Llama 4 offers a range of models with specific strengths in areas like multimodal reasoning and coding, along with robust safety measures and open-source fine-tuning capabilities. GPT-5, while still largely speculative, is expected to surpass its predecessors in reasoning and problem-solving, with a focus on improved accuracy and safety. The choice between the two depends on specific needs, with Llama 4 providing concrete options and GPT-5 promising future advancements.
| Attribute | Meta AI (Llama 4) | GPT-5 |
|---|---|---|
| Model Size (Number of Parameters) | Llama 4 Scout: 17 billion active parameters, 109 billion total parameters. Llama 4 Maverick: 17 billion active parameters, 400 billion total parameters. Llama 4 Behemoth: 288 billion active parameters, nearly 2 trillion total parameters. | Estimates vary widely, ranging from 200 billion to multiple trillions, with some speculating up to 17 trillion parameters. Some reports suggest the number of effective parameters may have plateaued. |
| Context Window Length | Llama 4 Scout: 10 million tokens. Llama 4 Maverick: 1 million tokens or 512,000 tokens. | Expected to have a significantly larger context window than its predecessors, potentially exceeding 1 million tokens. |
| Training Data Sources and Size | Trained on a mix of publicly available data, licensed data, and information from Meta's products and services, including posts from Instagram and Facebook, and interactions with Meta AI. Llama 4 Scout was pre-trained on approximately 40 trillion tokens. Llama 4 Maverick was pre-trained on approximately 22 trillion tokens. The pre-training data has a cutoff date of August 2024. | Expected to be extensive and diverse, potentially combining approximately 70 trillion tokens across 281 terabytes of data, including publicly available data, purchased datasets, and synthetic data. |
| Fine-tuning Capabilities and Customization Options | Enables open-source fine-tuning efforts by pre-training on 200 languages. Meta developed a new training technique called MetaP that allows reliable setting of critical model hyper-parameters such as per-layer learning rates and initialization scales. | Expected to offer greater control over the model's behavior and output, allowing developers to customize responses more effectively. |
| Multilingual Support (Number of Languages and Performance) | Pre-trained on 200 languages, including over 100 with over 1 billion tokens each. Supports 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Image understanding is currently limited to English. | Expected to support many languages, potentially with improved fluency and accuracy compared to previous models. However, performance may still vary slightly by language. |
| Reasoning and Problem-Solving Abilities | Llama 4 is designed to excel at multimodal reasoning, coding, and real-world problem-solving. Llama 4 Behemoth is focused on tasks that require high reasoning capacity and domain-specific knowledge, including mathematical problem-solving, scientific and engineering reasoning, and long-horizon decision-making across multimodal inputs. | Expected to incorporate a more advanced "Chain-of-Thought" reasoning process, allowing it to perform complex logical reasoning and solve multi-step problems. |
| Code Generation and Debugging Performance | Llama 4 Maverick matches advanced models in coding and reasoning tasks. | Expected to enhance code generation and debugging, making software development faster and more efficient. |
| Hallucination Rate and Factuality | Andri.ai reduces hallucinations through direct mapping of questions to verified citations. | Expected to significantly reduce hallucinations and improve structured problem-solving. |
| Safety Measures and Bias Mitigation Techniques | Includes data filtering, safety-specific tuning, red teaming, and bias mitigation. Utilizes Llama Guard, an input/output safety large language model based on the hazards taxonomy developed with MLCommons. Employs Prompt Guard, a classifier model trained on a large corpus of attacks, which is capable of detecting both explicitly malicious prompts (Jailbreaks) as well as prompts that contain inject inputs (Prompt Injections). | Has made strides in addressing bias and ensuring fairness in its outputs, including diverse training data, bias mitigation techniques, and continuous monitoring and evaluation. |
| API Availability and Pricing | There's no standalone API endpoint for Meta AI. | Expected to be available through an API, with pricing likely higher than GPT-4 initially. |
| Inference Speed and Hardware Requirements | Llama 4 Scout is designed to fit on a single H100 GPU. Llama 4 Maverick can be run on a single NVIDIA H100 DGX host. | Expected to be faster and more efficient than GPT-4. Training requires substantial computational resources, including high-end GPUs. |
| Community Support and Documentation Quality | Meta provides a Developer Use Guide: AI Protections, Llama Protections solutions, and other resources. | Not available |
| Price | Not available | Not available |
| Ratings | overall: Not available, performance: Llama 4 Maverick exceeds comparable models like GPT-4o and Gemini 2.0 on coding, reasoning, multilingual, long-context, and image benchmarks. It is competitive with DeepSeek v3.1 on coding and reasoning. Llama 4 Behemoth outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks. | Not available |
| Pros | Excels at multimodal reasoning, coding, and real-world problem-solving, Enables open-source fine-tuning efforts by pre-training on 200 languages, Pre-trained on 200 languages, Includes safety measures and bias mitigation techniques, Offers richer contextual understanding and improved responsiveness based on real-world interactions | Expected to surpass GPT-4o and GPT-o1 in complex reasoning tasks., May achieve higher accuracy in STEM fields., Includes built-in safety filters and moderation tools to help detect and prevent the generation of harmful or inappropriate content., Allows developers to integrate its capabilities into their applications. |
| Cons | Image understanding is currently limited to English, No standalone API endpoint, Commercial use restrictions apply if exceeding 700 million monthly active users |