Gemini 1.5 Pro
A multimodal large language model released by Google that supports an extended context window of up to one million tokens. It is suited for processing complex tasks involving very long documents, videos, and other high-volume inputs.
Core Facts
The context window in the GPT-3 era was only 2,048 tokens, whereas Google Gemini already supports millions of tokens, Claude supports 200K tokens, and GPT-4 Turbo supports 128K tokens.
Google Gemini 1.5 Pro supports a context window of up to 1 million tokens and has native multimodal capabilities.
The context window of Gemini 1.5 Pro has been extended to 1 million tokens, equivalent to approximately 750,000 English words.
Timeline (last 90 days)
DataBugBench testing covers five models: Claude 3.5 Sonnet, GPT-4o, Gemini 1.5 Pro, Qwen 2.5 Coder 32B, and Llama 3.3 70B.
When handling complex data ownership nodes, Gemini 1.5 Pro frequently uses unsafe code blocks to force compilation, sacrificing memory safety in exchange for compilability.
The output limit for Gemini 1.5 Pro is typically in the range of 8,192 to 32,768 tokens.
When comparing model pricing across the board, estimating the monthly bill for completing the same task is more meaningful than simply comparing the listed price per million tokens, because the number of tokens consumed by different models to complete the same task can vary significantly.
Frontier-grade is a term used in the AI industry to describe the capability tier of the current top large models, typically referring to models such as GPT-4, Claude 3, and Gemini Ultra.
Image-to-Text leverages the visual understanding capabilities of multimodal large language models to map visual features to natural language descriptions.
Gemini already supports a 2 million token context, Claude supports 200,000 tokens, and Qwen has advanced to the 1 million token level.
Perplexity combines retrieval-augmented generation (RAG) with a multi-model routing strategy, and the Pro version allows users to choose the underlying model, including GPT-4o, Claude 3.5 Sonnet, Sonar Large, and others.
All Facts (13)
The context window in the GPT-3 era was only 2,048 tokens, whereas Google Gemini already supports millions of tokens, Claude supports 200K tokens, and GPT-4 Turbo supports 128K tokens.
90%VerifiedGoogle Gemini 1.5 Pro supports a context window of up to 1 million tokens and has native multimodal capabilities.
90%VerifiedThe context window of Gemini 1.5 Pro has been extended to 1 million tokens, equivalent to approximately 750,000 English words.
85%VerifiedGemini 1.5 Pro is developed by Google DeepMind and features native multimodal capabilities and deep integration with Google Workspace.
80%VerifiedFrontier-grade is a term used in the AI industry to describe the capability tier of the current top large models, typically referring to models such as GPT-4, Claude 3, and Gemini Ultra.
75%VerifiedClaude 3.5 and Gemini 1.5 Pro have expanded their context windows to 100K and even million-token levels.
75%UnverifiedImage-to-Text leverages the visual understanding capabilities of multimodal large language models to map visual features to natural language descriptions.
60%UnverifiedPerplexity combines retrieval-augmented generation (RAG) with a multi-model routing strategy, and the Pro version allows users to choose the underlying model, including GPT-4o, Claude 3.5 Sonnet, Sonar Large, and others.
60%UnverifiedThe context window of LLMs has expanded from the early 4K tokens to the million-token scale of Gemini 1.5 Pro.
60%UnverifiedDataBugBench testing covers five models: Claude 3.5 Sonnet, GPT-4o, Gemini 1.5 Pro, Qwen 2.5 Coder 32B, and Llama 3.3 70B.
50%UnverifiedWhen handling complex data ownership nodes, Gemini 1.5 Pro frequently uses unsafe code blocks to force compilation, sacrificing memory safety in exchange for compilability.
50%UnverifiedThe output limit for Gemini 1.5 Pro is typically in the range of 8,192 to 32,768 tokens.
50%UnverifiedWhen comparing model pricing across the board, estimating the monthly bill for completing the same task is more meaningful than simply comparing the listed price per million tokens, because the number of tokens consumed by different models to complete the same task can vary significantly.
50%