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DeepSeek-V3.2-Exp API
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DeepSeek-V3.2-Exp is an experimental model that introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism designed to dramatically improve training and inference efficiency in long-context scenarios. Built on V3.1-Terminus, this model achieves substantial computational efficiency gains while maintaining virtually identical output quality and performance across diverse benchmarks including reasoning, coding, mathematics, and agentic tasks.
DeepSeek-V3.2-Exp API Usage
Endpoint
curl -X POST "https://api.together.xyz/v1/chat/completions" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp",
"messages": [
{
"role": "user",
"content": "What are some fun things to do in New York?"
}
]
}'
curl -X POST "https://api.together.xyz/v1/images/generations" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp",
"prompt": "Draw an anime style version of this image.",
"width": 1024,
"height": 768,
"steps": 28,
"n": 1,
"response_format": "url",
"image_url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
}'
curl -X POST https://api.together.xyz/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-d '{
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Describe what you see in this image."},
{"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
]
}],
"max_tokens": 512
}'
curl -X POST https://api.together.xyz/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-d '{
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp",
"messages": [{
"role": "user",
"content": "Given two binary strings `a` and `b`, return their sum as a binary string"
}]
}'
curl -X POST https://api.together.xyz/v1/rerank \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-d '{
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp",
"query": "What animals can I find near Peru?",
"documents": [
"The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
"The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
"The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
"The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
],
"top_n": 2
}'
curl -X POST https://api.together.xyz/v1/embeddings \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "Our solar system orbits the Milky Way galaxy at about 515,000 mph.",
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp"
}'
curl -X POST https://api.together.xyz/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-d '{
"model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
"prompt": "A horse is a horse",
"max_tokens": 32,
"temperature": 0.1,
"safety_model": "DeepSeek-AI/DeepSeek-V3-2-Exp"
}'
curl --location 'https://api.together.ai/v1/audio/generations' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $TOGETHER_API_KEY' \
--output speech.mp3 \
--data '{
"input": "Today is a wonderful day to build something people love!",
"voice": "helpful woman",
"response_format": "mp3",
"sample_rate": 44100,
"stream": false,
"model": "DeepSeek-AI/DeepSeek-V3-2-Exp"
}'
curl -X POST "https://api.together.xyz/v1/audio/transcriptions" \
-H "Authorization: Bearer $TOGETHER_API_KEY" \
-F "model=DeepSeek-AI/DeepSeek-V3-2-Exp" \
-F "language=en" \
-F "response_format=json" \
-F "timestamp_granularities=segment"
from together import Together
client = Together()
response = client.chat.completions.create(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
messages=[
{
"role": "user",
"content": "What are some fun things to do in New York?"
}
]
)
print(response.choices[0].message.content)
from together import Together
client = Together()
imageCompletion = client.images.generate(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
width=1024,
height=768,
steps=28,
prompt="Draw an anime style version of this image.",
image_url="https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
)
print(imageCompletion.data[0].url)
from together import Together
client = Together()
response = client.chat.completions.create(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Describe what you see in this image."},
{"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
]
}]
)
print(response.choices[0].message.content)
from together import Together
client = Together()
response = client.chat.completions.create(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
messages=[
{
"role": "user",
"content": "Given two binary strings `a` and `b`, return their sum as a binary string"
}
],
)
print(response.choices[0].message.content)
from together import Together
client = Together()
query = "What animals can I find near Peru?"
documents = [
"The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
"The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
"The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
"The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations.",
]
response = client.rerank.create(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
query=query,
documents=documents,
top_n=2
)
for result in response.results:
print(f"Relevance Score: {result.relevance_score}")
from together import Together
client = Together()
response = client.embeddings.create(
model = "DeepSeek-AI/DeepSeek-V3-2-Exp",
input = "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
)
from together import Together
client = Together()
response = client.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
prompt="A horse is a horse",
max_tokens=32,
temperature=0.1,
safety_model="DeepSeek-AI/DeepSeek-V3-2-Exp",
)
print(response.choices[0].text)
from together import Together
client = Together()
speech_file_path = "speech.mp3"
response = client.audio.speech.create(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
input="Today is a wonderful day to build something people love!",
voice="helpful woman",
)
response.stream_to_file(speech_file_path)
from together import Together
client = Together()
response = client.audio.transcribe(
model="DeepSeek-AI/DeepSeek-V3-2-Exp",
language="en",
response_format="json",
timestamp_granularities="segment"
)
print(response.text)
import Together from 'together-ai';
const together = new Together();
const completion = await together.chat.completions.create({
model: 'DeepSeek-AI/DeepSeek-V3-2-Exp',
messages: [
{
role: 'user',
content: 'What are some fun things to do in New York?'
}
],
});
console.log(completion.choices[0].message.content);
import Together from "together-ai";
const together = new Together();
async function main() {
const response = await together.images.create({
model: "DeepSeek-AI/DeepSeek-V3-2-Exp",
width: 1024,
height: 1024,
steps: 28,
prompt: "Draw an anime style version of this image.",
image_url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
});
console.log(response.data[0].url);
}
main();
import Together from "together-ai";
const together = new Together();
const imageUrl = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png";
async function main() {
const response = await together.chat.completions.create({
model: "DeepSeek-AI/DeepSeek-V3-2-Exp",
messages: [{
role: "user",
content: [
{ type: "text", text: "Describe what you see in this image." },
{ type: "image_url", image_url: { url: imageUrl } }
]
}]
});
console.log(response.choices[0]?.message?.content);
}
main();
import Together from "together-ai";
const together = new Together();
async function main() {
const response = await together.chat.completions.create({
model: "DeepSeek-AI/DeepSeek-V3-2-Exp",
messages: [{
role: "user",
content: "Given two binary strings `a` and `b`, return their sum as a binary string"
}]
});
console.log(response.choices[0]?.message?.content);
}
main();
import Together from "together-ai";
const together = new Together();
const query = "What animals can I find near Peru?";
const documents = [
"The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
"The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
"The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
"The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
];
async function main() {
const response = await together.rerank.create({
model: "DeepSeek-AI/DeepSeek-V3-2-Exp",
query: query,
documents: documents,
top_n: 2
});
for (const result of response.results) {
console.log(`Relevance Score: ${result.relevance_score}`);
}
}
main();
import Together from "together-ai";
const together = new Together();
const response = await client.embeddings.create({
model: 'DeepSeek-AI/DeepSeek-V3-2-Exp',
input: 'Our solar system orbits the Milky Way galaxy at about 515,000 mph',
});
import Together from "together-ai";
const together = new Together();
async function main() {
const response = await together.completions.create({
model: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
prompt: "A horse is a horse",
max_tokens: 32,
temperature: 0.1,
safety_model: "DeepSeek-AI/DeepSeek-V3-2-Exp"
});
console.log(response.choices[0]?.text);
}
main();
import Together from 'together-ai';
const together = new Together();
async function generateAudio() {
const res = await together.audio.create({
input: 'Today is a wonderful day to build something people love!',
voice: 'helpful woman',
response_format: 'mp3',
sample_rate: 44100,
stream: false,
model: 'DeepSeek-AI/DeepSeek-V3-2-Exp',
});
if (res.body) {
console.log(res.body);
const nodeStream = Readable.from(res.body as ReadableStream);
const fileStream = createWriteStream('./speech.mp3');
nodeStream.pipe(fileStream);
}
}
generateAudio();
import Together from "together-ai";
const together = new Together();
const response = await together.audio.transcriptions.create(
model: "DeepSeek-AI/DeepSeek-V3-2-Exp",
language: "en",
response_format: "json",
timestamp_granularities: "segment"
});
console.log(response)
How to use DeepSeek-V3.2-Exp
Model details
Architecture Overview:
• 685B total parameters with Mixture-of-Experts (MoE) architecture
• Multi-Latent Attention (MLA) with MQA mode for efficient key-value sharing
• 128K token context window with extended long-context capabilities
• DeepSeek Sparse Attention (DSA) featuring a lightning indexer and fine-grained token selection
Training Methodology:
• Continued pre-training from DeepSeek-V3.1-Terminus base checkpoint
• Two-stage training: dense warm-up (2.1B tokens) followed by sparse training (943.7B tokens)
• Lightning indexer trained with KL-divergence alignment to main attention distribution
• Post-training includes specialist distillation across mathematics, coding, reasoning, and agentic domains
• Group Relative Policy Optimization (GRPO) for reinforcement learning alignment
Performance Benchmarks:
DeepSeek-V3.2-Exp demonstrates performance on par with V3.1-Terminus across comprehensive evaluations:
Benchmark | DeepSeek-V3.1-Terminus | DeepSeek-V3.2-Exp |
---|---|---|
Reasoning Mode (General) | ||
MMLU-Pro | 85.0 | 85.0 |
GPQA-Diamond | 80.7 | 79.9 |
Humanity's Last Exam | 21.7 | 19.8 |
Code | ||
LiveCodeBench | 74.9 | 74.1 |
Codeforces-Div1 | 2046 | 2121 |
Aider-Polyglot | 76.1 | 74.5 |
Math | ||
AIME 2025 | 88.4 | 89.3 |
HMMT 2025 | 86.1 | 83.6 |
Agentic Tool Use | ||
BrowseComp | 38.5 | 40.1 |
BrowseComp-zh | 45.0 | 47.9 |
SimpleQA | 96.8 | 97.1 |
SWE Verified | 68.4 | 67.8 |
SWE-bench Multilingual | 57.8 | 57.9 |
Terminal-bench | 36.7 | 37.7 |
Efficiency Characteristics:
• Reduces core attention complexity from O(L²) to O(Lk) where k≪L
• Up to 70% cost reduction for long-context inference at 128K tokens
• Selects 2048 key-value tokens per query token during sparse attention
• Optimized for H800, H200, MI350, and NPU deployments with specialized kernels
Prompting DeepSeek-V3.2-Exp
Applications & Use Cases
Long-Context Processing:
• Extended document analysis and summarization up to 128K tokens
• Multi-document question answering and information synthesis
• Legal document review and contract analysis
• Research paper analysis and literature review automation
Code & Development:
• Software engineering tasks with large codebase context (SWE-bench: 67.8%)
• Multi-file code generation and refactoring (Aider-Polyglot: 74.5%)
• Competitive programming with advanced algorithms (Codeforces: 2121 rating)
• Terminal and command-line task automation
Reasoning & Mathematics:
• Advanced mathematical problem solving (AIME 2025: 89.3%, HMMT 2025: 83.6%)
• Multi-step logical reasoning and proof generation
• Scientific research assistance and hypothesis generation
• STEM education and tutoring applications
Agentic Applications:
• Web search and browsing agents (BrowseComp: 40.1%)
• Automated information gathering and fact-checking (SimpleQA: 97.1%)
• Task automation and workflow orchestration
• Multi-step planning and execution with tool use
Model Provider:
DeepSeek
Type:
Code
Variant:
Parameters:
685B
Deployment:
✔ Serverless
✔ On-Demand Dedicated
✔ Monthly Reserved
Quantization
Context length:
128K
Pricing:
Check pricing
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Quickstart docs
Quickstart docs
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