The large language model sits at the heart of every modern voice agent. Speech-to-text and text-to-speech handle the audio, but the LLM fundamentally determines how intelligent, helpful, and natural your agent feels to users.
And voice agents have a unique challenge: they require LLMs to reason while the user is still talking, and respond within milliseconds of a pause. The wrong choice leads to streaming delays, interrupted inputs, overly verbose agents, and even hallucinations.
This guide compares three of the most widely used models—GPT-4 (OpenAI), Claude (Anthropic), and LLaMA (Meta)—from the perspective of backend engineers and system architects. It helps you identify the best live deployment behavior for your streaming agentic system.
Key takeaways
In text applications, the right AI depends primarily on output quality metrics: accuracy on benchmarks, reasoning capabilities, or knowledge breadth. But voice agents introduce an entirely different set of challenges.
Because voice interactions happen in real time, performance isn't just about intelligence. It's equally about speed, stability, and streaming behavior.
Consider a voice agent that takes 2-3 seconds to respond after a user stops speaking. Even if the response is perfectly crafted and highly accurate, users find the system slow or unintelligent. Similarly, an LLM that produces excellent final outputs but struggles with streaming creates choppy, unnatural interactions.
Key differences between the top models matter, and you need to know how to choose the best one for your voice agent tool.
As a voice AI builder, you’re not just looking for “the smartest” LLM. You’re designing for speed, predictability, and conversation fluidity.
Here are the key dimensions to compare:
End-to-end response latency is the most critical performance metric for voice agents. This includes not just the time from prompt submission to first token, but the entire journey from a user completing their speech to the agent initiating audio playback.
Two key factors to consider:
As we’ll see, latency time differences can be quite stark, even between the three leading LLMs.
Voice agents rely on streaming LLM responses to maintain the flow of conversations. Rather than waiting, text-to-speech systems begin synthesizing audio as soon as the first words become available.
But streaming quality varies significantly across different models and API providers. Some systems produce smooth, consistent token streams, while others emit tokens in bursts or with irregular timing. The resulting audio can sound unnatural or confusing.
Models must be able to generate well-structured responses from the very first token, not just produce coherent content eventually.
Voice agents need to break down complex requests into multiple steps, maintain context across conversation turns, and coordinate various actions—then explain their reasoning to users.
A common (but complex) request might be: "find me a restaurant, check if they take reservations, and add it to my calendar." This requires the LLM to understand the task sequence, execute steps in logical order, and maintain context about intermediate results.
Voice conversations build on prior context. Models that struggle with long-term memory create frustrating user experiences where agents "forget" important details.
Real conversations include interruptions, corrections, and topic changes that don't occur in structured text interactions. Voice agents must handle these dynamics gracefully without losing coherence or context.
Does the model degrade gracefully when cut off mid-prompt? Can you restart or redirect a response without starting again from scratch?
Some models can adapt to new information smoothly, while others produce confused or contradictory outputs when their initial assumptions are challenged. Avoid forcing users to restart conversations or repeat information unnecessarily.
Despite major improvements, LLMs are still famous for hallucinations. And the real-time pressure of voice interactions can exacerbate these tendencies. Users often provide partial, ambiguous, or unclear inputs that challenge model reasoning capabilities.
Models should acknowledge uncertainty rather than generating confident-sounding but incorrect responses. Particularly in high-stakes domains like healthcare or finance.
Voice agents increasingly need to support multiple languages and handle code-switching, informal speech patterns, and cultural context variations.
Some models excel in major languages but struggle with regional dialects. Voice applications often need to handle informal speech, slang, and mixed-language inputs that don't appear in training data.
Tone and formality are also important and can vary by language or region. Models must understand not just what users say, but how they expect to be addressed in response.
Conversations can be long. Models need to retain context across extended interactions, while managing token limits efficiently. What’s the maximum size before an interaction truncates or degrades?
Agents that keep long conversational histories, RAG memory, or knowledge grounding need very large context windows—often more than 100K tokens. And some models maintain coherence even when approaching context limits, while others degrade significantly as windows fill up.
Voice agents often operate in mission-critical scenarios where downtime or performance degradation directly impacts business operations or user safety. And rate-limiting policies vary dramatically across providers and can significantly impact voice agent performance.
OpenAI's rate limits based on requests per minute may constrain high-concurrency voice applications, while Anthropic's token-based limits affect different usage patterns. Understanding these constraints early prevents deployment surprises.
Uptime guarantees are critical for voice agents in production environments. Enterprise SLAs from providers like Azure OpenAI offer contractual reliability commitments that may justify their higher costs.
This final point certainly cannot be overlooked. LLMs typically rely on per-token pricing (often per thousand tokens).
Clearly, you need to know what you’re expected to pay for ongoing use. And consider the cost implications as your tool grows in usage.
Let’s look now at the three large language models used most often in voice agent tools. We’ll consider each in light of the factors we’ve just laid out above.
Best for: deep reasoning, formal tone, and multi-step planning; compliance-sensitive environments (healthcare, finance, legal assistance); use cases where response quality outweighs sub-second speed.
OpenAI’s GPT-4 (especially the GPT-4-turbo variant) is widely regarded as the most capable general-purpose LLM in terms of reasoning, instruction-following, and consistency. It’s also known for its safety, both in reducing hallucinations and in meeting compliance standards.
But in voice agent settings, its latency and streaming behavior require careful orchestration. Product teams should carefully consider the potential bottlenecks that come with slower streaming.
Strengths:
Challenges:
Tips for voice agent deployment:
Best for: Conversational agents with low-latency expectations and frequent interruptions; multi-language use cases.
Anthropic’s Claude family (especially Claude 3 Opus and Sonnet) offers strong performance with a noticeable emphasis on fast, human-like response flow. In live agents, Claude often feels more natural due to quicker token output and better behavior in open-ended dialog.
Architectural decisions seem specifically designed to address real-time interaction challenges. Customer service agents, real-time translation systems, or interactive entertainment applications that prioritize speed often perform better with Claude than with alternatives.
Compared with GPT-4, Claude may not be as suitable for very complex queries or long task chains. But it’s a great choice for fast, predictable customer interactions where the risks aren’t high.
Strengths:
Challenges:
Tips for voice agent deployment:
Best for: Teams that own infrastructure and want to optimize for cost, latency, and fine-tuning; use cases where streaming speed is more important than absolute model IQ.
Meta’s LLaMA models (especially LLaMA 3 70B and smaller variants) are open-weight LLMs that offer fast inference and full deployment control. While not as capable out of the box as Claude or GPT-4, they’re highly tunable and cost-effective at scale.
This gives you more control and customization, but also creates operational complexity for your engineers.
Strengths:
Challenges:
Tips for voice agent deployment:
Choosing the right LLM is just one piece of the voice agent puzzle. Real-time performance and reliability depend heavily on how the model is orchestrated within the larger system.
Even the best model can underperform if downstream processes aren’t optimized for concurrency, error handling, and contextual grounding.
Here are a few system-level considerations:
Voice agents must manage multiple processes in parallel: transcription, LLM calls, TTS generation, and possibly external API queries. Latency creeps in quickly when these components run sequentially or block on slow completions.
Even the best models need strong scaffolding. This includes system prompts, formatting constraints, and fallback strategies when uncertainty is high.
Voice agent quality doesn’t just depend on LLM outputs. What’s more important is how those outputs synchronize with transcription inputs, to be delivered with clarity.
A well-integrated stack makes even mid-tier models feel smarter, while a poorly coordinated one can make top models sound robotic or inconsistent.
As usage grows, so can the costs of LLMs in your voice agent tool. So you either need to choose low-cost options, or find smart strategies to keep token usage to a minimum.
The voice AI landscape is moving fast. And the LLMs powering applications are evolving.
GPT-4 is the benchmark for complex reasoning, safety, and multi-turn conversations. It’s the preferred choice for knowledge-intensive applications, compliance-sensitive environments, and scenarios where response quality outweighs latency concerns.
Claude is the speed champion for voice applications, delivering consistently fast streaming responses and handling large context windows effectively. Meanwhile open-weight models like LLaMA provide unmatched control and customization potential, but come with significantly increased operational complexity.
In short, there’s no one-size-fits-all choice. For teams building production-grade voice agents, the winning choice is a system where that model shines for your specific buyers.
And it also requires the right inputs, which is where Gladia excels. We give you best-in-class, real-time speech-to-text to fuel your chosen LLM.
Gladia helps thousands of companies build better voice agents and call center tools. Get started for free or book a demo to see it in action.
Speech-To-Text
Real-time voice AI agents are among the most demanding applications in modern software engineering. Unlike traditional request-response systems, where latency can be measured in hundreds of milliseconds, voice agents must maintain the illusion of natural human conversation.
Speech-To-Text
The large language model sits at the heart of every modern voice agent. Speech-to-text and text-to-speech handle the audio, but the LLM fundamentally determines how intelligent, helpful, and natural your agent feels to users.
Speech-To-Text
The promise of voice AI for customer service is compelling. We now have intelligent agents that can handle inquiries, update records in real-time, and provide instant access to critical business data.
