SHUT DOWN2M CONTEXTARCHIVE PRICEREPLACE WITH GEMINI-2-5-PRO
Gemini 1.5 Pro API Pricing
Google's retired Gemini 1.5 Pro row, kept for historical invoices and 2M-context price comparisons: $1.25/M input, $5/M output, with no cache discount listed. Kept as an archive page.
Input - per 1M tokens
$1.25/M
Tier 1 baseline archive
Output - per 1M tokens
$5.00/M
Tier 2 $10/M archive
Cache N/A - billed as input
$1.25/M
No cache price listed N/A
Effective - agentic blend
$1.55/M
92/8 split - 82% cache
§ 01 / TERMINAL
Run the numbers.
Calculator pre-loaded with Gemini 1.5 Pro archive rates. Archive price uses the last-published project snapshot: $1.25/M input and $5/M output, with a higher >128K prompt tier at $2.50/M input and $10/M output.
$ /mo
Workload split
Prompt cache hit rate
Tokens you can process
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Words equivalent (English)
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Effective rate
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§ 02 / SCENARIOS
Real-world presets.
ARCHIVE
Invoice replay
$1.25/1M in
BENCHMARK
Legacy benchmark batch
$0.140/run
MIGRATION
Migration cost comparison
$0.275/pack
RAG
Archived RAG answer
$0.247/answer
§ 03 / TAPE
Price history.
Input · $1.3/M
Output · $5/M
Cached · $1.3/M
MAY 23 Released gemini-1-5-proSEP 29 Shutdown status confirmed by Google release notesSEP 29 Endpoint shut down; replacement is gemini-2-5-proMAY 18 Archive pricing retained from verified snapshot
§ 04 / TOKENIZER
Paste text. See tokens. See cost.
Estimate · gemini-tokenizer-estimate · ≈3.85 chars/token Auto-counts as you type
This is a chars-per-token approximation, not a real tokenizer. Actual tokens vary by language, code density, and tool-call overhead — counts are typically ±10–20% off for English prose, more for code or non-Latin scripts. For exact billing, use the vendor's official tokenizer.
Characters —
Words —
Tokens (estimated) —
Cost as input · uncached —
Cost as output · uncached —
Cost as cached input —
| Model | Input /M | Output /M | Effective blended | Context | Best for |
|---|---|---|---|---|---|
| Gemini 1.5 Pro Current | $1.25 | $5.00 | $1.55 archive 92/8 | 2M | Archive 2M-context invoices |
| Gemini 3.1 Pro Preview | $2.00 cache $0.20 | $12.00 | $1.44 current replacement | 1M | Current Gemini Pro preview |
| Gemini 3 Flash Preview | $0.50 cache $0.05 | $3.00 | $0.36 newer flash preview | 1M | Current Gemini 3 Flash |
| Gemini 3.1 Flash-Lite | $0.25 cache $0.03 | $1.50 | $0.18 new light tier | 1M | Current high-volume Gemini |
| Gemini 2.5 Pro | $1.25 cache $0.13 | $10.00 | $1.10 stable pro | 2M | Stable Pro replacement |
| Gemini 2.5 Flash | $0.30 cache $0.03 | $2.50 | $0.27 stable flash | 1M | Stable Flash replacement |
| Gemini 2.5 Flash-Lite | $0.10 cache $0.01 | $0.40 | $0.06 stable lite | 1M | Stable Lite replacement |
| GPT-5.4 mini | $0.75 cache $0.07 | $4.50 | $0.54 OpenAI mini | 400K | OpenAI mini alternative |
Frequently asked.
Gemini 1.5 Pro pricing questions, with archive/deprecation status separated from token math.
Q · 01 What is Gemini 1.5 Pro's API price? +
This is an archive price, not a current endpoint recommendation. The page uses
$1.25/M input and $5/M output. The archived higher prompt tier is $2.5/M input and $10/M output.Q · 02 Is Gemini 1.5 Pro still available? +
No. Google says
gemini-1-5-pro was shut down on 2025-09-29. Use gemini-2-5-pro for current traffic.Q · 03 Does prompt caching apply? +
No current cache price is listed for this model. For blended math, this page treats cached input as regular input at
$1.25/M rather than inventing a discount.Q · 04 How should I use this page? +
Use it for archive pricing, old invoice reconciliation, and generation-to-generation price comparisons. Do not route new production traffic to this model, because the endpoint has already been shut down.
Q · 05 What about Batch and Flex pricing? +
This archive page does not model a current Batch or Flex path because Gemini 1.5 Pro is shut down. Use Gemini 2.5 Pro or Gemini 3.1 Pro Preview for current pricing.
Q · 06 How accurate is the tokenizer estimate? +
The widget uses
4.0 characters per token as a Gemini planning estimate. Exact billing can differ by language, media inputs, tool calls, and how Google tokenizes multimodal content.