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DeepSeek: DeepSeek V3.2 Exp: AI model

DeepSeek V3.2 Exp is an experimental, intermediate step between V3.1 and future architectures, introducing sparse attention (DSA) for greater efficiency over long contexts, via Hormouz.

Context window:
163,840 tokens
Input:
$0.27/1M tokens
Output:
$0.41/1M tokens
Type:
text
  • Tools
  • Streaming
  • Long context

Thinking levels:

DeepSeek V3.2 Exp is an experimental language model released as an intermediate step between DeepSeek V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), designed to improve training and inference efficiency in long-context scenarios while preserving output quality.

The model was trained under conditions aligned with V3.1 Terminus to enable a direct comparison; benchmarking shows performance roughly on par with V3.1 across reasoning, coding and tool use. The release focuses on validating architectural optimizations for extended context lengths rather than raising raw accuracy, which makes it primarily a research-oriented model. The reasoning behaviour can be controlled with the reasoning parameter.

Pricing is usage-based: $0.27/M in · $0.41/M out. Hormouz provides stable access to the model from inside Iran, with no foreign credit card or circumvention tooling required.

DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...

Quickstart

curl https://api.hormouz.net/v1/chat/completions \
  -H "Authorization: Bearer ng-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/deepseek-v3.2-exp",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Specs

VendorDeepSeek
TypeText
Context window163,840 tokens
Pricing$0.27/M in · $0.41/M out
APIChat Completions
CapabilitiesStreaming · Tools

Benchmarks

Reasoning

GPQA Diamond
Graduate-level scientific reasoning
73.8%
HLE
Humanity's Last Exam
9%
IFBench
Instruction-following
43.1%
AA-LCR
Long-context reasoning
44.7%
CritPt
Research-level physics reasoning
1.4%
AA Omniscience
Broad factual knowledge accuracy
22.8%
Non-Hallucination
Rate of avoiding fabricated answers
7.9%

Coding

Terminal-Bench Hard
Agentic coding & terminal use
25%

Agentic

τ²-Bench
Tool-use agents in dual-control scenarios
33.9%

Source: Artificial Analysis

3D
Top 48%
1,159Elo
Win rate: 56.4%Avg time: 152s
Code
Top 49%
1,177Elo
Win rate: 54.2%Avg time: 198s
Data Viz
Top 61%
1,161Elo
Win rate: 50.6%Avg time: 165s
Game Dev
Top 54%
1,157Elo
Win rate: 53%Avg time: 228s
SVG
Top 88%
1,031Elo
Win rate: 42%Avg time: 54.9s
UI Component
Top 50%
1,173Elo
Win rate: 53.3%Avg time: 138s
Website
Top 50%
1,185Elo
Win rate: 54.3%Avg time: 203s

Metrics sourced from Design Arena

Code example

cURL
curl https://api.hormouz.net/v1/chat/completions \
  -H "Authorization: Bearer ng-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/deepseek-v3.2-exp",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
Python
from openai import OpenAI
client = OpenAI(api_key="ng-YOUR_API_KEY", base_url="https://api.hormouz.net/v1")
response = client.chat.completions.create(
    model="deepseek/deepseek-v3.2-exp",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
JavaScript
import OpenAI from "openai";
const client = new OpenAI({ apiKey: "ng-YOUR_API_KEY", baseURL: "https://api.hormouz.net/v1" });
const response = await client.chat.completions.create({
  model: "deepseek/deepseek-v3.2-exp",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);

Buy the DeepSeek: DeepSeek V3.2 Exp API, paid in rial

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  • One Hormouz key reaches DeepSeek: DeepSeek V3.2 Exp and 600+ other models, and you pay only for what you use.
  • No VPN and no risk of a banned account; just change the base URL to api.hormouz.net/v1.
Create an accountBuy DeepSeek API access, paid in rial

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FAQ

How much does this model cost?
Billing is usage-based: $0.27 per million input tokens and $0.41 per million output tokens. There is no fixed monthly fee. You pay only for what you use.
Is DeepSeek V3.2 Exp an experimental model?
Yes. It is released as an intermediate step between V3.1 and future architectures, focused on validating architectural optimizations (such as sparse attention) for long contexts.
How does DeepSeek V3.2 Exp perform?
Benchmarking shows performance roughly on par with DeepSeek V3.1 across reasoning, coding and tool use, with minor differences by domain.
How do I access this model from inside Iran?
Create an account on Hormouz and generate an API key. Hormouz provides a stable path to the model from inside Iran, with no foreign credit card required.
Is this model compatible with OpenAI code?
Yes. Hormouz exposes an OpenAI-compatible API. Just change the base URL to Hormouz and swap in your Hormouz key, and your existing SDKs run without a rewrite.

Updated 2026-10-13