Comprehensive side-by-side LLM comparison
Gemini 2.5 Pro Preview 06-05 leads with 4.0% higher average benchmark score. Llama 4 Scout offers 18.9M more tokens in context window than Gemini 2.5 Pro Preview 06-05. Llama 4 Scout is $10.87 cheaper per million tokens. Llama 4 Scout is available on 6 providers. Both models have their strengths depending on your specific coding needs.
Gemini 2.5 Pro Preview 06-05 is a multimodal language model developed by Google. It achieves strong performance with an average score of 68.8% across 13 benchmarks. It excels particularly in Global-MMLU-Lite (89.2%), AIME 2025 (88.0%), FACTS Grounding (87.8%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.
Meta
Llama 4 Scout is a multimodal language model developed by Meta. It achieves strong performance with an average score of 67.3% across 12 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (90.6%), ChartQA (88.8%). With a 20.0M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 6 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Meta's latest advancement in AI technology.
2 months newer
Llama 4 Scout
Meta
2025-04-05
Gemini 2.5 Pro Preview 06-05
2025-06-05
Cost per million tokens (USD)
Gemini 2.5 Pro Preview 06-05
Llama 4 Scout
Context window and performance specifications
Average performance across 22 common benchmarks
Gemini 2.5 Pro Preview 06-05
Llama 4 Scout
Gemini 2.5 Pro Preview 06-05
2025-01-31
Available providers and their performance metrics
Gemini 2.5 Pro Preview 06-05
Llama 4 Scout
Gemini 2.5 Pro Preview 06-05
Llama 4 Scout
Gemini 2.5 Pro Preview 06-05
Llama 4 Scout
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