Gemini 3.1 Pro Token Counter

Gemini 3.1 Pro Token Counter - Estimate Tokens Before You Send

A 1,048,576-token input limit and 65,536-token output limit make Gemini 3.1 Pro Preview one of the widest-context options for document-heavy workflows.¹

Because this tool runs in the browser, it uses a character-division estimate rather than Google's model tokenizer. For Gemini text, Google describes one token as roughly four characters, so the estimate is best for sizing prompts before you send them.² Gemini 3.1 Pro Preview uses tiered pricing: $2.00 per million input tokens and $12.00 per million output tokens for prompts up to 200K tokens, and $4.00 per million input tokens and $18.00 per million output tokens for prompts above 200K tokens.³

Context window and limits

Gemini 3.1 Pro Preview allows 1,048,576 input tokens per request, with a 65,536-token output limit.¹ That gives large-document analysis, research workflows, and retrieval pipelines plenty of room, but the limit still includes the response budget you reserve for the model, which means the output ceiling is a separate constraint you must account for when planning long-generation tasks.

The amber warning at 75% means about 262,144 tokens remain before the 1,048,576-token ceiling, providing substantial headroom for both retrieved context and model output. Use that threshold as a planning signal: trim retrieved context, summarize older conversation turns, or split the input before the request reaches the API, since exceeding the limit at runtime forces either an error or silent truncation that degrades output quality.

For research pipelines that load multiple documents, count each document's token contribution individually before assembling the full prompt. A workflow that retrieves five documents averaging 40,000 tokens each consumes 200,000 tokens on retrieval alone; combined with a system prompt and user query, the assembled request may already be near the 50% mark before the model generates a single output token. Breaking down the budget by component lets you identify which documents to truncate or summarize before the API call.

Pricing breakdown

Gemini 3.1 Pro Preview pricing is tiered by prompt size, which makes cost projection more nuanced than a flat-rate model but also creates an opportunity to optimize spending by keeping prompts under the tier boundary.³ For prompts up to 200K tokens, input costs $2.00 per million tokens and output costs $12.00 per million tokens. For prompts above 200K tokens, input costs $4.00 per million tokens and output costs $18.00 per million tokens, effectively doubling the input rate once the threshold is crossed.

Calculating request costs at high volume

A 5,000-token prompt with a 5,000-token response costs about $0.010 input plus $0.060 output, or $0.070 total, when the prompt stays under 200K tokens and qualifies for the lower tier. Sampling your prompt distribution before committing to a batch volume is the only reliable way to project costs when the tier boundary falls within your expected range, and measuring actual prompt lengths across your full dataset prevents the surprise of discovering that your average request crossed the threshold only after the first month of billing.

Tokenizer and accuracy

The ~ symbol on Gemini 3.1 Pro counts indicates that this is an approximation rather than an exact count derived from Google's proprietary model tokenizer. Google tokenizes text, images, audio, video, and other input modalities through the Gemini API using a vocabulary that is not published as a browser-compatible library, so the browser estimate here cannot reproduce that model-specific tokenization exactly and will carry some margin of error.²

Use these counts for budgeting, prompt sizing, and context-window checks, not for exact billing projection, since the character-division estimate cannot reproduce Google's model-specific tokenization of text, images, and other modalities. When precision matters, count the assembled request with the same model you plan to call, and treat the API-side count as the authoritative figure for any billing-critical cost projection.

Adding a buffer for batch cost projections

For batch planning, add a conservative buffer to the browser estimate before multiplying by request volume, since the buffer absorbs tokenizer variance while still showing whether the workload belongs under the 200K pricing tier or above it. A 20% buffer on a 150,000-token estimate brings the planning figure to 180,000 tokens, which keeps the request safely within the lower pricing tier even if the actual count runs higher than the browser approximation suggests.

Getting an exact Gemini 3.1 Pro count via the countTokens API

Getting a model-specific token count before sending to Gemini 3.1 Pro requires the Gemini API countTokens method, which runs the actual model tokenizer on your input content and returns the precise token count for the prompt.⁴ The API reference says models.countTokens processes the same content the model will see at inference time, so the count accounts for all modality-specific tokenization including text, images, and other input types that the browser estimate cannot reproduce.

Using countTokens for staged prompt assembly

For pipelines that manage context budgets programmatically, call countTokens after each assembly step: first after loading the system prompt, then again after appending retrieved chunks, and finally after adding the user message. If any intermediate count exceeds your target threshold, trim the retrieved context before reaching the final assembly. This staged counting approach prevents expensive API calls that would return a context overflow error and reveals exactly which step pushed the prompt over the limit, giving you a clear signal for where to apply compression or chunking. Running both the browser estimate and the API countTokens method on the same prompt also lets you measure the approximation error for your specific content type, which helps you set an appropriate buffer for pre-filtering inputs before the more expensive API-side count.

Build the assembled count into a lightweight pre-send check so the expensive API call never fires until the staged total clears your target window, which is especially valuable when many requests share a large retrieved context that changes between calls. Pairing the browser estimate with the staged API count gives you both a fast local signal and a precise server-side one without paying for the API count on every draft.

When Gemini 3.1 Pro pricing makes sense versus other 1M-context models

When your workload needs 1M-context capacity and produces long outputs relative to inputs, Gemini 3.1 Pro Preview's two-to-one input-to-output ratio can be easier to budget than models with steeper output pricing, especially when prompts stay under the 200K threshold. A request with 10,000 input tokens and 10,000 output tokens costs about $0.020 input plus $0.120 output, or $0.140 total, when the prompt qualifies for the lower pricing tier.

For workloads that generate verbose outputs, such as long analyses, detailed reports, or extended code files, the advantage grows as output length increases relative to input, because the two-to-one ratio scales more gently than the five-to-one or six-to-one ratios used by Claude and GPT. Compare your typical input-to-output ratio against the tiered price table before committing to a production model, then paste your prompt to see where it lands against the 200K Gemini 3.1 Pro tier boundary and whether it qualifies for the lower rate before you send it.

When to use this

Use this when sizing prompts for Gemini 3.1 Pro before sending. You should treat the count as an estimate for cost modeling and verify billing-critical requests with the Gemini API countTokens method.⁴

Examples

System prompt: 400 tokens. Document (50 pages): ~38,000 tokens. Questions: 200 tokens. Total estimate: ~38,600 tokens.

Input cost estimate: ~$0.077 at the short-prompt input rate. At 1,000 requests: ~$77 input cost.

Average article: 2,500 tokens. System prompt: 500 tokens. Average summary output: 300 tokens.

Total per request: ~3,000 tokens in, 300 out. Cost: ~$0.0096 at the short-prompt rates. At 10,000/day: ~$96/day total.