Requesty

gemini-2.5-flash

Coding API/🌍 Global/chat10% off

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.More
All models →

Which id to call

coding/gemini-2.5-flash

This exact deployment on Coding API, with no routing and no failover. Send it as the model field.

Input /1M

$0.27

$0.30 list

$0.07 cached

Output /1M

$2.25

$2.50 list

8.3x input

Context

1.0M

66K output

Added

Jun 2025

chat

Capabilities 4/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What gemini-2.5-flash costs

Provider prices per 1M tokens, updated August 24, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

10% off

This endpoint is discounted. List is $0.30 per 1M input and $2.50 per 1M output, and the rates below are what you pay. The discount applies to every request on this endpoint, with nothing to claim or enter.

Input /1M

$0.27

$0.30 list

Output /1M

$2.25

$2.50 list

Cache write /1M

$0.50

Cache read /1M

$0.07

What a workload costs

100K input + 10K output
$0.0495
1M input + 100K output
$0.49
10M input + 1M output
$4.95

At the rates above, before caching. A cache read costs $0.07 per 1M, so repeated context lands under these figures.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to coding/gemini-2.5-flash. Existing OpenAI SDK code needs no other edit.

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

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

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window1.0M tokens
Max output66K tokens
API typechat
AddedJun 2025
Model id
Data retentionNone
Used for trainingNo
Served from🌍 Global
Privacy policyPrivacy Policy→

Released 2025-05-20

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

GPQA Diamondreasoning
79.0%

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

Intelligence Indexreasoning
20.3%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

More from Coding API

Newest first, on the same provider and the same key.

gemini-2.5-flash10% off

1.0M ctx/$0.27/1M/us-west1

gemini-2.5-flash10% off

1.0M ctx/$0.27/1M/europe-west1

gemini-2.5-flash10% off

1.0M ctx/$0.27/1M/europe-west4

gemini-2.5-flash10% off

1.0M ctx/$0.27/1M/europe-west8

gemini-2.5-flash10% off

1.0M ctx/$0.27/1M/us-south1

gemini-2.5-flash10% off

1.0M ctx/$0.27/1M/us-east5

Reference

gemini-2.5-flash questions

How much does gemini-2.5-flash cost?

gemini-2.5-flash is priced at $0.27 per million input tokens and $2.25 per million output tokens when accessed via Requesty. Those figures include a 10% discount on this endpoint, off a list rate of $0.30 per million input tokens and $2.50 per million output tokens. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

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 83.2% on MMLU Pro, 79.0% on GPQA Diamond, 73.3% on Math Index. 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 "coding/gemini-2.5-flash". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run gemini-2.5-flash through Requesty?

Yes. gemini-2.5-flash runs through Requesty's OpenAI-compatible API, served from Coding API. You do not host the model yourself: point base_url at Requesty, set the model to "coding/gemini-2.5-flash", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call gemini-2.5-flash through one endpoint

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

All Coding API models