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Google LLC (Gemini API)

gemini-2.5-flash

Google's first hybrid reasoning model which supports a 1M token context window and has thinking budgets. Most balanced Gemini model, optimized for low latency use cases.

👁Vision🧠Reasoning🔧Tool calling⚡Caching
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Specifications

Context window1.0M tokens
Max output66K tokens
API typechat
AddedMay 20, 2025
Model IDgoogle/gemini-2.5-flash
Data retentionYes
Used for trainingUnknown
Provider location🌍 Global
Privacy policyGemini API Terms →

Benchmarks

Released 2025-04
SWE-Bench Verifiedcoding
53.2%

Resolving real GitHub issues from 12 popular Python repositories.

GPQA Diamondreasoning
68.3%

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

MMLU Proknowledge
78.4%

Massive Multitask Language Understanding across 57 academic subjects.

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 — always test on your own workload.

Pricing

Input / 1M
$0.30
Output / 1M
$2.50
Cache write / 1M
$0.55
Cache read / 1M
$0.07
Estimated cost
100K input + 10K output$0.0550
1M input + 100K output$0.55
10M input + 1M output$5.50

Requesty charges exactly what the upstream provider charges — 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 google/gemini-2.5-flash.

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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="google/gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

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

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Frequently asked questions

How much does gemini-2.5-flash cost?
gemini-2.5-flash is priced at $0.30 per million input tokens and $2.50 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-flash?
gemini-2.5-flash 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 perform on benchmarks?
gemini-2.5-flash scores 88.5% on HumanEval, 85.3% on MATH, 78.4% on MMLU Pro. 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 do?
gemini-2.5-flash supports vision input, tool calling, extended reasoning, prompt caching. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use gemini-2.5-flash 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 "google/gemini-2.5-flash". The Quickstart above shows Python, JavaScript and cURL snippets.

Access gemini-2.5-flash through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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