Gemini Token Counter

Count tokens for Google's Gemini models — 2.5 Pro, 2.5 Flash, 1.5 Pro — with tiered pricing baked in. Gemini's pricing steps up after 128K input tokens, and this tool handles that automatically. Runs in your browser; uses a heuristic calibrated against Google's official countTokens endpoint.

How to use the Gemini Token Counter

Paste any text, pick the Gemini variant, and press Count tokens. With cost enabled, the tool computes per-call, monthly, and annual cost projections — and correctly applies the long-context price step at 128K input tokens, which is easy to miss when planning a workload that sometimes runs short and sometimes runs long.

What's different about Gemini's tokenizer

Gemini uses Google's SentencePiece tokenizer with a vocabulary in the ~250K range. It's noticeably more efficient than Claude or GPT-4 (cl100k_base) for English — typically 5-10% fewer tokens for the same text. For non-Latin scripts (Mandarin, Hindi, Korean, Arabic), Gemini is among the most efficient of any major model — sometimes 30-50% fewer tokens than Claude or GPT-3.5 for the same content. That matters more than the published per-token price difference for multilingual workloads.

Long-context pricing in detail

Gemini 2.5 Pro and Gemini 1.5 Pro have two-tier input pricing:

The tier applies to the entire input on a per-request basis — a 130K-token input is billed entirely at the higher rate, not just the 2K above the threshold. This makes the difference between a 125K-token prompt and a 135K-token prompt much bigger than the 10K extra tokens suggests. If you can keep below 128K with smart retrieval, the per-call cost halves.

Common use cases

• Long-document Q&A. Pasting an entire book or transcript to see whether it fits under the 128K threshold for 2x cheaper pricing.
• Codebase summarization. Gemini 2.5 Pro's 2M context lets you fit substantial monorepos in a single call. Sizing the actual token count before submitting tells you whether you'll hit the long-context tier.
• Multimodal cost estimation. Combined with the rule of thumb (image ≈ 258 tokens, audio ≈ 32/sec, video ≈ 258/frame at 1fps), a text-only token count plus media budget gives a per-call estimate for full multimodal pipelines.
• Provider comparison. Pasting the same text into this tool and the OpenAI Token Counter shows where Gemini's tokenizer advantage applies to your workload.
• Vertex AI vs AI Studio. The same per-token pricing applies on both surfaces, but Vertex adds an enterprise SLA and regional deployment options. Token math is the same.