Is Meta's Llama 4 the next hottest leap in AI Calling?

Meta’s Llama 4 herd has arrived — and in the pack are three models: two publicly released and one massive version still behind closed doors. In the AI space, this release has sparked conversations around improved intelligence, extended context windows, and greater efficiency. Some view it as a major step forward for conversational AI; others suggest it could play a pivotal role in transforming AI-powered voice systems.

So, how relevant is it for AI calling right now?

Here’s a practical look at where Llama 4 fits — and where it might fall short — in the context of real-time voice applications.

Behind the New Llama 4 Models

Meta’s Llama 4 family consists of:

• Llama 4 Scout: A lightweight model, comparable to GPT-4o-mini and Claude 3.5 Haiku.
• Llama 4 Maverick: Touted as the best multimodal model in its class, beating GPT-4o and Gemini 2.0 across a range of benchmarks.
• Llama 4 Behemoth: A massive model with nearly 2 trillion parameters, reportedly outperforming GPT-4.5 and Claude 3.7 in STEM benchmarks.

This "herd of models" approach mirrors what we’ve seen from Anthropic with the Claude family (Haiku, Sonnet, Opus). And early results show strong gains in both performance and efficiency.

What’s new and impressive is that both Scout and Maverick come equipped with a 10 million token context window — far exceeding GPT-4o’s 128K. On paper, they’re remarkable. But how do they perform in the context of AI calling?

A Dive into Llama 4 Results

The Llama 4 models, especially Maverick, are showing top-tier performance across several benchmarks:

Multimodal Tasks

• Llama 4 Maverick outperforms GPT-4o and Gemini 2.0 Flash
• Multimodal Multitask Benchmark (MMMU): Maverick scores 73.4%, beating GPT-4o (69.1%) and Gemini (71.7%)

Code Generation

• Llama 4 Maverick excels, scoring 77.6% on the Massive Multitask Programming Benchmark (MBPP)
• Live Code Benchmark: Maverick (43.4%) surpasses GPT-4o (32.3%).

Reasoning and Knowledge

• Multitask Language Understanding Benchmark (MMLU Pro): Maverick scores 80.5%, outperforming Gemini (77.6).
• Mathematics Benchmark (MATH): Maverick (61.2%) beats Llama 3.1 (53.5%).

Long Context Tasks

• Multitask Text Benchmark (MTOB): Llama 4 processes full books, unlike competitors.

Cost Efficiency

• ‍Token Cost: Llama 4 Maverick costs just $0.49 per million tokens, compared to GPT-4o’s $4.38 — nearly 9x cheaper.

LMArena Standing

• As of 07/04/2025, Llama 4 Maverick is second on the LMArena leaderboard with a very impressive Elo of 1417

How does Llama perform in the Context of AI Calling?

To answer that, let’s break down how AI calling works today. There are three main components:

1. Speech-to-Text (STT): Converts spoken audio into text.
2. Natural Language Processing (NLP): The "brains" of the system, interpreting and reasoning through each interaction.
3. Text-to-Speech (TTS): Converts the AI’s response back into lifelike audio.

These elements need to run in real time — with minimal latency — to create a natural, human-like call experience. While speech-to-speech models do exist (like OpenAI’s real-time system), their current limitations in intelligence and consistency prevent widespread, confident deployment.

The Llama 4 models primarily enhance the NLP layer, but here's the key question: what do we need most from an AI caller’s brain? Speed.

And unfortunately, this is where Llama 4 Scout and Maverick fall behind.

Compared to GPT-4o's Output speed, its almost half - which is drastic - especially in the context of AI calling where each millisecond counts, and a few extra milliseconds of latency can feel like a full second of silence on the phone.

If Speed is crucial in AI Calling, why not use an even smaller base model?

To us, a phone call might seem straightforward: build rapport, shift to purpose, take notes. But from the AI’s perspective, there’s a complex stack of tools running in real time — and lightweight models just can’t keep up. This is where GPT-4o hits the sweet spot — fast enough for fluid conversation, yet intelligent enough to handle real-world logic and nuance.

Where Llama 4 Could Still Be Useful

Although not ideal for real-time conversation, Llama 4 could add value in other parts of the AI calling stack:

• Post-call summarisation: For detailed, long-context analysis after a call ends (e.g. extracting insights, QA).
• Smart routing or escalation: Enhancing decision-making behind the scenes, like triaging calls or routing based on context.
• Training and simulation: High-quality training agents for reps or fine-tuning conversational flows.

With it's affordable intelligence this hidden applications in AI Calling definitely benefit from a strong model like Llama 4.

Final Thoughts: Llama 4 and the Future of AI Calling

Llama 4 represents a technical leap — just not in the area AI calling needs most today. For use cases requiring long-form reasoning or document analysis, it’s a top contender.

But when it comes to voice AI, speed, latency, and speech integration are the name of the game — and Llama 4 still lags.

That said, it’s a strong signal of what’s coming. As large language models continue to get smarter and cheaper, keep an eye out for fast, lightweight versions from Meta that could soon hit the AI calling sweet spot.