Llama 4 provides more specific and quantifiable data regarding its performance, API costs, and hardware requirements. GPT-6 makes broader claims of superiority without providing detailed metrics, making it difficult to directly compare the two models on many attributes. Llama 4 is better for users who need open-source fine-tuning and a large context window. GPT-6 is better for users who need superior translation and dialogue capabilities.
| Attribute | Llama 4 | GPT-6 |
|---|---|---|
| Context Window Length | Llama 4 Scout: 10 million tokens; Llama 4 Maverick: 1 million tokens | Not available |
| Finetuning Difficulty | Enables open-source fine-tuning by pre-training on 200 languages | Fine-tuning requires a deep understanding of machine learning concepts and the specific architecture of the GPT models. It also requires proficiency in selecting and adjusting hyperparameters and managing overfitting. |
| Out-of-the-Box Performance on Benchmarks | Llama 4 Scout: Better results than Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1. Llama 4 Maverick: Beats GPT-4o and Gemini 2.0 Flash, comparable to DeepSeek v3 on reasoning and coding. LiveCodeBench: 43.4 (Maverick). HumanEval: ~62% pass@1 (Maverick) | GPT-6 exceeds GPT-4 in every way and manages enormous amounts of data, resulting in the creation of more complex and nuanced text. |
| Multilingual Support | Supports multiple languages including Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Pre-trained on 200 languages, including over 100 with more than 1 billion tokens each. | GPT-6 excels at translating across languages and accurately translates between languages, making it effective for removing linguistic barriers. |
| Hallucination Rate | LLaMA 4 Maverick: 4.6%; LLaMA 4 Scout: 4.7% | Hallucination rates are dropping amongst almost all LLMs. OpenAI holds five of the lowest hallucination rates. |
| API Availability and Cost | Available on Cerebras via API access. Llama 4 Scout: $0.65 per million input tokens and $0.85 per million output tokens on Cerebras Inference cloud. | For GPT-5, the input price is $1.250/1M tokens, and the output is $10.000/1M tokens. For GPT-5 mini, the input price is $0.250/1M tokens, and the output is $2.000/1M tokens. |
| Community Support and Documentation | Documentation at [https://www.llama.com/docs/overview] | Not available |
| Inference Speed | Cerebras: Over 2,600 tokens per second (Llama 4 Scout). Blackwell B200 GPU: Over 40,000 tokens per second with NVIDIA-optimized FP8 version of Llama 4 Scout, over 30,000 tokens per second on Llama 4 Maverick. | Inference speed is influenced by model size. Smaller models usually run faster and can even outperform larger models when used correctly. |
| Memory Footprint | Llama 4 Scout: Designed to run on a single NVIDIA H100 GPU using INT4 quantization. ~55-60GB VRAM for weights (4-bit quantized), plus KV cache overhead. | Not available |
| Safety Measures and Guardrails | Data filtering and other data mitigations during pre-training; various techniques during post-training to ensure models conform to helpful and safe policies. | OpenAI implements a range of safety measures at every stage of the model's life cycle, from pre-training to deployment. Guardrails are guidelines and controls to steer an LLM's behavior and prevent undesirable outcomes. |
| Code Generation Capabilities | Understands and generates application code. On LiveCodeBench, Llama 4 Maverick scores 43.4. On HumanEval, Maverick achieves a pass@1 score of around 62%. | GPT-5 is the best model for coding and agentic tasks across industries. GPT-5 excels at producing high-quality code and handling tasks such as fixing bugs, editing code, and answering questions about complex codebases. |
| Ability to Handle Complex Reasoning Tasks | The models reason through complex science and math problems. | GPT-6 has an impressive level of skill in picking up on the complexities of current dialogues. |