gemini-3.1-pro-preview
Google LLC (Gemini API)/🌍 Global/chat10% off
Gemini 3.1 Pro is the next iteration in the Gemini 3 series of models, a suite of highly capable, natively multimodal reasoning models. As of this model card’s date of publication, Gemini 3.1 Pro is Google’s most advanced model for complex tasks. Geminin 3.1 Pro can comprehend vast datasets and challenging problems from massively multimodal information sources, including text, audio, images, video, and entire code repositories.MoreLess
Which id to call
google/gemini-3.1-pro-previewThis exact deployment on Google LLC (Gemini API), with no routing and no failover. Send it as the model field.
Input /1M
$1.80
$2.00 list
$0.18 cached
Output /1M
$10.80
$12.00 list
6.0x input
Context
1.0M
66K output
Paid /1M
$1.58
measured, cache included
Capabilities 6/8
Provider rates
What gemini-3.1-pro-preview costs
Provider prices per 1M tokens, updated August 24, 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.
This endpoint is discounted. List is $2.00 per 1M input and $12.00 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
$1.80
$2.00 list
Output /1M
$10.80
$12.00 list
Cache write /1M
$4.05
Cache read /1M
$0.18
What a workload costs
- 100K input + 10K output
- $0.29
- 1M input + 100K output
- $2.88
- 10M input + 1M output
- $28.80
At the rates above, before caching. A cache read costs $0.18 per 1M, so repeated context lands under these figures.
model=
Which id to call
One base url, https://router.requesty.ai/v1, and one key for every id here.
google/gemini-3.1-pro-previewThis exact deployment on Google LLC (Gemini API), with no routing and no failover. Send it as the model field.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to any id on the left. 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="google/gemini-3.1-pro-preview", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Live from production
Google LLC (Gemini API) on gemini-3.1-pro-preview, measured
What this provider was measured doing on gemini-3.1-pro-preview across Requesty traffic.moreless
Whole-window figures, because that is the grain published per provider. They cover Google LLC (Gemini API) 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
11.76s
median
p95 wait
30.64s
slowest 5%
Output speed
146/s
median tokens
Paid /1M
$1.58
blended, cache included
Cache hit
54.7%
of input tokens
Compare these against the other 1 provider serving gemini-3.1-pro-preview.
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2026-02-19
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
Artificial Analysis Coding Index: a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.
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 (Gemini API)
Newest first, on the same provider and the same key.
Reference
gemini-3.1-pro-preview questions
How much does gemini-3.1-pro-preview cost?
gemini-3.1-pro-preview is priced at $1.80 per million input tokens and $10.80 per million output tokens when accessed via Requesty. Those figures include a 10% discount on this endpoint, off a list rate of $2.00 per million input tokens and $12.00 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-3.1-pro-preview?
gemini-3.1-pro-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-3.1-pro-preview perform on benchmarks?
gemini-3.1-pro-preview scores 95.6% on τ²-Bench, 94.1% on GPQA Diamond, 68.8% on Coding 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-3.1-pro-preview do?
gemini-3.1-pro-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-3.1-pro-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 "google/gemini-3.1-pro-preview". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run gemini-3.1-pro-preview through Requesty?
Yes. gemini-3.1-pro-preview runs through Requesty's OpenAI-compatible API, served from Google LLC (Gemini API). You do not host the model yourself: point base_url at Requesty, set the model to "google/gemini-3.1-pro-preview", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call gemini-3.1-pro-preview through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.