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Speech-To-Text
ElevenLabs vs Gladia comparison for voice AI builders. Compare STT accuracy, latency, pricing, and features for production agents. Get real-world accuracy metrics, total cost models, and technical specs to evaluate whether unified vendor stack or best-of-breed STT fits your pipeline.
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Speech-To-Text
Meeting bot speech recognition requires sub-300ms STT latency, real-time diarization, and code-switching for reliable transcripts. Production meeting bots fail when transcription infrastructure cannot handle multi-speaker overlap, language switching, and speaker attribution in real time.
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Speech-To-Text
Meeting transcription mistakes that break production systems: crosstalk handling, diarization failures, and code switching issues. Learn how to architect STT pipelines that survive real world audio conditions, avoid silent WebSocket failures, and prevent cost model surprises at scale.
ElevenLabs vs Gladia comparison for voice AI builders. Compare STT accuracy, latency, pricing, and features for production agents. Get real-world accuracy metrics, total cost models, and technical specs to evaluate whether unified vendor stack or best-of-breed STT fits your pipeline.
TL;DR: ElevenLabs' Scribe v2 is a viable STT add-on for teams already committed to their ecosystem, with ~150ms latency suitable for simple conversational prototypes. For note-takers or AI meeting assistants requiring high accuracy on noisy and accented audio, robust code-switching across 100+ languages, and predictable costs at scale, Gladia Solaria-1 is a viable choice. Many teams run Gladia for STT and ElevenLabs for TTS in the same pipeline, treating them as complementary rather than competing layers.
For teams building voice AI pulls toward a single vendor for both listening (STT) and speaking (TTS) is real. One API contract, one invoice, one integration surface. ElevenLabs is the clear TTS standard, and their Scribe v2 model reports 93.5% accuracy on FLEURS benchmarks across 30 languages, making their unified stack argument measurably stronger than their v1 release. The question worth asking before consolidating is whether "good enough" transcription holds up when it bottlenecks your LLM's input quality at production scale.
This guide breaks down ElevenLabs Scribe v2 and Gladia Solaria-1 across accuracy, latency, audio intelligence, and pricing. The comparison gives you the numbers to make that call rather than relying on vendor marketing.
The table below covers the decision-relevant specs for both models based on current documentation and published benchmarks.
The core tension is specialization vs. consolidation. ElevenLabs optimizes for low-friction entry into their ecosystem. Gladia focuses on transcription performance in real-world conditions, including noisy environments, multiple speakers, accented speech, and multilingual conversations with code-switching, and performs particularly well in asynchronous use cases.
Accuracy on clean studio audio is not the evaluation you need. Your production audio contains background noise, overlapping speech, heavy accents, domain-specific vocabulary, and variable recording quality. That is where model architecture diverges in ways that matter.
Gladia Solaria-1 was built with that reality as the target. The Solaria-1 benchmark methodology uses Mozilla Common Voice and Google FLEURS, both designed to challenge STT models with diverse accents, dialects, and audio conditions across multiple dataset versions to avoid tuning the model to a single release. The result is a 94% Word Accuracy Rate across English, Spanish, French, and other common languages, with particular strength on high-value terms like names, numbers, and identifiers.
ElevenLabs Scribe v2 publishes a 93.5% accuracy figure across 30 commonly used European and Asian languages on the FLEURS benchmark. That 0.5 percentage point gap may look small in aggregate, but WER compounds on low-resource languages and noisy conditions where both models diverge most significantly.
For meeting assistants, note-takers, and CCaaS workflows where the transcript feeds an LLM, transcription accuracy is the ceiling on your system’s intelligence. What you cannot recover downstream is what the STT layer fails to capture accurately in the first place.
"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
Code-switching (mid-sentence language changes) is not an edge case for global meeting assistants, note-takers, and CCaaS workflows. It’s a routine part of how multilingual teams communicate. In practice, conversations regularly shift between languages within the same sentence, meaning transcription, summarization, and downstream workflows must handle these transitions seamlessly rather than treating them as exceptions.
Gladia's code-switching implementation requires enabling code-switching and, for best accuracy and latency, providing a small set of expected languages. The system annotates each transcript segment with the detected language code and constrains detection to your declared language list, which reduces false positives in high-variance audio. The automatic language detection runs across all 100+ supported languages without requiring you to declare a primary language upfront.
ElevenLabs Scribe v2 Realtime also handles mid-conversation language switches automatically. ElevenLabs supports language specification through the language\_code parameter, but the approach differs: Gladia's language list parameter constrains detection to a specific set of expected languages, while ElevenLabs focuses on single-language specification rather than multi-language constraint.
Gladia supports 100+ languages, including 42 not covered by alternative APIs. If your system serves Bengali, Tagalog, or Swahili speakers alongside English, that coverage gap is not theoretical, it shows up directly in production. As outlined in our multilingual overview, Solaria-1 is designed for real call center conditions, including background noise, overlapping speakers, and domain-specific vocabulary.
"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
ElevenLabs markets "negative latency" as a key differentiator for Scribe v2 Realtime. The technical mechanism is predictive: the model analyzes buffered audio patterns and anticipates the most probable next words and punctuation before the speaker finishes their phrase. This reduces perceived delay in the output display.
The limitation for LLM pipelines is determinism. A predictive model produces tokens that may need correction as the actual audio arrives, creating transcript updates that your orchestration layer has to handle. That correction overhead adds to your latency budget and complicates context window management.
Gladia's approach uses deterministic partials: each word is returned to your pipeline as it is confirmed from the audio signal, not predicted. For an LLM building context incrementally, accurate partials produce cleaner inputs than predictive text that might be overwritten 200ms later.
Our STT API delivers <103 ms on partial transcription, 270 ms TTFB, and approximately 698 ms for final transcripts based on our published latency measurement methodology. ElevenLabs documents approximately 150 ms overall end-to-end latency excluding network latency for Scribe v2 Realtime, but does not separately publish TTFB or final transcript timing for their STT models. The different measurement approaches mean these figures are not directly comparable.
The measurement methodologies differ, so a direct comparison requires evaluation against your own audio samples. Rather than focusing on latency alone, it’s more important to evaluate how each system performs on real-world audio conditions such as multilingual speech, accents, and overlapping speakers.
Speaker diarization is where the real-time gap between the two platforms becomes a production constraint. Gladia's speaker diarization for batch processing is powered by pyannoteAI's Precision-2 model, which delivers sharper speaker boundaries, better handling of overlapping speech, and higher consistency across languages. Gladia also supports channel-based diarization for telecom and contact center use cases where each speaker maps to a unique audio channel.
ElevenLabs Scribe v2 batch model supports diarization for up to 32 speakers activated by a boolean flag, but their real-time model does not currently support diarization.
Metadata richness differs primarily in how audio intelligence features are priced. Both APIs return word-level timestamps and language detection results. Gladia's Audio Intelligence API also includes named entity recognition (emails, names, alphanumeric identifiers), sentiment analysis, summarization, and Audio to LLM capabilities, all included in the base rate. ElevenLabs offers entity detection across 56 categories and keyterm prompting, but both are priced as add-ons above the base transcription rate.
"Their transcription quality is the best for many languages... Their documentation is clear and easy to integrate, and implement." - Verified GladiaUser in Higher Education on G2
The pricing models reflect fundamentally different assumptions about how you will use audio intelligence features at scale.
Gladia bills per hour, with speaker diarization, sentiment analysis, named entity recognition, and summarization bundled into the base rate. There are no setup fees or hidden costs
ElevenLabs bills per audio minute with base transcription starting at $0.40 per included hour, per their published API pricing page. Entity detection adds $0.080/hour and keyterm prompting adds $0.080/hour on top of that. Those additions are individually small, but they compound as features are enabled across a production workload.
Per-hour pricing models produce measurable differences at scale. For shorter audio segments, billing structures that rely on fixed increments can introduce inefficiencies when usage is aggregated across large volumes. When scaled to thousands of calls per day, these differences can accumulate and impact the total monthly cost. Our pricing model is designed to reflect actual usage more closely, avoiding unnecessary overhead from coarse billing increments.
At 10,000 hours/month with diarization and entity detection enabled, an all-inclusive per-second model rolls multiple capabilities into a single consistent rate rather than billing each feature as a separate add-on. If your pipeline only needs basic transcription, the difference narrows. If you need diarization, entity extraction, and sentiment together, the bundled model may offer cost advantages.
Both APIs support REST and WebSocket protocols, allowing your existing voice stack to connect without architectural changes. Our transcription initialization reference covers both real-time and async paths from a single, consistent API design, and our pre-recorded transcription API reference handles batch workloads at scale.
If you are currently on AssemblyAI or Deepgram, we provide dedicated migration guides from AssemblyAI and Deepgram that map endpoint and parameter changes directly. The playground walkthrough demonstrates real-time transcription without code, giving you a fast way to evaluate accuracy on your own audio before writing a single line of 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.Gon G2
One practical consideration worth noting: We focus on providing a robust transcription layer that handles multilingual audio, code-switching, and diarization reliably. This means teams retain control of the application layer, meeting assistants, analytics pipelines, or workflow automation, without relying on us to build the end-user features. For integrations via Zapier or processing recordings in asynchronous pipelines, our transcription API provides a consistent interface, so the integration surface remains stable across different use cases.
The decision reduces to what you are optimizing for.
Choose ElevenLabs Scribe if:
Choose Gladia Solaria-1 if:
The unified stack argument is appealing when you need to move quickly and minimize complexity. However, input quality directly affects LLM output, and any gaps in transcription accuracy on your most challenging audio become the ceiling for your agent’s overall performance.
Get started with 10 free hours to test accuracy on your own audio, including your messiest call center recordings or accented test samples.
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 is not currently supported in that mode. Gladia's real-time model returns partials in under 103ms, and real-time diarization is also not yet available in production for either platform.
Which is cheaper, Gladia or ElevenLabs?
At low volumes under 1,000 hours/month for basic transcription only, the difference is small. At 10,000+ hours/month with diarization and entity detection enabled, the two platforms have structurally different cost models: Gladia uses all-inclusive per-hour billing, while ElevenLabs uses a per-minute base rate with entity detection and keyterm prompting priced at $0.080/hour each as add-ons, per their published pricing page, meaning total cost will vary depending on which features you enable and how your usage is distributed.
Can Gladia detect multiple languages in one audio file?
Yes. Gladia's code-switching feature, enabled via code_switching: true, detects language changes mid-sentence across all 100+ supported languages, with a language list parameter recommended for best accuracy and latency when your expected language distribution is known.
Does ElevenLabs charge extra for diarization?
For the Scribe v2 batch model, diarization is included in the base transcription rate with no separate charge according to ElevenLabs' published pricing page. Entity detection and keyterm prompting are priced as add-ons at $0.080/hour each. The real-time Scribe v2 model does not currently support diarization.
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 integrating them in the same inference pipeline requires no architectural compromise.
What compliance certifications does Gladia hold?
Gladia holds SOC 2, GDPR, and HIPAA compliance. Audio is not used to retrain models for Pro and Enterprise plans, and no opt-out is required. For the Free plan, data may be used to improve and train models.
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. WER on clean audiobook data and WER on noisy call center audio for the same model can differ by 10+ percentage points.
Code-switching: Alternating between two or more languages within a single conversation or sentence, common among bilingual speakers that most STT models handle poorly without explicit multilingual 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. Reduces perceived delay but introduces correction events when predictions are wrong, which adds orchestration overhead in LLM agent pipelines.
Partials: Deterministic word-by-word transcript outputs streamed in real time as each word is confirmed from the audio signal, rather than predicted. Gladia's partials arrive in under 103ms per word and do not require downstream correction handling.
Diarization: The process of segmenting an audio recording by speaker identity, attributing each transcript segment to a specific speaker. Currently available in batch/async mode for both Gladia and ElevenLabs. Real-time diarization is not yet available 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's TTFB for Solaria-1 is 270ms, distinct from partial latency (<103ms) and final transcript delivery (~698ms) as documented in their latency measurement methodology.
Data Processing Agreement (DPA): A formal contract governing how a vendor processes personal data on behalf of a customer, required for GDPR compliance. Review this before signing any STT vendor contract that processes audio containing personal information.
