AI Voice Agents: Choosing the Right LLM

Choosing an LLM for AI Voice Agents

With a growing number of LLMs available - ranging from proprietary models like OpenAI’s GPT-4o and Anthropic 3.7 Sonnet to open-source alternatives such as Meta’s LLaMA 3.3 - businesses must carefully evaluate their options. Factors like response latency, throughput, cost per token, hosting flexibility, and functional capabilities all play a crucial role in determining the best-fit model for a given use case.

What Are Large Language Models (LLMs), and Why Are They Important for Voice AI?

Large Language Models (LLMs) are advanced neural networks trained on massive amounts of text data, enabling them to process, understand, and generate human-like responses in natural language. These models leverage deep learning architectures, such as transformers, to predict text based on input prompts, making them incredibly versatile for various AI-driven applications.

In the context of voice AI, LLMs play a fundamental role in ensuring smooth, intelligent, and context-aware conversations. Unlike traditional voice assistants that rely on predefined scripts or rigid rule-based systems, LLM-powered AI voice agents can:

• Comprehend context and intent
• Generate human-like responses
• Follow complex instructions
• Handle dynamic, real-time interactions
• Support multilingual communication

Why Are LLMs Critical for Voice AI?

AI voice agents must process and generate responses within milliseconds to maintain a seamless real-time conversation. 

TTFT (Time to First Token) measures how long it takes for an AI model to generate the first symbol of its response after receiving a query. MMLU (Massive Multitask Language Understanding). MMLU is a benchmark that evaluates an AI model’s ability to understand and answer complex questions across multiple subjects, including math, law, medicine, and general knowledge.

The choice of LLM directly impacts:

• Response speed (latency) - Faster models, like Gemini 2.0 Flash (0.5s TTFT) and GPT-4o-Mini (0.7s TTFT), allow near-instant interactions​.
• Accuracy and coherence - A high MMLU score (e.g., 78% for Claude 3.7 Sonnet) ensures the model can handle complex queries with logical consistency​.
• Cost-effectiveness - Businesses processing millions of voice interactions monthly need cost-efficient models like Gemini Flash ($0.10 per million input tokens) vs. GPT-4o ($2.50 per million input tokens)​.

Key Challenges in Selecting an LLM for AI Voice Agents and Their Business Impact

Choosing the right large language model (LLM) for an AI voice agent is a strategic decision that directly affects customer experience, operational costs, and scalability. Unlike traditional chatbots, voice agents require real-time processing, seamless dialogue management, and accurate responses, making the selection process complex. Below, we explore the most critical challenges and their direct impact on business operations.

Demystifying LLM Selection: The Key Metrics That Matter

1. Latency & Performance Metrics

For voice assistants, responsiveness is critical. Latency directly affects the conversational flow - a slow response can feel unnatural or frustrating to users​. We focus on Time to First Token (TTFT) and Tokens per Second (TPS) (generation throughput). All the models support streaming output, meaning they can start speaking before the full answer is generated, which is essential for real-time voice. 

Business impact of latency

In real-world deployments, lower latency has direct benefits for user engagement and efficiency. Users are more likely to continue interacting when responses are prompt - a delay beyond about one second can start to feel awkward, and indeed studies show that delays over 1 second can frustrate users in voice interactions​ . Especially in customer-facing scenarios (e.g. a support hotline), shaving even a second off response times can yield measurable improvements in satisfaction. 

For instance, one industry report found that a one-minute increase in average call handle time leads to a 10% drop in customer satisfaction scores​ . While our focus is on seconds or fractions of a second per response, it all adds up: faster AI agents resolve queries quicker, reducing overall call duration and customer wait times. This can also translate to operational cost savings - if an AI agent can handle calls 10 - 20% faster thanks to low latency, it can potentially handle more calls in the same time or free up human agents sooner, improving contact center throughput. In summary, investing in low-latency LLMs or infrastructure pays off in smoother, more natural conversations and happier customers.

2. Accuracy & Coherence Metrics

We assess each model’s accuracy, knowledge depth, and coherence using standardized benchmarks: MMLU, GPQA, and IFEval. These metrics gauge how well the LLM can handle complex questions and follow instructions:

• MMLU (Massive Multitask Language Understanding) evaluates the model on 57 diverse subjects (from history to science exams). It’s a proxy for world knowledge and reasoning ability. Higher MMLU (%) means the model answers more questions correctly across these topics, indicating broad expertise.
• GPQA (Graduate-Level Google-Proof Q&A) presents extremely challenging questions (often college or grad-level problems in sciences) that aren’t easily solved by memorization or a quick web search​. This tests the model’s advanced reasoning and problem-solving.
• IFEval (Instruction-Following Evaluation) measures how well the model follows complex instructions and produces the desired output format. This covers understanding user intent, adhering to requested formats, and coherence in following multi-step directions.

The most reliable models - Gemini 2.0 Pro, Claude 3.5/3.7 Sonnet, and GPT-4o - score the highest in benchmarks, with MMLU scores around 80%, meaning they perform at nearly expert levels in understanding and answering complex questions. These models also follow instructions well, with Claude leading in structured and precise responses.

Smaller, faster models like GPT-4o-mini and Claude 3.5 Haiku trade accuracy for speed and cost efficiency. While they perform well for everyday questions, they may struggle with specialized knowledge or complex reasoning. This makes them suitable for basic customer support but less ideal for industries requiring high precision, such as finance or healthcare.

Business Impact: Why Accuracy Matters

• Finance: Mistakes in AI-generated advice on loans, interest rates, or transactions can lead to compliance issues and financial losses. Banks typically use high-accuracy models (Claude Sonnet, GPT-4o, Gemini Pro) and validate responses with real-time data sources or human review.
• Healthcare: AI in medical support must be highly reliable. Even the best models (~80% accuracy) can still make errors, so they should be used to assist, not replace human professionals. A voice agent might draft an answer, but a curated medical database or human expert should verify before providing final information.

In summary, accuracy and coherence metrics inform which model to choose based on the complexity of queries in your use case. If your voice agent is answering simple FAQs or doing basic tasks, a fast model like Gemini 2.5 Flash, GPT-4o-mini or Claude Haiku may suffice and be more cost-effective. But if it’s expected to handle highly technical or sensitive queries (like medical or financial advice), investing in a top-tier model (Claude Sonnet, Gemini Pro, GPT-4o) is wise - and even then, using system instructions and human oversight to mitigate the remaining error rate. The goal is to maximize correct, contextually appropriate answers while minimizing any hallucinations or policy violations.

3. Cost Analysis

Pricing per million tokens: Each model has different pricing, especially the proprietary ones offered via API. Table 3 summarizes the API usage costs (in USD per 1 million tokens processed). “Input” refers to prompt tokens and “output” refers to generated tokens. For reference, 1 million tokens is roughly 750k words (about 3,000-4,000 pages of text).

Context window impact or input length

The context window determines how much conversation history or documents the model can consider at once. Larger context is a double-edged sword: it enables more sophisticated use cases (feeding entire knowledge bases, long dialogs, etc.), but it can dramatically increase token consumption (and thus cost) if you always stuff the maximum context. On the flip side, if your voice agent needs to handle, say, a long customer call with hundreds of exchanges, models like GPT-4o (128k) or Claude (100k/200k) can maintain far more context of the conversation than a model limited to 8k tokens (which might equate to only a few pages of text or a few minutes of dialogue). This means fewer instances where the AI has to say, “I’m sorry, I forgot what we discussed earlier.”

API vs. Self-Hosting: Which is More Cost-Effective?

Using a managed API (OpenAI, Anthropic, Google, etc.) means you pay per token as above, which is convenient and scales automatically. Self-hosting an LLM involves running it on your own servers or cloud instances, incurring infrastructure costs but no direct token fees. The cost trade-off depends on usage volume:

• For low to moderate usage, APIs are often cheaper and easier (you don’t pay for idle time, and don’t need MLOps engineers to maintain the model). There’s also no large up-front investment.
• For very high usage, self-hosting can save money in the long run. But the general point stands: at large scale, owning the means of generation can be more cost-efficient.
• There’s a middle ground: using cloud infrastructure but on a rental basis (e.g. spinning up your own instances on AWS/GCP to run LLaMA). In this case, you’re paying for GPU hours. If you keep the GPUs busy close to 24/7 with generation, the effective token cost can approach the theoretical hardware cost. If the GPUs sit idle much of the time, then you’re better off sticking to API where you only pay for what you use.

Another consideration is rate limits and scaling. Many API providers have request quotas. For example, OpenAI’s GPT-4o has tiered limits up to 10,000 requests per minute and 30M tokens per minute for top enterprise plans​. These are quite high, but a large call center or voice assistant platform needs to be mindful of them. 

In summary, API pricing for these models ranges from ultra-cheap (~$0.1 per million tokens) to quite expensive ($10+ per million). Generally, cost tracks with capability: the more advanced models and larger contexts cost more to use. Businesses should calculate expected token usage per interaction (for a voice agent, consider how many tokens a typical user query + the AI response will consume) and multiply by volume to estimate monthly costs for each model. Sometimes a higher-priced but more accurate model can actually be cost-saving if it solves problems in fewer turns or requires less back-and-forth. In other cases, a fast, cheaper model might handle 90% of queries, and the expensive model is only invoked for the hardest 10% - this can be a very cost-effective strategy.

4. Deployment Factors: Cloud vs. Self-Hosted, Security & Scalability

When choosing an LLM for enterprise, it’s not just about model quality - deployment considerations like infrastructure, data security, compliance, and scalability are equally important.

Cloud-based APIs (OpenAI, Anthropic, Google, etc.):

• Pros: Easiest to integrate (simple API calls), no ML ops burden, and providers optimize the model’s performance for you. They also handle scaling - if your voice agent’s call volume spikes, the cloud service can accommodate (within your rate limit) by allocating more compute. Updates and improvements to the model are delivered automatically.
• Cons: Ongoing cost per use, potential data privacy concerns (since user queries are sent to a third-party server), and dependence on the provider’s uptime and policies. While major providers have strong security, some organizations are uneasy sending sensitive data off-site. Compliance requirements can be a barrier - for example, a healthcare company may be legally restricted from using a cloud AI unless certain certifications are in place​. There’s also less flexibility: you can’t customize the model beyond what the API allows.

Self-hosting (on-prem or private cloud):

• Pros: Full control over data (nothing leaves your servers, which aids privacy and regulatory compliance)​, and potentially lower marginal cost at scale as discussed. You can also customize the stack - for instance, run real-time voice ASR (speech recognition) and the LLM on the same machine to minimize latency, or fine-tune the model on proprietary data. It also allows using open-source models that aren’t available via API. Data residency and sovereignty concerns are alleviated since you decide where the system runs (important for EU GDPR, which requires controlling cross-border data flow; self-hosting lets you keep data in-country).
• Cons: You now assume responsibility for operations and security. An open-source model server is like any other sensitive system - if misconfigured, it could leak data or be attacked​. Maintaining uptime, applying model updates, and scaling the system are non-trivial tasks requiring skilled engineers. There is also the hardware cost and maintenance - running a fleet of GPUs or specialized AI accelerators. If your usage is sporadic or low-volume, those resources might sit idle (still costing money). And while open models give freedom, they might not reach the absolute performance of the best proprietary models yet; there’s often a quality gap to consider.

Security & Compliance

All major cloud LLM providers have taken steps to alleviate data privacy concerns. OpenAI, Google, and Anthropic state that API data is not used to train their models (unlike consumer-facing free services)​. OpenAI even offers a “zero data retention” mode for enterprises where they don’t store API prompts at all​. Microsoft Azure OpenAI service will sign a BAA (Business Associate Agreement) for HIPAA compliance in healthcare and ensures data is siloed to specific regions​. These measures mean using a closed model via API can meet strict requirements, but it relies on trusting the vendor and legal safeguards. Some organizations, especially in finance and government, still prefer that sensitive data never leaves their own infrastructure - hence a tilt toward open-source models they can deploy internally​.

Scalability

Cloud APIs abstract this - you just need to watch your rate limits. For high-throughput scenarios, you may have to request higher quotas or pay for enterprise tiers (as shown in Table 3, some go very high). Self-hosting requires scaling out infrastructure. The good news is LLM workloads scale horizontally - if you need to handle N concurrent calls, you can run N (or fewer, if each can handle multiple threads) instances of the model. Tools like Kubernetes or auto-scaling groups in cloud can spin up more instances when load increases. The latency difference is that cloud API calls might go to geographically load-balanced servers, whereas if you self-host in one region, global users might experience more network latency (unless you deploy servers in multiple regions). For a voice agent, this is usually minor compared to generation time.

Fine-tuning and Customization

Many providers now allow limited fine-tuning of these models. For example, OpenAI allows fine-tuning GPT-4o (with some restrictions) and GPT-4o-mini. Anthropic does not yet allow fine-tuning Claude 3, but AWS Bedrock has introduced a feature to fine-tune select models including Claude (with guardrails)​. Google’s Vertex may allow fine-tuning smaller PaLM or GeMMI models, but Gemini 2.0 fine-tuning hasn’t been announced (it supports tool augmentation instead). 

Open-source LLaMA can be fine-tuned freely on your data, which is a big plus if you need the model to learn domain-specific terminology or style (e.g., fine-tuning LLaMA 3.3 on your company’s past support transcripts to better handle industry-specific vocabulary)​. Fine-tuning a closed model via API means you are sending your custom dataset to the provider - one should check that this data is not absorbed into the base model beyond your use (typically it isn’t - it results in a separate model instance just for you.

Tool Integration and Function Calling

Many voice agents need the LLM to interface with external systems (booking appointments, fetching account info, etc.). Models like Gemini and GPT-4o support function calling out of the box. Claude, via AWS Bedrock, also supports a form of function calling or “JSON mode” to output structured data. LLaMA can be made to do this with fine-tuning or by using frameworks like LangChain that parse its output. Grok-2 was noted as not supporting structured outputs natively.

If your voice agent needs to reliably return data in a particular format for downstream systems, this capability is important. It might tilt you toward models that explicitly advertise function calling (GPT-4o family, Gemini, etc.). However, even those that don’t natively have it can often be prompted to do so with high reliability if their IFEval (instruction following) score is good - for example, Claude with a well-crafted prompt can output JSON consistently (Anthropic even demonstrated tools usage via their “Constitutional AI” approach in experiments).

Implementation Strategies for AI Voice Agents

Now that we’ve analyzed the key performance metrics, costs, and business impact of different LLMs, the next step is to focus on how to effectively implement AI voice agents using these models. Successful deployment requires careful consideration of model selection, performance optimization, system integration, security, and continuous improvement.

Selecting the Right LLM for Your Use Case

Choosing the right model depends on business goals, response time requirements, accuracy needs, and budget constraints.

• For fast and cost-efficient customer interactions, models like Claude 3.5 Haiku, GPT-4o-mini, or Gemini 2.0 Flash are effective. They provide quick responses and lower operational costs, making them ideal for FAQ-based support, appointment scheduling, or general inquiries.
• For more complex interactions that require precise and well-reasoned responses, models like Claude 3.7 Sonnet, GPT-4o, and Gemini 2.0 Pro are better suited. These models handle legal, financial, or technical inquiries, ensuring that responses are accurate and contextually relevant.
• Some businesses take a hybrid approach, where a lower-cost model handles standard inquiries, while a high-end model is used selectively for critical or complex requests. This balances cost and performance while ensuring the AI agent delivers the best possible responses when needed.

Integrating AI Voice Agents with Business Systems

For AI voice agents to be truly effective, they must seamlessly integrate with existing business systems. This includes customer databases, CRMs, and support ticketing platforms.

• CRM integration allows AI to retrieve customer history and personalize responses, improving engagement.
• ERP and order management systems enable AI to check order status, process refunds, or update customer records in real-time.
• Function calling and API integration let AI trigger automated actions, such as scheduling appointments or fetching account details.

Models like GPT-4o, Gemini 2.0, and Claude 3.7 Sonnet natively support function calling, making them well-suited for structured automation. For models that don’t have built-in function calling, structured output formatting techniques can still enable integration with external systems.

For voice-based AI, it’s also critical to choose the right Speech-to-Text (STT) and Text-to-Speech (TTS) solutions. High-quality transcription ensures the AI correctly interprets user requests, while a natural-sounding TTS system enhances the user experience.

Discover the best STT & TTS technologies for AI voice agents in our in-depth guide!

Measuring Success and Continuous Improvement

AI voice agents require ongoing optimization to maintain high-quality interactions. Businesses should track key performance indicators (KPIs) to evaluate effectiveness:

• Accuracy and coherence - how well the AI understands and responds to inquiries.
• Response time - measuring delays between user input and AI-generated responses.
• Customer satisfaction - evaluating feedback to determine if users find AI interactions helpful.
• First-call resolution rate - analyzing how many queries are resolved without escalation to human agents.

To improve performance over time, businesses should continuously monitor AI-generated interactions, analyze customer feedback, and refine AI responses. This might involve updating prompts, fine-tuning models, or introducing new automation workflows based on observed usage patterns.

Key Considerations for a Successful AI Voice Agent Deployment

1. Align model selection with business needs - fast models for simple tasks, highly accurate models for complex interactions.
2. Optimize token usage - use only the necessary context to control costs and speed up responses.
3. Ensure seamless system integration - connect AI voice agents with internal databases, CRMs, and APIs to enable automated workflows.
4. Prioritize security and compliance - ensure that sensitive customer data is handled according to regulatory requirements.
5. Monitor, measure, and refine AI performance - use real-time analytics and customer feedback to improve AI interactions over time.

A well-implemented AI voice agent reduces operational costs, enhances customer engagement, and improves efficiency across various industries. By following these strategies, businesses can ensure their AI deployments are both scalable and cost-effective while maintaining a high standard of user experience.

Use Cases: How AI Voice Agents Drive Business Impact

1. AI Voice Agents in Customer Support & Call Centers

Automated Customer Service for Common Inquiries

Companies receive thousands of repetitive support requests daily - order status, password resets, appointment scheduling. Handling these manually is expensive and inefficient.

An AI voice agent can handle routine customer inquiries instantly, reducing wait times and freeing human agents for complex cases. Claude 3.5 Haiku or GPT-4o-mini can efficiently answer FAQs, while more advanced models like GPT-4o or Claude 3.7 Sonnet manage complex queries.

AI-Driven Call Routing & Escalation

Many customers are transferred multiple times before reaching the right department, leading to frustration.

An AI-powered voice agent can analyze a caller’s intent in real time and route them directly to the correct department or agent. It can also summarize the issue before transferring, saving agent time.

AI Voice Agents in Finance & Banking

AI-Powered Banking Assistants

Banks handle millions of inquiries about balances, transactions, loan applications, and card activations. Managing these with human agents is costly.

AI voice agents using Gemini 2.0 Pro, GPT-4o, or Claude 3.7 Sonnet can provide real-time account information securely while ensuring compliance with financial regulations.

Fraud Detection & Customer Verification

Fraud prevention teams need to verify unusual transactions quickly to prevent financial losses.

AI-powered voice agents can automatically call customers to verify flagged transactions, ask security questions, and escalate suspicious cases to human fraud teams.

AI Voice Agents in Healthcare & Telemedicine

AI-Powered Medical Appointment Scheduling

Hospitals and clinics struggle with scheduling inefficiencies and missed appointments. AI voice agents can schedule, reschedule, and send reminders for medical appointments using Claude 3.7 Sonnet or GPT-4o, reducing administrative workload.

AI-Assisted Patient Triage & Symptom Checking

Patients often call clinics with non-urgent concerns that take up valuable staff time. AI voice agents can guide patients through symptom checks using predefined medical protocols, directing them to emergency care if necessary.

AI Voice Agents in Retail & E-Commerce

AI-Powered Order Tracking & Customer Support

Retailers receive thousands of inquiries about order status, shipping updates, and refunds, often leading to long wait times. AI voice agents can automatically track orders, process refunds, and handle returns, reducing human workload.

AI Voice Shopping Assistants

Customers increasingly shop using voice assistants but often struggle with limited functionality. AI-powered voice assistants using Gemini 2.0 Flash or GPT-4o can provide personalized shopping recommendations, process orders, and suggest complementary products.

AI Voice Agents in Travel & Hospitality

AI-Driven Hotel Concierge Services

Hotels receive numerous guest inquiries about room service, check-in/out, and amenities. AI voice agents can handle routine guest requests, take room service orders, and provide travel recommendations, improving guest experience.

AI-Powered Travel Booking Assistants

Customers expect fast and convenient travel booking options. AI-powered voice assistants can search flights, book hotels, and make itinerary changes in real-time using function-calling capabilities.

AI Voice Agents in Logistics & Supply Chain

AI-Powered Fleet & Delivery Management

Logistics companies need real-time updates on fleet and package tracking. AI voice agents can provide real-time tracking updates, reroute deliveries, and communicate with drivers efficiently.

AI for Warehouse Operations

Warehouses often deal with manual inventory checks and order processing inefficiencies. AI-powered voice agents can guide workers through picking and packing processes, reducing errors and improving speed.

Not Sure Where to Start? Here’s Your AI Voice Agent Roadmap

If you’re considering AI voice agents but aren’t sure how to begin, you’re not alone. The key to a successful implementation is starting small, testing results, and scaling efficiently.

Here’s a simple roadmap to guide your business through the process:

1. Define Your Use Case - Identify where AI can add the most value (customer support, sales, finance, etc.).
2. Choose the Right LLM - Match your needs with models that balance speed, accuracy, and cost.
3. Integrate with Your Systems - Connect AI with your CRM, ticketing platform, or database for seamless automation.
4. Optimize for Performance - Reduce latency, improve accuracy, and track performance metrics.
5. Test & Scale - Start with a pilot, refine your approach, and expand AI adoption based on real results.

Want a step-by-step guide to deploying AI voice agents in your business?

Softcery provides expert consulting and AI-powered solutions to help you build, integrate, and optimize AI voice technology for real-world applications. Whether you need recommendations on the best LLM, custom integrations, or a full AI voice strategy, we’ve got you covered.

Which LLM Should You Choose for Your AI Voice Agent in 2025?

Whether you prioritize real-time responsiveness, enterprise-grade accuracy, or cost-effective self-hosting, the right choice depends on your specific business needs. Let’s summarize the best models for different use cases and help you make an informed decision.

The Future of AI Voice Agents

The AI voice agent landscape is evolving rapidly, with LLMs becoming faster, more accurate, and more cost-effective. Businesses must carefully balance performance, scalability, and cost when selecting a model.

• For Real-Time AI - GPT-4o-Mini and Gemini 2.0 Flash are currently the best choices, offering low latency and fast responses, making them ideal for call centers, customer support, and live interactions.
• For Enterprise & Complex Workflows - GPT-4o, Gemini 2.0 Pro, and Claude 3.7 Sonnet provide high accuracy and structured output capabilities, making them suitable for finance, healthcare, and regulated industries.

For Cost Efficiency & Self-Hosting - LLaMA 3.3 is a strong option for businesses looking to reduce API costs while maintaining control over data privacy and performance. While it requires infrastructure investment, it can significantly cut long-term operational expenses.

As AI voice technology advances, businesses that strategically integrate these solutions will gain a competitive advantage in automation, efficiency, and customer experience. The key is to start with clear goals, test AI performance, and scale based on measurable success.