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Google LLC (Vertex AI)

deepseek-v3.2

DeepSeek-V3.2 is a model that harmonizes high computational efficiency with superior reasoning and agent performance. DeepSeek's approach is built upon three key technical breakthroughs: DeepSeek Sparse Attention (DSA), scalable reinforcement learning framework, and large scale agentic task synthesis pipeline.

VisionReasoningTool callingCachingWeb searchJSON schemaImage gen
Try deepseek-v3.2CompareDocs

Specifications

Context window164K tokens
Max output66K tokens
API typechat
AddedMar 19, 2026
Model IDvertex/deepseek-v3.2
Data retentionNo
Used for trainingNo
Provider locationπŸ‡ΊπŸ‡Έ US / πŸ‡ͺπŸ‡Ί EU
Privacy policyVertex AI Data Governance→

Benchmarks

Released 2025-12-01
Coding Indexcoding
36.7%

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

GPQA Diamondreasoning
84.0%

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

Intelligence Indexreasoning
41.7%

Artificial Analysis Intelligence Index β€” 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 β€” always test on your own workload.

Pricing

Input / 1M
$0.56
Output / 1M
$1.68
Cache write / 1M
$1.68
Cache read / 1M
$0.06
Estimated cost
100K input + 10K output$0.0728
1M input + 100K output$0.73
10M input + 1M output$7.28

Requesty charges exactly what the upstream provider charges β€” 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/deepseek-v3.2.

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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/deepseek-v3.2",
    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 deepseek-v3.2 cost?
deepseek-v3.2 is priced at $0.56 per million input tokens and $1.68 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 β€” we don't add markup.
What is the context window of deepseek-v3.2?
deepseek-v3.2 has a context window of 164K tokens, with a maximum output of 66K tokens per response. That's roughly 218 words of input you can fit in a single prompt.
How does deepseek-v3.2 perform on benchmarks?
deepseek-v3.2 scores 92.0% on Math Index, 92.0% on AIME 2025, 90.6% on τ²-Bench. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can deepseek-v3.2 do?
deepseek-v3.2 supports vision input, tool calling, extended reasoning, prompt caching, web search, structured outputs (JSON schema), image generation. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use deepseek-v3.2 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/deepseek-v3.2". The Quickstart above shows Python, JavaScript and cURL snippets.

Access deepseek-v3.2 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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