevenLabs Scribe v2 posts 9.11%, ranking #14. The gap is concentrated exactly where it matters most for production voice AI: on conversational speech, natural dialogue with interruptions, fillers, and variation. Solaria-3 holds a clear lead (14.18% vs. 15.76%), and it carries that lead across both US-accented (8.46% vs. 9.38%) and non-US-accented (7.89% vs. 8.32%) audio. On scripted, controlled-recording audio the two are essentially tied with ElevenLabs scoring 3.00% vs. Gladia 3.12%, which is the audio type least representative of what a meeting assistant, contact-center pipeline, or voice agent actually processes.
Gladia's own open benchmark, with 8 datasets, 9 providers, 74+ hours of audio, methodology and normalization code published on GitHub for independent reproduction, confirms the same pattern with finer-grained detail: Solaria-3 leads on real customer support audio, Switchboard conversational speech, and curated Earnings22 calls, while Scribe v2's advantage is concentrated on VoxPopuli, Common Voice, and Multilingual LibriSpeech audiobooks: clean, scripted, or narrated content. Two sources land on the same conclusion: the Solaria series is the stronger choice once audio gets messy.
The overall takeaway holds up across every source here: match the model to your actual audio. If your transcript is feeding an LLM for meeting notes, call QA, or agent context from real conversations, the independent data supports Gladia. If your audio is closer to scripted or narrated content, the gap narrows to a rounding error, and ElevenLabs' broader keyterm and entity-detection toolkit may tip the decision instead.
Note: A third independent source, Artificial Analysis, currently tracks Solaria-1 rather than Solaria-3 — Gladia's original broad-multilingual model, not the real-audio-specialized one benchmarked above. On that index, Scribe v2 scores 2.2% AA-WER against Solaria-1's 4.1%, a gap that lines up with exactly the kind of real-world audio performance Solaria-3 was built to close. Once Artificial Analysis adds a Solaria-3 entry, this comparison will reflect Gladia's current flagship model directly.
"Gladia delivers precise speech-to-text transcriptions with reliable timestamps, making it perfect for downstream tasks. It saves time and ensures smooth integration into our workflows." - Verified User in Computer Software on G2
"Gladia deliver real time highly accurate transcription with minimal latency, even across multiple languages and accents. The API is straightforward and well documented, making integration into our internal tools quick and easy." - VFaes W. on G2
Handling real-world audio: accents, noise, and code-switching
Code-switching, mid-sentence language changes, isn't an edge case for global meeting assistants, note-takers, and CCaaS workflows. It's routine for multilingual teams, and transcription, summarization, and downstream automation all need to treat it as normal input rather than an exception.
Gladia's code-switching implementation requires enabling the feature and, for the best accuracy and latency, supplying a small set of expected languages. Each transcript segment is annotated with the detected language code, and constraining detection to a declared language list reduces false positives on high-variance audio. Automatic language detection runs across all 100+ supported languages without requiring a primary language upfront.
ElevenLabs Scribe v2 Realtime also handles mid-conversation language switches automatically, and ElevenLabs shipped a specific fix in this area in 2026: English words embedded in another language, e.g, a product name spoken mid-sentence in Hindi, now stay in Latin script automatically across supported Indic languages, without a separate language-switching configuration.
Gladia supports 100+ languages on Solaria-1, including the ones that aren't widely covered elsewhere. If your system serves Bengali, Tagalog, or Swahili speakers alongside English, that coverage gap shows up in production, not just on a spec sheet. Solaria-3 trades that breadth for depth: it currently targets English and five core European languages rather than the full language set, so the choice between Gladia's two models depends on whether your audio is broadly multilingual or concentrated in EN/FR/DE/ES/IT.
"Gladia provides a highly accurate real-time speech-to-text solution for high volumes of support and service calls. Latency is low and accuracy high, even for numericals." - Verified User in Financial Services on G2
Real-time latency and performance
"Negative latency" vs. deterministic partials
ElevenLabs markets "negative latency" as a differentiator for Scribe v2 Realtime. The mechanism is predictive: the model analyzes buffered audio and anticipates the most probable next words and punctuation before the speaker finishes, which reduces perceived delay in the displayed output.
The trade-off for LLM pipelines is determinism. A predictive model produces tokens that may need correcting once the actual audio arrives, and your orchestration layer has to absorb those correction events, which is an overhead that adds to your latency budget and complicates context-window management for a downstream LLM.
Gladia's real-time model, Solaria-1, uses deterministic partials instead: each word is returned once it's confirmed from the audio signal, not predicted. For an LLM building context incrementally, confirmed partials produce cleaner inputs than predictive text that might be overwritten a moment later. Solaria-3 is async-only and isn't part of this comparison.
Solaria-1 is doing a specific, harder job than a single-language, single-condition model: it's the only model in this comparison holding sub-103ms partial latency, native mid-sentence code-switching, and broadest language coverage simultaneously, in the same real-time stream. That combination is why it remains Gladia's production real-time model, built to stay usable across the full range of languages and switching patterns a live multilingual product actually encounters.
Documented latency figures
Gladia reports under 103ms on partial transcription, 270ms time-to-first-byte, and roughly 698ms for final transcripts on Solaria-1. ElevenLabs documents approximately 150ms end-to-end latency for Scribe v2 Realtime, excluding network latency, but doesn't separately publish TTFB or final-transcript timing. Because the measurement methodologies differ, validate against your own audio and network path before committing to either.
Audio intelligence features
Speaker diarization is where the real-time gap between the two platforms becomes a genuine production constraint: neither offers it in real time as of this update. For batch processing, Gladia's diarization runs on pyannoteAI's Precision-2 model, and on Gladia's open benchmark it posts a 3x lower diarization error rate (DER) than every other tested provider.
One consideration worth flagging: that benchmark evaluates ElevenLabs Scribe-v1, the first generation model. We could not find a current, independently verified diarization comparison against Scribe v2, so treat Gladia's diarization lead as directional. ElevenLabs Scribe v2's batch model supports diarization for up to 32 speakers, activated with a single flag.
Metadata and enrichment have both expanded. ElevenLabs raised keyterm prompting from 100 to 1,000 terms in April 2026 and offers entity detection across 65 categories, dynamic audio tagging for non-speech events like laughter or music, and a PII auto-redaction mode. Both are still priced as add-ons above the base transcription rate. Gladia's Audio Intelligence API bundles named entity recognition, sentiment analysis, summarization, and Audio-to-LLM output into the same per-hour rate. So there is nothing to itemize separately.
"Their transcription quality is the best for many languages... Their documentation is clear and easy to integrate, and implement." - Verified Gladia User in Higher Education on G2
Pricing and total cost of ownership
Since pricing is one of the most dynamically changing aspects, it’s worth checking directly with both vendors before you model your own workload, rates move.
ElevenLabs bills per hour through its API: $0.22/hr for Scribe v2 batch and $0.39/hr for Scribe v2 Realtime, with entity detection at +$0.07/hr and keyterm prompting at +$0.05/hr, both batch-only add-ons.
Gladia bills per hour with diarization, sentiment analysis, named entity recognition, and summarization bundled into the rate at every tier: Starter is pay-as-you-go at $0.61/hr async and $0.75/hr real-time, with 50€ in free credits. Growth drops to as low as $0.20/hr async and $0.25/hr real-time on committed volume. Enterprise adds zero data retention.
Run the numbers side by side and the picture is more nuanced.
Pricing Plan Comparison
| Provider / Plan |
Async rate |
1,000 hrs/mo |
10,000 hrs/mo |
What’s included |
| ElevenLabs Scribe v2 (base only) |
$0.22/hr |
$220 |
$2,200 |
Transcription, diarization, language detection |
| ElevenLabs Scribe v2 + entity detection + keyterm prompting |
$0.34/hr |
$340 |
$3,400 |
Adds entity detection, up to 1,000 keyterms |
| Gladia Starter (pay-as-you-go) |
$0.61/hr |
$610 |
$6,100 |
Everything included: diarization, NER, sentiment, summarization |
| Gladia Growth (committed volume) |
from $0.20/hr |
from $200 |
from $2,000 |
Same bundle as Starter, at volume-discounted rate |
The upshot: Gladia comes out cheaper once you commit to Growth-tier volume, at which point its bundled per-hour rate undercuts ElevenLabs even with add-ons included. Model both scenarios rather than assuming either vendor's marketed rate is the one you'll actually pay. Real-time pricing follows the same pattern: Gladia Starter at $0.75/hr runs well above ElevenLabs Realtime at $0.39/hr, while Gladia Growth's $0.25/hr undercuts it.
Developer experience and integration
Both APIs support REST and WebSocket protocols, so an existing voice stack can connect without an architectural rebuild. Gladia's transcription initialization reference covers real-time and async paths from a single API design.
If you're currently on AssemblyAI or Deepgram, Gladia publishes dedicated migration guides mapping endpoint and parameter changes directly, and a no-code playground walkthrough lets you evaluate real-time transcription on your own audio before writing integration code.
"The speed and accuracy of the transcriptions is really solid, especially with challenging audio. I also like how easy the API is to setup, it works nicely without too much fiddling." - Adam B. on G2
One practical difference worth noting: Gladia is a pure-play transcription layer rather than an end-to-end application platform, so teams retain control of the application layer, including meeting assistants, analytics pipelines, or workflow automation, without depending on Gladia to ship end-user features. ElevenLabs, by contrast, is building a broader voice-AI platform around Scribe (Agents, TTS, dubbing).
Final verdict: when to choose which
The decision reduces to what you're optimizing for, and the independent data supports a more specific answer than either vendor's own marketing.
Choose ElevenLabs Scribe v2 if:
- You're building on ElevenLabs for TTS or Agents already, and a single vendor contract is worth more to you than a small accuracy edge.
- Your audio is clean, scripted, or single-speaker.
- You need up to 1,000 keyterms or 65-category entity detection and don't mind them as billed add-ons.
- Your volume is moderate and pay-as-you-go, where ElevenLabs' fully-loaded rate is now the cheaper option outright.
Choose Gladia (Solaria-1 or Solaria-3) if:
- Your audio is conversational, accented, or multi-speaker (real customer calls, contact-center audio, meetings) where Solaria-3 holds an independently Hugging-Face-verified lead reporting 10% lower WER on conversational speech; better on both US and non-US accents.
- You need broad multilingual coverage and native code-switching across 100+ languages, and sub-103ms real-time latency with deterministic partials: Solaria-1's combination of breadth and speed.
- You want diarization, NER, sentiment, and summarization included at every tier with no per-feature line items.
- You're at a committed, high-volume scale, where Gladia's Growth tier undercuts ElevenLabs' fully-loaded rate.
What hasn't changed is the underlying trade-off: your transcript's accuracy ceiling is set by whichever model actually handles your audio distribution. The independent data in this update makes that trade-off more precise than it was: clean audio favors ElevenLabs, conversational, accented, real-life audio favors Gladia. However, "test on your own recordings" is still the right final step.
Before you get there, we'll admit we had some fun with this. We built a blind ASR comparison tool, mostly out of curiosity: drop in an audio file, and it transcribes it across the major engines side by side, provider names hidden until you've picked a favorite. Feed it something messy — crosstalk, a heavy accent, a mid-sentence language switch — and it's a quick, oddly satisfying way to see which engines actually hold up once real speech gets involved.
It's not a substitute for the real evaluation. It won't tell you your production WER, it won't test at your volume, and it's one clip at a time rather than a reproducible benchmark run. Think of it as a gut check before you commit to step one of running your own worst audio through every candidate with identical settings, which is still the only methodology that produces numbers you can defend to your team.
If you’re up for testing Gladia, we’re giving away €50 worth of free credits, no expiry, to test accuracy on your own audio, including your messiest call-center recordings or accented test samples.
FAQs
Does ElevenLabs offer real-time transcription?
Yes, via Scribe v2 Realtime at approximately 150ms end-to-end latency excluding network latency, though real-time diarization isn't supported in that mode. Gladia's real-time model, Solaria-1, returns partials in under 103ms; real-time diarization also isn't yet available on Gladia.
Which is more accurate, Gladia or ElevenLabs?
It depends on your audio. On Hugging Face's independent Open ASR Leaderboard Private data track, Gladia's Solaria-3 ranks #2 overall (8.26% avg WER) against ElevenLabs Scribe v2 at #14 (9.11%), with Gladia's lead concentrated in conversational and accented speech; on clean, scripted audio the two are close, with ElevenLabs marginally ahead. Which comparison applies to you depends on your audio type and which Gladia model you'd actually deploy.
Which is cheaper, Gladia or ElevenLabs?
It depends on volume and commitment. At pay-as-you-go rates, ElevenLabs' Scribe v2 with entity detection and keyterm prompting enabled ($0.34/hr) is now cheaper than Gladia's Starter tier ($0.61/hr), even though Gladia's rate is all-inclusive. Gladia becomes the cheaper option at committed Growth-tier volume, where its rate can drop to $0.20/hr. Model both at your actual monthly hours before deciding.
Can Gladia detect multiple languages in one audio file?
Yes. Gladia's code-switching feature, detects language changes mid-sentence across all 100+ supported languages on Solaria-1, with a language-list parameter recommended for best accuracy and latency when your expected language distribution is known. Solaria-3 currently focuses on English plus French, German, Spanish, and Italian.
Does ElevenLabs charge extra for diarization?
No. For the Scribe v2 batch model, diarization of up to 32 speakers is included in the base transcription rate. Entity detection (+$0.07/hr) and keyterm prompting (+$0.05/hr, up to 1,000 terms) are the two features billed separately. The real-time Scribe v2 model doesn't currently support diarization at all.
Can I use Gladia for STT and ElevenLabs for TTS in the same pipeline?
Yes. They operate on different layers: Gladia handles audio input and transcription, ElevenLabs handles voice synthesis output. Both expose standard REST and WebSocket interfaces, so combining them in one inference pipeline doesn't require an architectural compromise.
What compliance certifications does Gladia hold?
Gladia holds SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliance as well as supports HDS hosting for healthcare enterprises. On Growth and Enterprise plans, audio isn't used to retrain models and no opt-out is required; on the Starter plan, data can be used to improve models by default.
Does ElevenLabs use my audio to train its models?
On ElevenLabs' non-Enterprise tiers, audio may be used to improve its models by default unless you opt out through the account's Data Use settings, and that opt-out applies only prospectively — audio already submitted may already be part of a training set. Enterprise customers get training off by default plus Zero Retention Mode, which covers the Speech-to-Text, Text-to-Speech, and Agents APIs.
Key terms
Word Error Rate (WER): The standard STT accuracy metric, calculated by dividing the sum of substitutions, deletions, and insertions by the total number of reference words. On the independent HF Open ASR Leaderboard, the same models' WER roughly doubles moving from scripted to conversational audio, which is why a single aggregate accuracy number is rarely the whole story.
Code-switching: Alternating between two or more languages within a single conversation or sentence, common among bilingual and multilingual speakers, and a pattern most STT models handle poorly without explicit support.
Negative latency: A predictive transcription technique used by ElevenLabs that anticipates upcoming words from buffered audio patterns to display text before the speaker finishes a phrase. It reduces perceived delay but introduces correction events when predictions are wrong, adding orchestration overhead in LLM agent pipelines.
Partials: Word-by-word transcript output streamed in real time. Gladia's partials are deterministic, confirmed from the audio signal rather than predicted, and arrive in under 103ms per word without requiring downstream correction handling.
Diarization: The process of segmenting an audio recording by speaker identity and attributing each transcript segment to a specific speaker. Available in batch/async mode for both Gladia and ElevenLabs as of this update; real-time diarization isn't in production for either platform.
Time to first byte (TTFB): The latency from when audio is sent to an STT API to when the first transcript token is returned. Gladia reports 270ms TTFB for Solaria-1 (vendor-reported, not independently verified in this update), distinct from partial latency (<103ms) and final transcript delivery (~698ms).
Data Processing Agreement (DPA): A formal contract governing how a vendor processes personal data on behalf of a customer, required for GDPR compliance. Review the training-data and retention clauses in any STT vendor's current DPA before signing, since these terms change more often than the rest of the contract.
