Skip to content
CostPerPrompt

Gemini 2.5 Pro Preview 06-05 API Pricing

Google · context window 1M · prices updated 2026-09-15

This is a dated/preview alias of Gemini 2.5 Pro and currently carries identical pricing. The main page is kept up to date — this one exists so the exact model string resolves.

Input per 1M tokens
$1.25
Output per 1M tokens
$10.00
Cached input per 1M
$0.125
90% cheaper than fresh input

Where Gemini 2.5 Pro Preview 06-05 sits on price

At $10.00 per million output tokens, Gemini 2.5 Pro Preview 06-05 is a premium tier model — 4.8× the median output price of $2.08, which puts it cheaper than 22% and pricier than 78% of the models we track. It sits 34th cheapest of the 38 Google models we track. Output costs 8.0× more than input here, so anything that makes the model write less — tighter instructions, structured output, lower max_tokens — moves the bill more than trimming the prompt.

Output tokens per $1
100,000
One full context fill
$1.31
Cheaper than
22% of tracked models

Cached input is 90% cheaper. On an input-heavy workload you need roughly a 28% cache-hit rate to take 25% off the input line — reachable for chatbots and agents that resend the same system prompt and history.

What real workloads cost on Gemini 2.5 Pro Preview 06-05

These three workloads are the ones teams actually run on a premium-priced model — a $10.00/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
Support chatbot — 500 conversations/day (5K in / 1.4K out) Conversation history re-sent each turn — the classic prompt-caching win. $0.0203 $304
Coding agent — 300 sessions/day (60K in / 12K out) Multi-step loops re-read the same files; caching matters more than raw price. $0.195 $1755
Long-context analysis — 200 runs/day (250K in / 4K out) Only possible on large-context models; input cost is nearly the whole bill. $0.3525 $2115

Model your exact traffic in the API cost calculator — it preloads Gemini 2.5 Pro Preview 06-05 with caching and batch options.

Gemini 2.5 Pro Preview 06-05 price history

Gemini 2.5 Pro Preview 06-05's price has not moved since we began tracking it on Aug 2, 2026 — 44 days of stability in a market where 78 of the 334 models we track have repriced over the same period, including 6 of Google's own 38 models.

Change-points from our daily price snapshots (tracking since Aug 2, 2026; intraday moves between snapshots are not captured).

Gemini 2.5 Pro Preview 06-05 vs Claude Sonnet 5

The closest-priced alternative from another vendor is Claude Sonnet 5 (Anthropic) — priced within a rounding error on output, with $0.75 more per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See Claude Sonnet 5 pricing →

Cheaper alternatives

More Google models

Frequently asked questions

How much does the Gemini 2.5 Pro Preview 06-05 API cost?

Gemini 2.5 Pro Preview 06-05 costs $1.25 per million input tokens and $10.00 per million output tokens, with cached input at $0.125 per million (90% cheaper). That works out to roughly 100,000 output tokens per dollar.

What does the support chatbot workload cost on Gemini 2.5 Pro Preview 06-05?

Support chatbot — 500 conversations/day (5K in / 1.4K out) costs about $0.0203 per request and $304 per month on Gemini 2.5 Pro Preview 06-05. Conversation history re-sent each turn — the classic prompt-caching win.

What does it cost to fill Gemini 2.5 Pro Preview 06-05's 1M context window?

Sending 1M of input in a single request costs $1.31 at $1.25 per million tokens — before any output. With prompt caching that same fill drops to about $0.1311 on repeat requests. This is why large context windows are cheap to advertise and expensive to actually use.

Is Gemini 2.5 Pro Preview 06-05 worth the price?

Gemini 2.5 Pro Preview 06-05 sits in the middle of the market (34 of 38 by price within Google). The honest test is a routing experiment: send the same 200 real requests to this model and to a tier below, and compare failure rate against the price gap — most teams find a majority of traffic never needed the pricier model.