Llama 4 vs. GPT-4o: A Detailed Comparison

Overview

Llama 4: A suite of open-weight natively multimodal models.
GPT-4o: An autoregressive omni model accepting any combination of text, audio, image, and video as input and generating any combination of text, audio, and image outputs.

Model Architecture

Llama 4: Uses a mixture of experts (MoE) architecture.
- Example: Llama 4 Maverick has 17B active parameters and 400B total parameters with 128 routed experts and a shared expert.
GPT-4o: Trained end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network.

Multimodality

Llama 4: Designed with native multimodality, incorporating early fusion to seamlessly integrate text and vision tokens into a unified model backbone.
GPT-4o: Accepts and generates any combination of text, audio, image, and video.

Context Length

Performance

Llama 4 Maverick: Best-in-class performance to cost ratio, exceeding comparable models like GPT-4o and Gemini 2.0 on coding, reasoning, multilingual, long-context, and image benchmarks.
Llama 4 Behemoth: Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM-focused benchmarks such as MATH-500 and GPQA Diamond.
GPT-4o: Matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50% cheaper in the API. Especially better at vision and audio understanding compared to existing models.

Llama 4 Maverick

Strengths:
- Best-in-class multimodal model.
- Excels in coding, reasoning, multilingual tasks, long context handling, and image understanding.
- Offers a superior performance-to-cost ratio compared to Llama 3.
- Competitive with DeepSeek v3.1 on coding and reasoning.
Architecture:
- 17 billion active parameters.
- 128 experts.
- 400 billion total parameters.
Use Cases:
- General assistant and chat use cases.
- Precise image understanding.
- Creative writing.

Llama 4 Scout

Strengths:
- Industry-leading context length of 10 million tokens.
- Best-in-class image grounding.
- Strong performance in coding, reasoning, and long context tasks.
- Outperforms all previous Llama models.
Architecture:
- 17 billion active parameters.
- 16 experts.
- 109 billion total parameters.
Key Innovations:
- Interleaved attention layers without positional embeddings (iRoPE architecture).
- Inference time temperature scaling of attention.
Use Cases:
- Multi-document summarization.
- Parsing extensive user activity for personalized tasks.
- Reasoning over vast codebases.

Llama 4 Behemoth

Strengths:
- Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks (MATH-500, GPQA Diamond).
- Serves as a teacher model for smaller Llama 4 models.
Architecture:
- 288 billion active parameters.
- 16 experts.
- Nearly two trillion total parameters.
Training:
- Codistillation to improve quality of smaller models.
- Novel distillation loss function.
- Large-scale reinforcement learning (RL) with hard prompt sampling.

GPT-4o

Strengths:
- Omni model: Accepts and generates any combination of text, audio, image, and video.
- End-to-end training across text, vision, and audio.
- Fast response time (similar to human conversation).
- Matches GPT-4 Turbo performance on English text and code.
- Significant improvement on non-English text.
- Faster and cheaper than GPT-4 Turbo in the API.
- Superior vision and audio understanding.
Data and Training:
- Pre-trained on data up to October 2023.
- Publicly available data (web crawls, machine learning datasets).
- Proprietary data from data partnerships (e.g., Shutterstock).
Safety and Mitigation:
- Moderation API and safety classifiers for filtering harmful content.
- Data filtering to reduce personal information.
- Opt-out mechanism for images in training data.

Training Data

Llama 4: Pre-trained on 200 languages, including over 100 with over 1 billion tokens each, and overall 10x more multilingual tokens than Llama 3. The overall data mixture for training consisted of more than 30 trillion tokens, which is more than double the Llama 3 pre-training mixture and includes diverse text, image, and video datasets.
GPT-4o: Text and voice capabilities were pre-trained using data up to October 2023, sourced from a wide variety of materials including select publicly available data and proprietary data from data partnerships.

Post-training

Llama 4: Revamped post-training pipeline by adopting a different approach: lightweight supervised fine-tuning (SFT) > online reinforcement learning (RL) > lightweight direct preference optimization (DPO).
GPT-4o: Aligned to human preferences during post-training; red-teamed the resulting models and add product-level mitigations such as monitoring and enforcement; and provide moderation tools and transparency reports to users.

Availability

Llama 4: Available for download on llama.com and Hugging Face.
GPT-4o: Available via API and in Meta AI in WhatsApp, Messenger, Instagram Direct, and on the Meta.AI website.

Safety Measures

Llama 4: Built with the best practices outlined in the Developer Use Guide: AI Protections. This includes integrating mitigations at each layer of model development from pre-training to post-training to tunable system-level mitigations that shield developers from adversarial users.
GPT-4o: Assesses and mitigates potential risks that may stem from generative models, such as information harms, bias and discrimination, or other content that violates usage policies. Uses Moderation API and safety classifiers to filter out data that could contribute to harmful content or information hazards.

Voice Capabilities

Llama 4: No specific details on voice capabilities in the provided document.
GPT-4o: Can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in a conversation.

Use Cases

Llama 4: Adding next-generation intelligence to products, general assistant and chat use cases, precise image understanding and creative writing, multi-document summarization, parsing extensive user activity for personalized tasks, and reasoning over vast codebases.
GPT-4o: Not explicitly detailed in the provided document, but implied to be broad given its omni-model nature.

Llama 4 Use Cases

Llama 4 Scout

Llama 4 Maverick

GPT-4o Use Cases

Broad societal impacts (health and medical applications, economic impacts)
Scientific research and advancement
General-purpose tasks involving text, audio, image, and video inputs and outputs

GPT-4o Specific Applications

GPT-4o in Underrepresented Languages

Improved reading comprehension and reasoning in languages like Amharic, Hausa, Northern Sotho (Sepedi), Swahili, and Yoruba.