Gemini Token Calculator

Gemini models use advanced tokenization to break down text into smaller units for processing. Google's tokenizer is optimized for natural language understanding and handles various content types efficiently, including text, code, images, and multilingual content. The tokenization is designed to work seamlessly across Gemini's multimodal capabilities.

Our calculator provides highly accurate token counting for Gemini models by using methods that closely match Google's tokenization process. The results should be very close to what you'll see when using Gemini's API, helping you accurately estimate costs for your multimodal AI applications.

Gemini models are Google's most advanced AI systems, designed for multimodal understanding. They can process text, images, audio, and code simultaneously. Gemini 1.5 Pro offers exceptional reasoning capabilities, massive context windows, and strong performance across diverse tasks while maintaining Google's safety standards.

Google offers several Gemini variants: Gemini 1.5 Pro (the flagship model with the largest context window and best performance), Gemini 1.5 Flash (optimized for speed and efficiency), Gemini 1.0 Pro (previous generation with solid performance), and specialized variants for specific use cases. Each model balances performance, speed, and cost differently.

Gemini offers competitive pricing: Gemini 1.5 Flash costs around $0.075/$0.30 per million tokens (input/output), while Gemini 1.5 Pro costs approximately $1.25/$5.00 per million tokens. This pricing is generally competitive with similar models from other providers, often offering better value for multimodal applications.

Gemini 1.5 Pro supports an extraordinary context window of up to 2 million tokens, which is among the largest available in commercial AI models. This massive context window allows for processing entire books, large codebases, hours of audio, or extensive document collections in a single request.

Yes, Gemini models are inherently multimodal and can process text, images, audio, and video content simultaneously. This makes them ideal for applications requiring visual understanding, document analysis with images, video content analysis, and complex multimodal reasoning tasks that other text-only models cannot handle.

Gemini models excel at coding and programming tasks across multiple languages. They can understand complex codebases, generate high-quality code, debug issues, and provide detailed explanations. Gemini's large context window is particularly valuable for working with large codebases and understanding complex software architectures.

Gemini's multimodal capabilities make it excellent for business applications requiring document processing with images, data analysis from charts and graphs, customer service with visual content, content moderation, automated report generation from mixed media, and applications requiring understanding of both text and visual elements.

Gemini models support extensive multilingual capabilities and can understand, generate, and translate text in dozens of languages. The models are trained on diverse global datasets and can maintain context across different languages within the same conversation, making them suitable for international applications.

Gemini models are ideal for: 1) Multimodal applications requiring image and text processing, 2) Large document analysis and summarization, 3) Complex reasoning tasks with massive context, 4) Video and audio content analysis, 5) Educational applications with visual content, 6) Research applications requiring extensive context, 7) Creative projects combining text and visuals, and 8) Enterprise applications with mixed media content.