Gemini Token Counter
Estimate tokens before sending to Gemini 2.5 Pro, Flash, or Flash Lite. Gemini’s 1M+ context window unlocks workflows that don’t fit in any other model — this tool keeps you oriented when you’re pasting a lot.
Keyword density
Top 8Add a prompt to see your top keywords.
Gemini context windows
| Model | Input window | Notes |
|---|---|---|
| Gemini 2.5 Flash Lite | 1M tokens | Fastest, cheapest |
| Gemini 2.5 Flash | 1M tokens | Default for most workflows |
| Gemini 2.5 Pro | 2M tokens | Heavy multimodal + reasoning |
Note that Gemini’s tokenizer is based on SentencePiece and differs from OpenAI’s and Anthropic’s. For multilingual content the gap is larger — up to ~20%. This counter gives you a fast practical estimate.
Multimodal tokens
Gemini accepts images, audio, and video — and each one consumes tokens. Rough rule-of-thumb from Google’s pricing pages:
- Image: ~258 tokens per image regardless of resolution
- Audio: ~32 tokens per second
- Video: ~263 tokens per second (audio + frames)
This tool counts text only. For mixed inputs, sum your text estimate with the per-modality cost above to budget the total context use.
When you’d pick Gemini over ChatGPT or Claude
The 1M+ window unlocks specific workflows:
- Processing entire textbooks or novels in one pass
- Multi-hour audio transcripts with structure analysis
- Whole-codebase reasoning across hundreds of files
- Video understanding tasks (highlights, summaries, Q&A)
For anything under ~200K tokens, Claude is usually the better fit. For multimodal tasks or anything that genuinely needs the longer window, Gemini is the right call.