gemini-3-flash-preview
Google LLC (Vertex AI)/🇺🇸 US/chat
Gemini 3 Flash Preview is designed to deliver strong agentic capabilities (near-Pro level) at substantial speed and value. Making it perfect for engaging multi-turn chats, and collaborating back and forth with your coding agent without getting out of flow. Compared to 2.5 Flash it delivers significant improvements across the board.MoreLess
Which id to call
vertex/gemini-3-flash-previewThis exact deployment on Google LLC (Vertex AI), with no routing and no failover. Send it as the model field.
Input /1M
$0.50
$0.05 cached
Output /1M
$3.00
6.0x input
Context
1.0M
66K output
Paid /1M
$0.77
measured, cache included
Capabilities 6/8
Provider rates
What gemini-3-flash-preview costs
Provider prices per 1M tokens, updated September 11, 2026.moreless
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.
Input /1M
$0.50
Output /1M
$3.00
Cache write /1M
$1.00
Cache read /1M
$0.05
What a workload costs
- 100K input + 10K output
- $0.0800
- 1M input + 100K output
- $0.80
- 10M input + 1M output
- $8.00
At the rates above, before caching. A cache read costs $0.05 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 vertex/gemini-3-flash-preview. Existing OpenAI SDK code needs no other edit.
123456789101112131415from 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-3-flash-preview", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Live from production
Google LLC (Vertex AI) on gemini-3-flash-preview, measured
What this provider was measured doing on gemini-3-flash-preview across Requesty traffic.moreless
Whole-window figures, because that is the grain published per provider. They cover Google LLC (Vertex AI) serving this model in every region it serves it from, so a region-pinned deployment shares them with its siblings. A figure is absent where no qualifying sample exists.
First token
4.78s
median
p95 wait
16.61s
slowest 5%
Output speed
132/s
median tokens
Paid /1M
$0.77
blended, cache included
Cache hit
23.2%
of input tokens
Compare these against the other 1 provider serving gemini-3-flash-preview.
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2025-12-17
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
Graduate-level physics, chemistry & biology questions designed to resist Googling.
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 Google LLC (Vertex AI)
Newest first, on the same provider and the same key.
Reference
gemini-3-flash-preview questions
How much does gemini-3-flash-preview cost?
gemini-3-flash-preview is priced at $0.50 per million input tokens and $3.00 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. 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-3-flash-preview?
gemini-3-flash-preview 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-3-flash-preview perform on benchmarks?
gemini-3-flash-preview scores 97.0% on Math Index, 97.0% on AIME 2025, 90.8% 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-3-flash-preview do?
gemini-3-flash-preview 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-3-flash-preview 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-3-flash-preview". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run gemini-3-flash-preview through Requesty?
Yes. gemini-3-flash-preview runs through Requesty's OpenAI-compatible API, served from Google LLC (Vertex AI). You do not host the model yourself: point base_url at Requesty, set the model to "vertex/gemini-3-flash-preview", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call gemini-3-flash-preview through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.