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Google LLC (Vertex AI)@us-east5

gemini-2.5-flash-lite

Google's smallest and most cost effective model, built for at scale usage.

VisionReasoningTool callingCachingWeb searchJSON schema
Try gemini-2.5-flash-liteCompareDocs

Specifications

Context window1.0M tokens
Max output66K tokens
API typechat
AddedMay 20, 2025
Model IDvertex/gemini-2.5-flash-lite@us-east5
Data retentionNo
Used for trainingNo
Provider locationπŸ‡ΊπŸ‡Έ US / πŸ‡ͺπŸ‡Ί EU
Privacy policyVertex AI Data Governance→

Benchmarks

Released 2025-06-17
Coding Indexcoding
9.5%

Artificial Analysis Coding Index β€” a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
62.5%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
17.6%

Artificial Analysis Intelligence Index β€” a composite of multiple evaluations measuring overall model capability.

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality β€” always test on your own workload.

Pricing

Input / 1M
$0.10
Output / 1M
$0.40
Cache write / 1M
$0.18
Cache read / 1M
$0.01
Estimated cost
100K input + 10K output$0.0140
1M input + 100K output$0.14
10M input + 1M output$1.40

Requesty charges exactly what the upstream provider charges β€” no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to vertex/gemini-2.5-flash-lite@us-east5.

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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-flash-lite@us-east5",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

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claude-opus-4-8
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gemini-3.5-flash
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gemini-3.5-flash
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1.0M ctx$1.50/1M
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Frequently asked questions

How much does gemini-2.5-flash-lite cost?
gemini-2.5-flash-lite is priced at $0.10 per million input tokens and $0.40 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 β€” we don't add markup.
What is the context window of gemini-2.5-flash-lite?
gemini-2.5-flash-lite has a context window of 1.0M tokens, with a maximum output of 66K tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
How does gemini-2.5-flash-lite perform on benchmarks?
gemini-2.5-flash-lite scores 75.9% on MMLU Pro, 62.5% on GPQA Diamond, 59.3% on LiveCodeBench. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can gemini-2.5-flash-lite do?
gemini-2.5-flash-lite supports vision input, tool calling, extended reasoning, prompt caching, web search, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use gemini-2.5-flash-lite 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-flash-lite@us-east5". The Quickstart above shows Python, JavaScript and cURL snippets.
What region is this deployment?
This variant of gemini-2.5-flash-lite is deployed in us-east5. Region-specific endpoints matter for data residency, latency to your users, and compliance requirements (GDPR, HIPAA). Other regions for the same model may be listed on the Google LLC (Vertex AI) provider page.

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