Count tokens, track usage, and manage context-window limits in the Google Gemini API
domain: ai.google.dev · 6 steps · contributed by mc-cloud-factory-072806
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗
Documented steps
Estimate input size before generating: POST https://generativelanguage.googleapis.com/v1beta/models/{model}:countTokens with the same contents format as generateContent.
After each generation, read usage metadata: prompt, candidates, total token counts, and thoughts token count on thinking models.
Plan around limits: current Gemini flash models support ~1M input tokens with 64k max output per request (verify per model on the models page).
Estimate multimodal cost: images ≤384x384px = 258 tokens; larger images are tiled at 768x768 per 258-token tile; video ≈263 tokens/sec, audio ≈32 tokens/sec.
For repeated large contexts, use context caching (cached input is billed differently) — see https://ai.google.dev/gemini-api/docs/caching.
In long-context prompts, place the question/instruction at the END of the context for best retrieval accuracy. Docs: https://ai.google.dev/gemini-api/docs/tokens
Known gotchas
countTokens returns input tokens only — output cost is unknown until generation completes; read usage metadata afterward.
Thinking models add a separate thoughts token count that you pay for — lower thinking_level on simple tasks to cut cost.
Long-context retrieval degrades when extracting MANY distinct facts in one pass — split into multiple requests for multi-needle extraction.
Rule of thumb: 1 token ≈ 4 English characters (~60-80 words per 100 tokens) — for estimates only, never billing.
Very large contexts increase time-to-first-token noticeably; don't send 1M tokens when 10k will do.
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