Deepgram is one of the strongest speech-to-text platforms for developers building real-time voice applications. Its current Nova-3 models focus on low-latency transcription, multilingual speech, background noise, crosstalk and far-field audio, while Flux is designed specifically for conversational voice agents.

That makes Deepgram a serious choice for production speech.

Shunya takes a different approach.

Shunya Zero STT is built around 216+ languages, 55+ Indian languages, sub-500ms first-token positioning, speaker diarization and speech intelligence, with specialized models for Indian, code-switched and medical speech.

The important question isn’t simply which platform is faster.

It’s:

Which speech-to-text platform gives you the right combination of accuracy, language coverage, real-time performance and production features for your workload?

Shunya and Deepgram: at a glance

Both platforms are built for production speech, but their strongest areas are different.

Aspect	Shunya Zero STT	Deepgram
Platform	Enterprise speech-to-text platform	Speech AI platform
Main ASR models	Zero STT Universal, Indic, Codeswitch, Med	Nova-3, Flux and other models
Deployment	Cloud and enterprise private options	Managed cloud; self-hosted options for some enterprise workloads
Pricing	From $0.0039/min	Nova-3 from $0.0043/min pre-recorded; streaming from $0.0048/min monolingual
Key strength	Multilingual speech, Indic depth, specialized models and speech intelligence	Low latency, streaming and voice applications
Language coverage	216+	45+ for Nova models
Indian languages	55+	Model dependent
Real-time	Sub-500ms first-token positioning	Low-latency streaming
Speaker diarization	Supported	Supported
Language detection	Supported	Supported
Custom vocabulary	Supported	Keyterm Prompting
Code-switching	Dedicated model	Supported through multilingual/voice models
Speech intelligence	Intent, sentiment, emotion and more	Speech intelligence add-ons / voice platform
Medical ASR	Zero STT Med	Domain-specific / custom options
Best fit	Enterprise multilingual speech	Real-time voice and speech applications

Deepgram currently lists Nova-3 as its highest-performing general model, with multilingual support, automatic language detection, speaker diarization, Smart Formatting and Keyterm Prompting. Flux is positioned specifically for real-time voice agents.

Deepgram is built around real-time speech

Deepgram’s biggest strength is simple:

speed.

Its speech recognition stack is designed for applications where audio is arriving continuously and the system needs to respond quickly.

Nova-3 is positioned for production speech with support for:

• Streaming
• Pre-recorded transcription
• Speaker diarization
• Smart Formatting
• Keyterm Prompting
• Automatic Language Detection

Deepgram’s Flux models go further, with turn detection and interruption handling specifically designed for conversational voice agents.

Shunya also provides real-time speech recognition, but puts more emphasis on the broader multilingual and specialized speech layer.

That makes the platforms different in emphasis:

Deepgram is heavily optimized around real-time speech applications.

Shunya puts greater emphasis on multilingual enterprise speech and specialized model paths.

Which is more accurate?

There is no single Word Error Rate (WER) that answers this for every application.

Accuracy changes with:

• Language
• Accent
• Audio quality
• Noise
• Crosstalk
• Domain terminology
• Recording environment

Deepgram currently describes Nova-3 as its highest-performing general-purpose model and recommends it for challenging audio with multiple languages, background noise, crosstalk and far-field input.

Shunya currently publishes a 3.10% composite WER in English across eight OpenASR benchmarks.

Benchmark	Shunya Zero STT
LibriSpeech Clean	0.71%
SPGISpeech	1.10%
TED-LIUM	1.43%
LibriSpeech Other	2.17%
AMI	4.19%
VoxPopuli	4.34%
GigaSpeech	4.99%
Earnings22	5.83%
Composite	3.10%

These are Shunya benchmark results.

Deepgram also publishes comparative benchmarks, but benchmark setups, datasets and model versions differ, so comparing one company’s headline WER directly with another company’s published number can be misleading.

The better approach is simple:

Use your own audio.

The feature difference is about what sits around the transcript

Deepgram already provides a strong set of production speech capabilities.

Feature	Shunya	Deepgram
Batch transcription	✓	✓
Real-time streaming	✓	✓
Speaker diarization	✓	✓
Automatic language detection	✓	✓
Word timestamps	✓	✓
Smart formatting	✓	✓
Custom terminology	✓	✓ Keyterm Prompting
Code-switching	Dedicated model	Multilingual models
Indian-language specialization	Dedicated Indic model	Model dependent
Sentiment	✓	✓ add-on / model dependent
Intent	✓	✓ add-on / model dependent
Emotion	✓	✓ add-on / model dependent
Medical speech	Zero STT Med	Custom/domain options
Voice-agent support	✓	✓ Flux + Voice Agent stack
Private deployment	Enterprise options	Enterprise/self-hosted options

Deepgram’s current platform includes speech-to-text plus speech intelligence capabilities, while its Voice Agent platform combines STT, LLM and TTS for conversational applications.

So the difference isn’t that one platform has all the features and the other doesn’t.

It’s how those capabilities are packaged and which workloads they are optimized for.

Real-time is a strength for both

Real-time transcription is one of Deepgram’s strongest areas.

Nova-3 supports streaming, while Flux is specifically designed for conversational speech with turn detection and interruption handling.

Shunya also provides streaming recognition and publicly positions Zero STT at sub-500ms first-token latency.

So don’t stop at:

Streaming: ✓

Measure:

First-result latency

Final latency

Partial transcript quality

Endpointing

Concurrent streams

Accuracy under background noise

For a voice agent, these numbers matter far more than a generic “real-time” label.

Indian languages are where specialization matters

Deepgram’s current Nova platform supports 45+ languages, with automatic language detection and multilingual models.

Shunya currently publishes 216+ language coverage and 55+ Indian languages through Zero STT Indic.

The difference becomes more meaningful when your users speak:

• Hindi
• Tamil
• Telugu
• Bengali
• Marathi
• Malayalam
• Gujarati
• Hinglish
• Other regional varieties

Shunya’s Zero STT Indic model is specifically designed around Indian languages and dialects rather than treating Indic speech as one part of a general multilingual model.

For an Indian voice application, that specialization can matter.

Code-switching changes the problem

Consider:

“Mera card block ho gaya, can you help me activate it?”

The speaker isn’t choosing one language.

They’re switching naturally.

That creates a different ASR problem from standard multilingual transcription.

Shunya offers a dedicated Zero STT Codeswitch model for mixed-language speech such as Hinglish and Tanglish.

Deepgram also supports multilingual workloads, including multilingual models and voice-agent capabilities.

The useful question is therefore:

Which model performs better on your actual code-switched conversations?

For Indian applications where mixed-language speech is common, testing this directly can be more informative than comparing overall language counts.

Keyterm Prompting vs specialized speech models

Deepgram’s Keyterm Prompting is one of its important customization features.

It lets applications provide terms they want the recognizer to pay particular attention to, which can be useful for:

• Product names
• Company names
• Industry terminology
• Technical vocabulary
• Proper nouns

Deepgram currently documents Keyterm Prompting for Nova-3 and multilingual use cases.

Shunya approaches terminology and domain specialization through custom terminology handling plus dedicated models such as Zero STT Med and Zero STT Codeswitch.

The distinction is:

Keyterm Prompting helps a general model recognize important words.

A specialized model is designed around a particular speech problem.

Both approaches can be useful.

Which one works better depends on the workload.

The hidden cost isn’t just API pricing

At first glance, Deepgram and Shunya have relatively straightforward usage-based pricing.

Deepgram currently lists Nova-3 pre-recorded transcription at $0.0043/min on Pay As You Go for monolingual usage and $0.0052/min for multilingual. Its current streaming promotional rates are listed separately by model.

Shunya lists Zero STT from $0.0039/min, with separate rates for Indic, Codeswitch and Med.

That puts the base transcription pricing in a similar range.

So cost isn’t simply:

Deepgram vs Shunya per minute

The real calculation is:

model + streaming/batch + add-ons + intelligence + concurrency + monthly volume

Deepgram also prices speech intelligence capabilities separately depending on the feature, while Shunya includes a broader set of speech intelligence capabilities within its speech platform.

For a production application, compare the complete workload, not just the base transcription rate.

Deepgram is particularly strong for voice agents

This deserves its own section.

Deepgram’s Flux models are explicitly designed for conversational speech recognition and provide:

• Turn detection
• Interruption handling
• Ultra-low latency
• Multilingual conversation support

Deepgram also offers a broader Voice Agent platform combining speech recognition, LLM and TTS.

For teams building voice agents as their primary application, that’s a significant advantage.

Shunya approaches voice agents from a broader multilingual enterprise perspective, with STT, TTS, Voice Agents, SLMs, Edge SLU and Knowledge Graph capabilities across its platform.

So the choice becomes more specific:

Deepgram is particularly strong when real-time voice interaction is the centre of the product.

Shunya is particularly strong when multilingual enterprise speech and specialized speech workloads are central to the product.

Where Deepgram can be the better choice

Deepgram can be the better fit when:

Real-time voice is the priority

You need low-latency streaming and conversational turn handling.

You’re building voice agents

Flux and the broader Deepgram Voice Agent stack are designed around conversational applications.

You need strong developer tooling

Deepgram is built around API-first speech infrastructure and developer workflows.

You need Keyterm Prompting

You want lightweight runtime customization for specific terminology.

Challenging audio is common

Your application deals with background noise, crosstalk or far-field speech.

When Shunya makes more sense

Shunya becomes more compelling when:

Indian speech is central to the application

You need 55+ Indian languages through a dedicated Indic model.

You need broad multilingual coverage

Zero STT currently publishes 216+ languages.

Code-switching is common

Your users regularly mix Indian languages and English.

You need specialized speech models

Healthcare and other domain-specific speech workloads need dedicated model paths.

Speech intelligence is part of the workflow

Intent, sentiment, emotion and speaker information need to sit alongside transcription.

You want a broader enterprise speech stack

STT can connect into TTS, voice agents, SLMs, Edge SLU and Knowledge Graph capabilities.

Shunya and Deepgram by use case

Use case	Better fit	Why
Real-time voice agent	Deepgram	Flux is purpose-built for conversational speech
Voice-agent stack	Deepgram	Integrated Voice Agent platform
Challenging noisy/far-field audio	Deepgram	Nova-3 specifically positioned for these conditions
Keyterm prompting	Deepgram	Dedicated Keyterm Prompting
General production ASR	Both	Both provide managed production APIs
Indian-language application	Shunya	55+ Indian languages + Indic model
Hinglish application	Shunya	Dedicated code-switching model
Broad multilingual coverage	Shunya	216+ languages
Medical speech	Shunya	Dedicated Zero STT Med
Speech intelligence	Shunya	Intent, sentiment, emotion and related capabilities
Private enterprise workloads	Both	Enterprise deployment options
Speech-focused enterprise stack	Shunya	STT + TTS + Voice Agents + SLMs + Knowledge Graph

Can you use Deepgram and Shunya together?

Yes.

A multi-provider architecture can make sense when workloads have different requirements.

For example:

Deepgram

→ real-time voice agents
→ turn detection
→ conversational streaming

Shunya

→ Indian languages
→ code-switching
→ specialized speech models
→ speech intelligence
→ multilingual enterprise workloads

The right architecture doesn’t always require one provider to handle every speech workload.

How should you evaluate them?

Use your own production audio.

Test:

Your languages

Your accents

Your background noise

Your terminology

Your code-switching

Your speakers

Your concurrency

Then measure:

Metric	What to test
WER	Overall transcription
Entity accuracy	Names, brands and terminology
Number accuracy	Dates, amounts and identifiers
Code-switch accuracy	Mixed-language conversations
Diarization	Speaker attribution
First-result latency	Responsiveness
Final latency	End-to-end speed
Concurrent streams	Production capacity
Cost per hour	Actual usage economics
Downstream accuracy	Intent and application performance

The best ASR platform is the one that performs reliably on your audio at the economics your product can sustain.

Final words

Deepgram is a strong speech-to-text platform built around real-time applications.

Nova-3 provides multilingual transcription, streaming, speaker diarization, automatic language detection, Smart Formatting and Keyterm Prompting, while Flux is designed specifically for conversational voice agents.

For teams building real-time voice applications, those capabilities are a major advantage.

Shunya takes a different approach.

Zero STT combines 216+ languages, 55+ Indian languages, sub-500ms first-token positioning, 240+ concurrent streams per GPU and speech intelligence, with dedicated models for Indian, code-switched and medical speech.

The difference isn’t simply about which platform has the lower latency.

It’s about what kind of speech workload you’re building.

Deepgram is particularly strong for real-time conversational speech.

Shunya is particularly strong for multilingual enterprise speech, Indian languages and specialized speech workloads.

For voice-agent-first products, Deepgram can be the natural choice.

For applications where Indian languages, code-switching, specialized models and speech intelligence are central, Shunya can be the better fit.

The right question isn’t:

“Which ASR API is better?”

It’s:

“Which speech platform performs best on the languages, audio and workflows my product actually depends on?”

Frequently asked questions

Is Shunya better than Deepgram?

It depends on the workload. Deepgram is particularly strong for real-time speech and voice agents. Shunya is particularly differentiated around Indian-language speech, multilingual coverage, code-switching and specialized speech models.

How many languages does Deepgram support?

Deepgram currently lists 45+ languages across its Nova models, with coverage varying by model.

How many languages does Shunya support?

Shunya currently markets Zero STT across 216+ languages, with 55+ Indian languages through Zero STT Indic.

Does Deepgram support Indian languages?

Yes, but language availability depends on the specific Deepgram model. Deepgram’s current multilingual offerings cover a range of global languages.

Does Shunya support Indian languages?

Yes. Zero STT Indic supports 55+ Indian languages.

Does Deepgram support speaker diarization?

Yes. Speaker diarization is available with supported Deepgram speech models.

Does Shunya support speaker diarization?

Yes. Speaker diarization is part of Zero STT’s published capabilities.

Does Deepgram support real-time transcription?

Yes. Nova-3 supports streaming, and Deepgram’s Flux models are specifically built for real-time conversational speech.

Does Shunya support real-time transcription?

Yes. Shunya provides streaming recognition and publicly positions Zero STT around sub-500ms first-token latency.

What is Deepgram Keyterm Prompting?

Keyterm Prompting lets developers provide important words or phrases that Deepgram should pay particular attention to during recognition.

Does Shunya support code-switching?

Yes. Shunya provides a dedicated Zero STT Codeswitch model for mixed-language speech such as Hinglish and Tanglish.

Which is better for voice agents?

Deepgram is particularly strong for voice-agent applications because Flux is built around conversational turn-taking, interruption handling and low-latency speech recognition.

Which is better for Indian voice applications?

Shunya can be the stronger fit when Indian-language depth and code-switching are central because of its dedicated Indic and Codeswitch models.

How much does Deepgram cost?

Deepgram’s current Nova-3 pre-recorded pricing starts at $0.0043/min on Pay As You Go for monolingual transcription. Streaming and multilingual models have separate rates.

How much does Shunya STT cost?

Shunya currently lists Zero STT from $0.0039/min for batch transcription, with separate rates for specialized models.

Does Deepgram support custom speech models?

Yes. Deepgram offers custom speech-to-text models for scenarios requiring customer-specific training and edge-case accuracy.

Can I use Deepgram and Shunya together?

Yes. Different workloads can use different providers depending on language, latency, deployment and application requirements.