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gemini-2.5-proGoogle LLC (Vertex AI) @us-east5

in /1M$1.25input tokens
out /1M$10.00output tokens
context1.0M66K out
trainingnoon your prompts
api
chat
hosting
US
model lab
Google
weights
closed
added
June 2025
model id
vertex/gemini-2.5-pro@us-east5

capabilities 7/8

visionreasoningtoolscachingweb searchjson schemacomputer useimage gen

2 providers serve gemini-2.5-pro. This page is one of them. Compare all endpoints

pricing

no markup
input
$1.25
output
$10.00
cache write
$2.38
cache read
$0.31

worked cost

Estimated cost for three request volumes
volumecost
100K in + 10K out$0.23
1M in + 100K out$2.25
10M in + 1M out$22.50

Requesty charges what the upstream provider charges, with no markup and no per-request fee. Prompt caching and smart routing cut the effective cost further on repeated context. Gateway pricing

provider price, passed throughper 1M tokens

measured

through Sep 12

no measured traffic for this endpoint yet. pricing above is the provider's own

benchmarks

artificial analysis
Benchmark scores, each normalised to 100
evalScore as a share of 100/100
Math Index87.7
AIME 202587.7
MMLU Pro86.2
GPQA Diamond84.4
LiveCodeBench80.1
τ²-Bench54.1
SciCode46.3
Coding Index33.3
Terminal-Bench Hard26.5
Humanity's Last Exam22.5
Intelligence Index16.7
11 evalsmean56.9

Scores from Artificial Analysis and public leaderboards, normalised to 100. Bar colour is the band, not the rank: green 80 and up, blue 55 and up, amber 30 and up. Benchmarks measure narrow skills, so test on your own workload before committing. Released 2025-06-05.

policy

data terms
retention
none
trains on prompts
no
hosted in
US
deployment
us-east5

Terms are the provider's, not Requesty's: routing a request here puts it under them. Vertex AI Data Governance

Need every request to stay in one jurisdiction? EU hosting routes only through EU-hosted providers.

quickstart

openai compatible
vertex/gemini-2.5-pro@us-east5

this id pins the google llc (vertex ai) deployment, with no routing and no failover. the managed id on the canonical page routes across all 2 providers instead

main.py
from openai import OpenAI client = OpenAI(    api_key="YOUR_REQUESTY_API_KEY",    base_url="https://router.requesty.ai/v1",) response = client.chat.completions.create(    model="vertex/gemini-2.5-pro@us-east5",    messages=[        {"role": "user", "content": "Explain quantum computing in one paragraph."},    ],) print(response.choices[0].message.content)

Change the base url to https://router.requesty.ai/v1, use your Requesty key, set the model to the id above. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog. Browse all models

notes

reference

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.

questions 7

How much does gemini-2.5-pro cost?
gemini-2.5-pro is priced at $1.25 per million input tokens and $10.00 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Requesty charges exactly what the upstream provider charges, we don't add markup.
What is the context window of gemini-2.5-pro?
gemini-2.5-pro has a context window of 1.0M tokens, with a maximum output of 66K tokens per response. That is roughly 1,398 words of input you can fit in a single prompt.
How does gemini-2.5-pro perform on benchmarks?
gemini-2.5-pro scores 87.7% on Math Index, 87.7% on AIME 2025, 86.2% on MMLU Pro. The benchmarks pane shows every published result, each normalised to 100.
What can gemini-2.5-pro do?
gemini-2.5-pro supports vision input, tool calling, extended reasoning, prompt caching, web search, structured outputs (JSON schema), computer use. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use gemini-2.5-pro with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "vertex/gemini-2.5-pro@us-east5". The quickstart pane shows Python, JavaScript and cURL snippets.
Can I run gemini-2.5-pro through Requesty?
Yes. gemini-2.5-pro runs through Requesty's OpenAI-compatible API, served from Google LLC (Vertex AI) in us-east5. You do not host the model yourself: point base_url at Requesty, set the model to "vertex/gemini-2.5-pro@us-east5", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
What region is this deployment?
This variant of gemini-2.5-pro is deployed in us-east5. Region-specific endpoints matter for data residency, latency to your users, and compliance requirements such as GDPR and HIPAA. Other regions for the same model are listed on the canonical model page.

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call gemini-2.5-pro through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover when a provider degrades, and prompt caching built in. Methodology

gemini-2.5-progoogle llc (vertex ai)in $1.25 /1Mout $10.00 /1Mctx 1.0Mhosted US2 providers serve this model