Choosing the right voice AI models is one of the most important steps in building a smooth and reliable Voice AI agent. At Bolna, every agent is powered by three main systems. The first is Automatic Speech Recognition (ASR), which listens to the user and converts their speech into text. The second is a Large Language Model (LLM), which interprets that text and decides what the agent should say next. The third is Text-to-Speech (TTS), which turns the response back into spoken audio.
Different model providers perform better at different things. Some are very fast, some are more accurate, and some are cheaper to run. There is no single model that is perfect across all these areas, which is why picking the right combination matters.
To make this easier, we look at three practical factors: latency, price, and quality. This article explains what each of these means in real conversations and how they help you choose the right model setup for your specific use case.
Table of Contents
How Bolna Benchmarks Models
Before comparing different voice AI models, it is important to understand how we evaluate them. Bolna’s approach to ASR TTS benchmarking focuses on real-world performance rather than controlled or synthetic tests. This ensures that the results you see reflect how the models behave during actual customer conversations.
- Real call data: We benchmark models using thousands of live calls made through Bolna across different regions in India. This helps us measure how well they handle natural speech, varying accents, and unpredictable caller behavior.
- Noise and environment: Many callers speak from noisy surroundings such as markets, homes, or traffic. We test how models perform under these conditions to understand their noise-handling ability in real usage.
- Language and code-mixing: Indian users frequently switch between English, Hindi, and regional languages. Our evaluations include these mixed-language scenarios to provide an accurate LLM comparison for multilingual and code-mixed conversations.
- Latency and cost under load: We also measure model speed and cost at different call volumes. This helps us determine how efficient each model is in large-scale deployments and how well it maintains performance when multiple calls happen simultaneously.
This benchmarking approach ensures that the comparisons in the next sections reflect real conversational needs and help you pick a model setup that performs reliably in production.
Understanding the Trifecta: Latency vs Price vs Quality
Selecting the right voice AI models becomes much easier once you understand the three factors that matter most. Instead of thinking in terms of “best model,” it’s more useful to see every decision through the lens of the latency vs price vs quality trade-off. What these three factors mean
1. Latency: How quickly the agent responds. Affects conversational flow, ability to interrupt, and how human the interaction feels.
2. Price: How much it costs to run the model at scale. Important for high-volume use cases like reminders, surveys, and notifications.
3. Quality: How accurate, natural, and intelligent the model is. This includes transcription accuracy, reasoning ability, and voice expressiveness.
Every model provider prioritizes these three factors differently. A fast model may not be the most accurate. A high-quality model may cost more. A low-cost model may introduce delays. By treating latency, price, and quality as a single framework rather than separate considerations, you can choose a combination that matches your exact use case instead of relying on generic benchmarks.
Latency
Latency refers to the time it takes for the system to generate a response after the user finishes speaking. It is one of the clearest signals of conversational quality because people instantly notice delays. When latency is low, the interaction feels smooth and natural. When it is high, the conversation feels mechanical and disconnected. This is why latency is often the first thing to check when selecting a voice AI model.
In real-time conversations such as sales, lead qualification or customer support, users expect the agent to react almost instantly. A fast system allows the agent to acknowledge intent quickly, handle interruptions gracefully and maintain a natural rhythm. In these situations, the best AI model for voice bots is usually the one that delivers the quickest, most consistent response times.
Typical latency expectations are straightforward:
• Under 500 milliseconds feels immediate and natural
• Between 700 milliseconds and 1 second is still acceptable but noticeably slower
• Above 1 second begins to feel robotic and works better for simple tasks like reminders or information reads
Latency differs significantly across providers, which makes it a major factor in model selection. At Bolna, we automatically choose faster models for high engagement conversations and use slower, more cost-efficient options for workflows where timing is less critical.
Price
Price becomes a major factor when your system handles a large number of calls. The total cost is shaped by both usage minutes and token consumption, so choosing a cost efficient voice AI setup can have a big impact on long-term operating costs. Models that are optimized for affordability usually do one of the following:
• Offer lower token rates
• Use fewer tokens to produce the same response
This makes them ideal for high-volume, low-complexity tasks where the agent does not need deep reasoning or long answers. Common examples include:
• Reminders
• Appointment confirmations
• Delivery updates
• Short surveys
These workflows do not require advanced intelligence, which means models with better AI model pricing perform just as well as larger, more expensive ones. Sample messages in this category are:
• Your payment is due tomorrow
• Your appointment is confirmed for 5 PM
The key idea is simple: for structured conversations, smaller and cheaper models often deliver the same user experience while costing far less. This allows you to scale confidently without unnecessary spend.
Quality and What It Means for Accuracy and Naturalness
Quality affects how reliable and natural your agent feels. It covers how well the system hears the user, how intelligently it interprets the meaning, and how human the response sounds. Strong voice AI quality ensures that users feel understood, guided, and engaged throughout the conversation.
a) ASR (Transcriber) Quality
ASR quality focuses on how accurately speech is transcribed. This is where ASR TTS benchmarking becomes important because different models behave very differently in real-world audio conditions. Key elements to evaluate include:
• Noise handling and the ability to filter out background sounds
• Word Error Rate and how closely the transcription matches what was actually spoken
High ASR quality is essential in use cases such as recruitment, customer service and KYC, where every word matters and mistakes can change the meaning of the conversation.
b) LLM (Language Model) Quality
LLM quality reflects how intelligently the system interprets user input and decides what to say next. A strong language model understands intent, handles context and follows the flow of a conversation naturally. Good LLM comparison looks beyond accuracy and considers how well the model adapts in real interactions. Important aspects to evaluate include:
• The ability to reason about what the user wants
• Understanding context across multiple turns
• Handling multilingual or mixed language sentences
High quality LLMs are essential for intelligent voice AI tasks such as interviews, complex sales conversations and detailed product explanations, where clear reasoning matters more than speed.
c) TTS (Text-to-Speech) Quality
TTS quality determines how natural and human the agent sounds during a conversation. A strong TTS engine creates a smooth, expressive voice that matches the tone of the interaction. This is where a solid TTS quality benchmark becomes important, because the difference between robotic and natural delivery directly affects user trust and engagement.
Key elements to consider include:
• Tonality and the ability to express emotion
• Control over speaking speed and pacing
• Overall expressiveness and how well the voice follows a conversational rhythm
High quality TTS is essential for natural voice AI use cases such as sales conversations, brand-driven interactions and customer onboarding, where the agent’s tone plays a direct role in user comfort and conversion.
Model by Model Comparison
This section provides an AI model comparison across the most widely used voice AI models, based on Bolna’s call data from October 2025. Each table highlights strengths, limitations and the scenarios where each model works best.
ASR Models
| Provider | Strengths | Limitations | Ideal Use |
|---|---|---|---|
| Deepgram | Very fast responses, strong noise handling, solid English and Hindi accuracy | Limited support for South Indian languages | Sales calls, lead qualification, customer support |
| Azure Speech | Low cost, good multilingual range, steady latency | Slightly weaker on code-mixed content | Large scale reminders and information calls |
| Sarvam (India Multi) | Excellent accuracy for Indian languages, strong accent coverage | Higher latency and heavier compute needs | Recruitment workflows and vernacular support |
LLM Models
| Model | Strengths | Limitations | Ideal Use |
|---|---|---|---|
| GPT-4.1 Mini | Strong reasoning, good speed, reliable output | Slightly higher cost | General purpose reasoning agents |
| GPT-4o Mini | Cost effective, fast, handles context well | Less depth in nuanced reasoning | High volume calling use cases |
| GPT-4.1 / Gemini 1.5 | Best reasoning and context tracking | Higher cost and slower responses | Interviews, complex support, escalations |
TTS Models
| Provider | Strengths | Limitations | Ideal Use |
|---|---|---|---|
| ElevenLabs | Natural voices, low latency, expressive delivery | Limited vernacular languages beyond Tamil | Most conversational agents |
| Rime | Strong emotional range, pitch and tone control | Slightly slower output | Sales, lead qualification, storytelling |
| Sarvam | Natural Indian accents, wide language coverage | Higher latency | Vernacular campaigns and regional outreach |
| Azure TTS | Very affordable and quick | Slightly robotic tone | Large scale outbound notifications |
Example Use Case Mapping
Different situations require different strengths from your models. Understanding how each component behaves in real conversations makes it easier to match the right setup with the right task.
Recruitment screening focuses heavily on accuracy. Calls often involve varied accents, detailed answers and important information, so high quality ASR and a reasoning capable LLM matter most. A strong TTS engine helps maintain a professional tone Sales calls, lead qualification and similar AI call workflows rely on speed and expressiveness. The agent must respond quickly, adapt to interruptions and sound confident. Low latency models with natural TTS delivery create smoother interactions.
For payment reminders, appointment confirmations and delivery notifications, the priority is cost efficiency. These interactions follow a predictable structure and do not require detailed reasoning, so lightweight models offer the best balance of performance and price.
Customer support calls benefit from a combination of good reasoning and reliability. The agent often needs to understand context, handle follow up questions and maintain clarity throughout longer conversations. Vernacular or regional campaigns work best with models that handle accents and multilingual input naturally. ASR and TTS engines trained on Indian languages deliver a smoother user experience for these situations.
Summary Table of Bolna’s Recommendations
This table provides quick voice AI recommendations based on common priorities. Each category highlights the best model setup depending on whether you value overall performance, speed, cost or quality.
| Category | Best Overall | Best for Latency | Best for Cost | Best for Quality |
|---|---|---|---|---|
| ASR | Azure | Deepgram | Azure | Sarvam |
| LLM | GPT 4.1 Mini | GPT 4o Mini | GPT 4o Mini | GPT 4.1 or Gemini |
| TTS | ElevenLabs | Azure | Azure | Rime or Sarvam |
The “Best Overall’’ column represents the most balanced option across different use cases. The latency focused column prioritizes the quickest response times. The cost focused column highlights the most affordable choices. The quality focused column lists the strongest options for accuracy, reasoning and naturalness.
Conclusion
Choosing models for your Voice AI agent is not about finding one best option. It is about matching the model to the job. Latency matters in sales and support, price matters in large volume reminders, and quality matters in interviews or complex conversations.
Bolna lets you combine the strengths of multiple providers in one workflow, pairing fast ASR with strong reasoning and natural TTS wherever needed. This keeps your calls smooth, cost efficient and human sounding.
You can refine and customize your model setup directly on the Bolna platform.
Frequently Asked Questions
How do I choose the right models for my Voice AI agent?
Start by identifying what matters most for your use case: speed, cost or quality. Different tasks require different priorities, and the best results come from matching model strengths to the type of conversation.
Can I use different providers for ASR, LLM and TTS?
Yes. Bolna supports mixing providers within a single workflow, allowing you to combine fast ASR, strong reasoning and natural TTS for the best overall experience.
Are low-cost models good enough for real calls?
For short, predictable conversations such as reminders or confirmations, low-cost models perform very well. You only need higher-quality models for complex, open-ended interactions.
