Why Shunya is a stronger choice than Gladia for production speech-to-text

Gladia has built a strong audio intelligence platform around speech-to-text, real-time transcription, multilingual audio, code-switching, diarization, sentiment, named entity recognition, translation, summarization, and audio-to-LLM workflows. Its current platform offers 100+ languages, while its newer Solaria models are designed around different production workloads.

Its latest Solaria-3 model is particularly focused on business audio. Gladia says it ranks first on its tested English customer-call and core European-language evaluations, while Solaria-1 remains its option for broader language coverage, real-time streaming, and multilingual code-switching.

So the interesting comparison is not simply which platform supports more languages or has more features.

Both platforms are moving beyond basic transcription.

The more important question is which architecture gives enterprises better control over production speech across accuracy, model specialization, latency, concurrency, domain vocabulary, speech intelligence, and deployment.

Shunya Zero STT takes a model-specialized approach with 216+ languages, 3.10% published composite WER across eight OpenASR benchmarks, sub-500ms streaming performance, 240+ concurrent streams per GPU, and dedicated models for Indic speech, code-switching, and healthcare.

It also extends transcription into intent, sentiment, emotion, and speech intelligence, and connects ASR with TTS, voice agents, SLMs, and edge speech understanding through the broader Shunya Platform.

For companies evaluating Gladia, that creates a broader decision than “which API transcribes audio?”

It becomes:

Which platform is better aligned with the speech workloads and voice applications we are actually building?

Gladia has evolved beyond transcription

Gladia’s product is no longer just a speech-to-text API.

Its current audio intelligence platform bundles capabilities such as:

Transcription

Speaker diarization

Code-switching

Sentiment analysis

Named entity recognition

Translation

Summarization

Custom vocabulary

Audio-to-LLM

Gladia says these capabilities are included in its base pricing rather than being individually charged add-ons.

Its newer Audio-to-LLM workflow goes even further by combining transcription, diarization, and LLM analysis into a single request, returning structured outputs through one webhook.

That is a compelling product experience.

Shunya takes a different path.

Its speech intelligence layer sits on top of a specialized ASR model family and can extend transcription into intent, sentiment, emotion, diarization, and speaker information.

The broader Shunya Platform then takes that speech into TTS, voice agents, SLMs, edge speech understanding, and knowledge-grounded AI.

So the fundamental difference is not whether either platform goes beyond transcription.

Both do.

The question is how much of the speech stack you want the platform to own.

One model does not have to solve every speech problem

Shunya’s strongest architectural differentiator is its specialized STT family.

Instead of using one general model for every production workload, Shunya provides dedicated paths for different speech conditions:

Zero STT for broad multilingual speech.

Zero STT Indic for Indian-language speech.

Zero STT Codeswitch for mixed-language conversations.

Zero STT Med for healthcare and clinical speech.

It also provides on-device models for lightweight local inference.

The idea is simple:

A medical consultation is not the same speech problem as a contact-center call.

A Hinglish conversation is not the same speech problem as a clean English recording.

A high-volume voice agent does not have exactly the same requirements as batch transcription.

Model specialization lets enterprises optimize for the workload instead of asking one model to be equally good at everything.

Gladia also has a multi-model architecture. Its current offering distinguishes Solaria-1 from Solaria-3, with Solaria-1 focused on breadth, real-time streaming, and code-switching, while Solaria-3 is positioned around difficult business audio and is currently async-only.

That makes the competition much more interesting.

The differentiation is not “one vendor has multiple models.”

It is what those models are specialized for and how directly developers can map them to their production workload.

Solaria-3 changes the accuracy conversation

Gladia’s new Solaria-3 model deserves particular attention.

Gladia says Solaria-3 is designed for real-world business audio, including noisy, fast-paced, conversational recordings, and reports that it ranks first across its tested English and core European-language customer-call evaluations.

That makes Solaria-3 a serious production ASR model.

Shunya currently publishes a different set of evidence.

Its Zero STT benchmarks report a 3.10% composite WER in English across eight OpenASR benchmarks, with datasets spanning speech such as LibriSpeech, SPGISpeech, TED-LIUM, AMI, VoxPopuli, GigaSpeech, and Earnings22. Shunya also publishes 146× real-time throughput on the benchmark.

These results should not be treated as directly interchangeable.

Different vendors use different datasets, evaluation pipelines, model versions, and test conditions.

For enterprise buyers, the better question is:

How does the model perform on our audio?

And that means testing:

Phone calls

Background noise

Overlapping speakers

Accents

Fast speech

Names

Numbers

Industry terminology

Code-switching

The model with the lowest published WER isn’t necessarily the model that produces the fewest business-critical errors.

Production accuracy is about critical words

Consider a financial-services call:

“My account number is 438729 and the transaction amount is twenty-five thousand.”

Getting the sentence structure right is not enough.

The system needs to get the account number and amount right.

Now consider:

“The doctor prescribed metformin five hundred milligrams.”

A single terminology error can change the meaning.

This is why production ASR evaluation should track critical-word accuracy, not only overall WER.

Gladia also recognizes this problem. Its current custom vocabulary capabilities are designed to improve recognition of brand names, acronyms, technical terms, and unusual sound patterns. Gladia recommends custom vocabulary and custom spelling together for some terminology-heavy workflows.

Shunya combines terminology normalization with specialized speech models.

That means there are two layers of adaptation:

Vocabulary-level adaptation

and

model-level specialization.

For enterprises with highly specialized speech environments, that distinction can matter.

Indian speech is a major Shunya differentiator

Gladia has strong global multilingual capabilities.

Its current Solaria-1 offering supports 100+ languages, and the company specifically promotes language breadth and code-switching as core strengths.

But Indian speech is about more than checking whether Hindi or Tamil appears on a language list.

Real Indian conversations often contain:

Regional accents

English words inside Indian-language sentences

Local names

Brand names

Informal speech

Phone-quality audio

Rapid code-switching

Shunya’s Zero STT Indic is specifically built around 55+ Indian languages and regional speech, while Zero STT Codeswitch targets mixed-language conversations.

This gives enterprises a clearer specialization path:

Indic speech → Indic model

Indic-English speech → Codeswitch model

That can be particularly important for Indian banking, insurance, telecom, healthcare, consumer, and public-sector applications.

Gladia’s broad multilingual model may be better suited to teams that need large global language coverage across a wide range of markets.

The question is not which has the larger language list.

It’s which platform is optimized around the languages and speech patterns your customers actually use.

Code-switching is one of Gladia’s strongest features

Gladia has invested heavily in code-switching.

Its current documentation allows developers to enable code_switching for real-time and asynchronous transcription, with the model dynamically adapting to the languages used in the conversation.

Gladia says Solaria-1 can handle native code-switching across its supported languages.

That’s a significant capability.

Shunya approaches the problem through a dedicated Zero STT Codeswitch model, designed around mixed-language speech such as Hinglish, Tanglish, and Benglish.

That gives the two products different areas of emphasis.

Gladia: broad multilingual code-switching across its global language set.

Shunya: dedicated specialization around Indic code-switching.

For global multilingual products, Gladia’s approach is compelling.

For Indian customer-service and conversational applications, Shunya’s specialized route can be particularly relevant.

The correct comparison is therefore not “who supports code-switching?”

Both do.

The real question is:

How well does each system handle the exact languages your customers mix?

Real-time speech is where latency becomes a product feature

Gladia has a particularly strong real-time story.

Its current Solaria-1 documentation reports partial transcripts in under 103ms, making it well suited for live voice interactions and meeting assistants.

Shunya currently publishes sub-500ms first-token streaming performance for Zero STT.

These numbers measure slightly different things, so they should not be treated as a direct apples-to-apples benchmark.

For a production voice application, measure:

First partial token

Time to final transcript

Transcript stability

Endpointing

Speaker attribution

Interruption behavior

End-to-end response latency

A voice agent doesn’t just need fast transcription.

It needs the entire listen → understand → reason → respond loop to feel instantaneous.

This is why Shunya’s ASR is part of a larger voice-agent stack, rather than being treated as an isolated transcription endpoint.

High concurrency can matter more than single-stream latency

A benchmark on one stream doesn’t tell you what happens when the system is processing hundreds of calls simultaneously.

This matters for:

Contact centers

Telecom

Voice-agent platforms

Large meeting platforms

Customer-support automation

Shunya publicly publishes 240+ concurrent streams per GPU for Zero STT.

That gives engineering teams a concrete performance metric to investigate.

Gladia currently describes its Enterprise offering as providing unlimited concurrency, while its Growth plan offers flexible concurrency as volume scales.

These are not directly comparable measurements.

One is a published inference-capacity metric, while the other is a managed-service capacity model.

For enterprise buyers, the right question is therefore:

How much does it cost to maintain our target latency at our peak concurrency?

A system that works beautifully at ten simultaneous calls can behave very differently at five hundred.

Gladia has a strong all-inclusive pricing model

One of Gladia’s strongest commercial advantages is its bundled pricing model.

Gladia’s current Starter pricing begins at $0.61/hour for asynchronous transcription and $0.75/hour for real-time, while Growth pricing can fall as low as $0.20/hour async and $0.25/hour real-time with volume commitments.

Gladia says features including diarization, sentiment analysis, NER, translation, summarization, and code-switchingare included in the base pricing rather than being charged separately.

That is genuinely attractive.

Shunya currently lists:

Zero STT: $0.0039/minute

Zero STT Indic: $0.0045/minute

Zero STT Codeswitch: $0.005/minute

Zero STT Med: $0.005/minute

See the latest Shunya pricing for current volume and enterprise options.

At $0.0039/minute, standard Zero STT is approximately $0.234/hour before volume discounts.

So the pricing comparison isn’t simply “Shunya is cheaper.”

For some managed transcription workloads, Gladia’s Growth pricing can be extremely competitive, and its bundled feature model makes cost forecasting easier.

The better comparison is cost for the complete application workflow.

If one platform requires separate services for downstream intelligence and another bundles them, calculate the total.

If one workload requires a specialized ASR model, calculate that model’s cost.

If you need high concurrency, include the infrastructure economics.

If you need private deployment, include the operational cost.

Cost per successful conversation is more useful than cost per transcription minute.

Audio-to-LLM is a compelling Gladia feature

Gladia’s Audio-to-LLM deserves specific attention.

Instead of building:

Audio → STT → diarization → LLM → structured output

Gladia allows those steps to be handled through a single API request, returning the resulting structured output through a webhook.

For developers building meeting assistants, call summarization, action-item extraction, and other applications, this can significantly reduce integration work.

Shunya takes a more modular platform approach.

Its speech intelligence layer provides structured speech understanding, while the broader Shunya Platform connects speech with SLMs, knowledge graphs, voice agents, TTS, and edge speech understanding.

This makes the two architectures attractive for different reasons.

Gladia: simplify the path from audio to structured output.

Shunya: provide the building blocks for a broader voice intelligence system.

If your requirement is simply:

“Send audio and get structured information back.”

Gladia’s Audio-to-LLM is compelling.

If your requirement is:

“Build a complete voice system around specialized speech models and downstream intelligence.”

Shunya’s broader platform becomes more relevant.

Speech intelligence is where Shunya extends beyond ASR

Shunya’s Zero STT workflow is built around more than the transcript.

It can move from:

Audio

↓

Transcript

↓

Intent

↓

Sentiment

↓

Emotion

↓

Speaker labels

This matters in applications such as contact centers, BFSI, healthcare, telecom, and enterprise voice automation.

For example, a customer interaction could produce:

Transcript: what happened

Intent: why they called

Sentiment: how they felt

Emotion: what they were experiencing

Speaker: who said what

That is much closer to an actionable business event than a raw transcript.

Gladia also provides sentiment, NER, topic detection, summaries, and other audio intelligence capabilities, and its current platform bundles these into its pricing.

The advantage of Shunya is the connection between this intelligence layer and a specialized ASR model family, followed by the broader voice stack.

Healthcare is another specialized workload

Healthcare transcription is a good example of why specialized models matter.

A clinical recording can contain:

Drug names

Dosages

Medical abbreviations

Procedures

Anatomical terminology

Diagnoses

Clinician names

Gladia provides a compliant enterprise platform and supports healthcare use cases within its broader audio intelligence offering. Its current compliance positioning includes HIPAA, GDPR, SOC 2 Type 2, and ISO 27001.

Shunya has a dedicated Zero STT Med model specifically designed around medical and clinical speech.

For healthcare buyers, the important benchmark isn’t whether the vendor says “medical.”

It is:

How many critical medical terms are transcribed correctly on our actual clinical audio?

Test drug names.

Test dosages.

Test abbreviations.

Test accents.

Test doctor-patient overlap.

Test the terminology from the specialties you actually serve.

Deployment and privacy

Gladia currently positions itself as an enterprise-grade platform with GDPR, HIPAA, SOC 2 Type 2, and ISO 27001coverage. It hosts primarily on European cloud infrastructure and offers US-based clusters for customers requiring US data residency. Gladia also says paid-tier audio is not used for model training.

Shunya’s broader enterprise platform supports cloud, private, on-premise, and edge-oriented architectures, with lightweight models available for on-device inference.

For enterprise deployments, this means the comparison should include:

Data residency

Retention

Encryption

Private deployment

Network requirements

Hardware requirements

Air-gapped environments

Model update process

Both platforms have strong enterprise positioning.

The right choice depends on where the speech must run and what level of infrastructure control your organization requires.

Where Gladia is the better choice

Gladia is particularly compelling when you want:

Broad multilingual audio

Solaria-1 supports 100+ languages, with native code-switching and real-time streaming.

Business-call transcription

Solaria-3 is specifically optimized for real-world business audio and Gladia reports strong results on English and European customer-call evaluations.

Fast real-time partials

Gladia reports under-103ms partial latency for Solaria-1 in its real-time testing.

Bundled intelligence

Diarization, sentiment, NER, translation, summarization, code-switching, and other capabilities are bundled into the base pricing.

Audio-to-LLM

One API call can combine transcription, diarization, and LLM analysis.

Competitive managed pricing

Growth pricing can reach $0.20/hour async and $0.25/hour real-time at volume.

For teams prioritizing these capabilities, Gladia is a strong platform.

Where Shunya makes a stronger case

Shunya becomes particularly compelling when:

Indian speech is a core workload

The Zero STT Indic model is designed for 55+ Indian languages and regional speech.

Code-switching is specifically Indian and conversational

Zero STT Codeswitch is designed around mixed-language use cases such as Hinglish and Tanglish.

The domain requires a dedicated model

Zero STT Med provides a specialized path for medical speech.

High concurrency is important

Shunya publishes 240+ concurrent streams per GPU.

You need a broader performance profile

Shunya publishes 3.10% composite WER, 146× real-time throughput, and sub-500ms streaming performance.

Speech needs to become intelligence

The speech intelligence layer extends ASR into intent, sentiment, emotion, diarization, and speaker intelligence.

You are building a broader voice-AI system

The Shunya Platform connects STT, TTS, voice agents, SLMs, edge speech understanding, and knowledge-grounded AI.

You need edge inference

On-device models provide a path toward lightweight local speech processing.

Shunya vs Gladia by use case

Use case	Stronger fit
Broad global multilingual transcription	Gladia / Both
English business-call transcription	Gladia Solaria-3
Real-time multilingual transcription	Gladia / Both
Indian-language contact centers	Shunya
Hinglish / Indic code-switching	Shunya
Global code-switching	Gladia
Medical speech	Both
High-concurrency voice systems	Shunya
Meeting transcription + bundled intelligence	Gladia
Audio-to-LLM workflow	Gladia
Transcript + intent / emotion / sentiment	Shunya
Edge speech inference	Shunya
Broad voice-AI platform	Shunya
All-inclusive audio intelligence pricing	Gladia
Specialized Indic speech models	Shunya
Cloud deployment	Both
Enterprise private deployment	Both, depending on requirements

How to benchmark Shunya and Gladia

A meaningful evaluation should start with your production audio.

Create a representative dataset with:

Clean recordings

Phone-quality calls

Background noise

Multiple speakers

Overlapping speech

Indian accents

Code-switched conversations

Domain terminology

Numbers and alphanumeric IDs

Then evaluate six things.

1. Transcription accuracy

Measure WER, but also track critical terms separately.

2. Business-critical entities

Test:

Names

Numbers

Account IDs

Product codes

Medical terms

Brand names

3. Language behavior

Test:

Language recognition

Code-switching

Regional accents

Low-resource languages

4. Real-time performance

Measure:

First partial

First token

Finalization

Transcript stability

End-to-end response time

5. Scale

Test at:

10 concurrent streams

100 concurrent streams

500 concurrent streams

and whatever peak load your system actually expects.

6. Business outcome

Measure whether the transcript produces the right:

Intent

Summary

Customer classification

Agent response

Workflow action

Voice-agent tool call

This last metric matters most.

The goal isn’t to produce a beautiful transcript.

The goal is to make the application work better.

The real choice: audio intelligence API or speech platform?

Gladia has made a strong case for simplifying the audio intelligence stack.

Its current proposition is essentially:

Send us audio.

We’ll transcribe it.

We’ll separate speakers.

We’ll identify entities.

We’ll detect sentiment.

We’ll summarize it.

We’ll translate it.

And we can feed it into an LLM.

That’s powerful.

Shunya takes a different position.

Start with the speech model that best fits the workload.

Then add:

Speech intelligence

TTS

Voice agents

SLMs

Knowledge grounding

Edge inference

This makes Shunya particularly interesting for organizations where speech is becoming a core interface to the product, rather than simply another source of data.

Final words

Gladia is a strong modern audio intelligence platform.

Its current strengths include 100+ languages, native code-switching, low-latency streaming, Solaria-3 for business audio, diarization, sentiment, NER, translation, summarization, custom vocabulary, Audio-to-LLM, and all-inclusive feature pricing.

For teams that want to move quickly from audio to structured information, Gladia is a compelling choice.

Shunya’s strongest argument is different.

It gives enterprises more specialized paths through the speech problem.

Zero STT for broad speech.

Zero STT Indic for Indian languages.

Zero STT Codeswitch for mixed-language conversations.

Zero STT Med for healthcare.

Then it connects those models to speech intelligence, voice agents, TTS, SLMs, edge models, and knowledge-grounded AI.

Shunya also publishes 3.10% composite WER, sub-500ms streaming performance, 146× real-time throughput, and 240+ concurrent streams per GPU.

That creates a strong proposition for enterprises where speech is itself a critical part of the application architecture.

The difference is easiest to see in real workloads.

For a global meeting assistant, Gladia’s bundled multilingual audio intelligence and Audio-to-LLM workflow can be extremely attractive.

For an Indian contact center, the combination of Indic speech, code-switching, concurrency, and speech intelligencecan make Shunya a stronger fit.

For a voice-agent platform, low latency, model specialization, speech intelligence, TTS, and the broader voice stackbecome much more important than language count.

For edge applications, local inference changes the architecture completely.

So don’t decide based on the feature list.

Take your hardest production recordings and run them through both platforms.

Measure:

Accuracy.

Critical terminology.

Code-switching.

Speaker attribution.

Latency.

Concurrency.

And ultimately:

Did the application make fewer mistakes?

That’s the metric that matters.

Frequently asked questions

Is Shunya better than Gladia?

It depends on the workload. Gladia is particularly strong for multilingual audio intelligence, bundled downstream features, real-time streaming, and Audio-to-LLM workflows. Shunya differentiates through specialized STT models, Indian-language depth, high concurrency, speech intelligence, and a broader voice-AI platform.

Which supports more languages?

Shunya currently supports 216+ languages, while Gladia’s current Solaria-1 offering supports 100+ languages.

Does Gladia support code-switching?

Yes. Gladia’s Solaria-1 supports native code-switching across its supported languages, including real-time use cases.

Does Shunya support code-switching?

Yes. Zero STT Codeswitch is designed specifically for mixed-language conversations such as Hinglish and Tanglish.

Does Gladia support diarization?

Yes. Gladia provides speaker diarization, including as part of its broader bundled audio intelligence offering.

Does Shunya support diarization?

Yes. Shunya includes speaker labels and diarization in its speech intelligence workflow.

Does Gladia support sentiment and named entity recognition?

Yes. Gladia includes sentiment analysis and NER among its bundled audio intelligence capabilities.

Does Shunya support intent and emotion?

Yes. Shunya’s speech intelligence layer supports intent, sentiment, emotion, diarization, and speaker intelligence.

Which is faster?

Both have strong real-time offerings. Gladia reports under-103ms partial latency for Solaria-1, while Shunya publishes sub-500ms first-token performance. These metrics measure different points in the pipeline, so the right comparison is a direct test under the same conditions.

What is Solaria-3?

Solaria-3 is Gladia’s newer speech model focused on noisy, conversational business audio. Gladia currently positions it for asynchronous workloads and reports strong results on English and core European-language customer calls.

Does Gladia provide Audio-to-LLM?

Yes. Gladia’s Audio-to-LLM combines transcription, diarization, and LLM analysis into a single API workflow.

Which is cheaper?

Gladia currently lists $0.61/hour async and $0.75/hour real-time on Starter, with Growth pricing as low as $0.20/hour async and $0.25/hour real-time at volume. Shunya Zero STT currently starts at $0.0039/minute, approximately $0.234/hour before volume discounts.

The exact total cost depends on the model, volume, intelligence features, and deployment requirements.

Which is better for Indian-language applications?

Both support multilingual speech, but Shunya has a more explicit Indic specialization strategy, including a dedicated model for 55+ Indian languages and another for code-switched speech.

Which is better for voice agents?

Both can support voice-agent systems. Gladia’s real-time streaming and Audio-to-LLM capabilities are strong, while Shunya connects STT, speech intelligence, TTS, voice agents, SLMs, and knowledge-grounded AI in one broader platform.

How should I compare Shunya and Gladia?

Use representative production audio and compare WER, critical terms, code-switching, diarization, latency, concurrency, and downstream task accuracy. Your own audio is more useful than a generic benchmark.