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DeepSeek · Chat / LLM · 685B Parameters · 128K ContextQubrid Playground License HuggingFaceStreaming Reasoning Code Long Context Agentic Tool Use Chat

Overview

DeepSeek V3.2 is DeepSeek’s frontier open-source model with 685B total parameters and novel DeepSeek Sparse Attention (DSA) that reduces long-context computational cost by 50%. Trained with a scalable RL framework across 1,800+ agentic environments, it achieves performance comparable to GPT-5 — earning gold-medal results at both the 2025 IMO and IOI. With integrated reasoning and tool-use capabilities through large-scale agentic synthesis, DeepSeek V3.2 represents a landmark in open-source frontier AI. Served instantly via the Qubrid AI Serverless API.
🏆 Gold-medal IMO 2025 & IOI 2025. GPT-5-class performance. Fully open-source. Deploy via Qubrid AI — no H100 cluster required.

Model Specifications

FieldDetails
Model IDdeepseek-ai/DeepSeek-V3.2
ProviderDeepSeek
KindChat / LLM
ArchitectureDeepSeek Sparse Attention (DSA) MoE Transformer — 685B total, 256 experts per layer (8 activated per token), MLA attention
Parameters685B total
Context Length128,000 Tokens
MoENo
Release DateDecember 2025
LicenseMIT
Training DataLarge-scale diverse corpus + RL post-training with 1,800+ agentic environments and 85,000 complex prompts
Function CallingNot Supported
Image SupportN/A
Serverless APIAvailable
Fine-tuningComing Soon
On-demandComing Soon
State🟢 Ready

Pricing

💳 Access via the Qubrid AI Serverless API with pay-per-token pricing. No infrastructure management required.
Token TypePrice per 1M Tokens
Input Tokens$0.56
Input Tokens (Cached)$0.28
Output Tokens$1.68

Quickstart

Prerequisites

  1. Create a free account at platform.qubrid.com
  2. Generate your API key from the API Keys section
  3. Replace QUBRID_API_KEY in the code below with your actual key
💡 Temperature note: Use 1.0 (default) for optimal performance with this model.

Python

from openai import OpenAI

# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
    base_url="https://platform.qubrid.com/v1",
    api_key="QUBRID_API_KEY",
)

# Create a streaming chat completion
stream = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V3.2",
    messages=[
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms"
      }
    ],
    max_tokens=8192,
    temperature=1,
    top_p=0.95,
    stream=True
)

# If stream = False comment this out
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print("\n")

# If stream = True comment this out
print(stream.choices[0].message.content)

JavaScript

import OpenAI from "openai";

// Initialize the OpenAI client with Qubrid base URL
const client = new OpenAI({
  baseURL: "https://platform.qubrid.com/v1",
  apiKey: "QUBRID_API_KEY",
});

// Create a streaming chat completion
const stream = await client.chat.completions.create({
  model: "deepseek-ai/DeepSeek-V3.2",
  messages: [
    {
      role: "user",
      content: "Explain quantum computing in simple terms",
    },
  ],
  max_tokens: 8192,
  temperature: 1,
  top_p: 0.95,
  stream: true,
});

// If stream = false comment this out
for await (const chunk of stream) {
  if (chunk.choices[0]?.delta?.content) {
    process.stdout.write(chunk.choices[0].delta.content);
  }
}
console.log("\n");

// If stream = true comment this out
console.log(stream.choices[0].message.content);

Go

package main

import (
	"bufio"
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
)

func main() {
	url := "https://platform.qubrid.com/v1/chat/completions"

	data := map[string]interface{}{
		"model": "deepseek-ai/DeepSeek-V3.2",
		"messages": []map[string]string{
			{
				"role":    "user",
				"content": "Explain quantum computing in simple terms",
			},
		},
		"temperature": 1,
		"max_tokens":  8192,
		"stream":      true,
		"top_p":       0.95,
	}

	jsonData, _ := json.Marshal(data)
	req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
	req.Header.Set("Authorization", "Bearer QUBRID_API_KEY")
	req.Header.Set("Content-Type", "application/json")

	client := &http.Client{}
	res, _ := client.Do(req)
	defer res.Body.Close()

	scanner := bufio.NewScanner(res.Body)
	for scanner.Scan() {
		line := scanner.Text()
		if line != "" {
			fmt.Println(line)
		}
	}
}

cURL

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "deepseek-ai/DeepSeek-V3.2",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 1,
  "max_tokens": 8192,
  "stream": true,
  "top_p": 0.95
}'

Live Example

Prompt: Explain quantum computing in simple terms
Response:
Quantum computing is a fundamentally different way of processing information
— one that exploits the laws of quantum mechanics rather than classical physics.

Classical computers store data as bits: each bit is either 0 or 1.

Quantum computers use qubits, which can exist as 0, 1, or any combination
of both simultaneously — a property called superposition. This allows a
quantum computer to evaluate an enormous number of possible states at once,
rather than one at a time.

Two key properties make this possible:

  Superposition   — a qubit holds multiple values simultaneously until measured
  Entanglement    — two qubits can be correlated so that the state of one
                    instantly determines the other, enabling massively
                    parallel computation across linked qubit pairs

Where quantum computers outperform classical machines:

  • Cryptography        — breaking and constructing encryption systems
  • Drug discovery      — simulating molecular structures at atomic resolution
  • Optimization        — solving logistics, scheduling, and financial problems
  • AI acceleration     — certain training and inference workloads

The important caveat: quantum computers aren't general-purpose replacements
for classical computers. They are precision instruments — extraordinarily
powerful for specific problem classes that are intractable for even the
fastest supercomputers today.
Try it yourself in the Qubrid AI Playground →

Playground Features

The Qubrid AI Playground lets you interact with DeepSeek V3.2 directly in your browser — no setup, no code, no cost to explore.

🧠 System Prompt

Define the model’s role, reasoning depth, and output constraints before the conversation begins — essential for agentic workflows, structured analysis, and complex multi-turn tasks.
Example: "You are an expert software architect. Analyze problems from first
principles, propose solutions with trade-offs clearly articulated,
and always output code in production-ready style with inline comments."
Set your system prompt once in the Qubrid Playground and it applies across every turn of the conversation.

🎯 Few-Shot Examples

Guide the model’s reasoning approach and output format with concrete examples — no fine-tuning, no retraining required.
User InputAssistant Response
Prove that there are infinitely many prime numbersAssume finitely many primes p₁...pₙ. Let N = (p₁×p₂×...×pₙ) + 1. N is either prime (contradiction) or divisible by a prime not in our list (contradiction). Therefore infinitely many primes exist. ∎
Write a binary search in Pythondef binary_search(arr, target): l, r = 0, len(arr)-1 \n while l <= r: \n mid = (l+r)//2 \n if arr[mid] == target: return mid \n elif arr[mid] < target: l = mid+1 \n else: r = mid-1 \n return -1
💡 Stack multiple few-shot examples in the Qubrid Playground to dial in reasoning depth, output format, and domain focus — no fine-tuning required.

Inference Parameters

ParameterTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output
Temperaturenumber1Recommended 1.0 for optimal performance
Max Tokensnumber8192Maximum number of tokens to generate
Top Pnumber0.95Controls nucleus sampling

Use Cases

  1. Advanced reasoning and agent tasks
  2. Long-horizon agentic tool use
  3. Mathematical competition problems (IMO/IOI level)
  4. Code generation and complex debugging
  5. Enterprise automation
  6. Long-context document analysis

Strengths & Limitations

StrengthsLimitations
DeepSeek Sparse Attention — 50% compute savings on long contexts128K max context window
GPT-5-class performance on reasoning benchmarksRequires H100/H200 class infrastructure for full self-hosting
Gold-medal IMO 2025 and IOI 2025 performanceNo official Jinja chat template — custom encoding required
685B MoE with efficient inference (8 experts activated per token)Tool calling may need warm-up on cold-start phases
Integrated reasoning into tool-use via RL synthesisFunction calling not supported via API
MIT License — fully open source

Why Qubrid AI?

  • 🚀 No infrastructure setup — 685B MoE served serverlessly, pay only for what you use
  • 🔁 OpenAI-compatible — drop-in replacement using the same SDK, just swap the base URL
  • 💰 Cached input pricing — $0.28/1M for cached tokens, dramatically reducing costs on repeated long contexts
  • 🧪 Built-in Playground — prototype with system prompts and few-shot examples instantly at platform.qubrid.com
  • 📊 Full observability — API logs and usage tracking built into the Qubrid dashboard
  • 🌐 Multi-language support — Python, JavaScript, Go, cURL out of the box

Resources

ResourceLink
📖 Qubrid Docsdocs.platform.qubrid.com
🎮 PlaygroundTry DeepSeek V3.2 live
🔑 API KeysGet your API Key
🤗 Hugging Facedeepseek-ai/DeepSeek-V3.2
💬 DiscordJoin the Qubrid Community

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