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Google LLC (Vertex AI)

gemini-3.1-pro-preview:flex

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.

VisionReasoningTool callingCachingWeb searchJSON schema
Try gemini-3.1-pro-preview:flexCompareDocs

Specifications

Context window1.0M tokens
Max output66K tokens
API typechat
AddedFeb 19, 2026
Model IDvertex/gemini-3.1-pro-preview:flex
Data retentionNo
Used for trainingNo
Provider location🇺🇸 US / 🇪🇺 EU
Privacy policyVertex AI Data Governance→

Benchmarks

Released 2026-02-19
Coding Indexcoding
55.5%

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

GPQA Diamondreasoning
94.1%

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

Intelligence Indexreasoning
57.2%

Artificial Analysis Intelligence Index — 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 — always test on your own workload.

Pricing

Input / 1M
$1.00
Output / 1M
$6.00
Cache write / 1M
$2.75
Cache read / 1M
$0.10
Estimated cost
100K input + 10K output$0.16
1M input + 100K output$1.60
10M input + 1M output$16.00

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 vertex/gemini-3.1-pro-preview:flex.

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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-3.1-pro-preview:flex",
    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-3.1-pro-preview:flex cost?
gemini-3.1-pro-preview:flex is priced at $1.00 per million input tokens and $6.00 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. Requesty charges exactly what the upstream provider charges — we don't add markup.
What is the context window of gemini-3.1-pro-preview:flex?
gemini-3.1-pro-preview:flex 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:flex perform on benchmarks?
gemini-3.1-pro-preview:flex scores 95.6% on τ²-Bench, 94.1% on GPQA Diamond, 58.9% on SciCode. 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:flex do?
gemini-3.1-pro-preview:flex 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:flex 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-3.1-pro-preview:flex". The Quickstart above shows Python, JavaScript and cURL snippets.

Access gemini-3.1-pro-preview:flex 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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