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

Overview

DeepSeek V3.2 is DeepSeekโ€™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 โ€” 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.
๐Ÿ† Gold-medal IMO 2025 & IOI 2025. GPT-5-class performance. Fully open-source. Deploy via Qubrid AI โ€” no H100 cluster required.

Model Specifications

FieldDetails
Model IDdeepseek-ai/DeepSeek-V3.2
ProviderDeepSeek
KindChat / LLM
ArchitectureDeepSeek Sparse Attention (DSA) MoE Transformer โ€” 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
State๐ŸŸข Ready

Pricing

๐Ÿ’ณ 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
๐Ÿ’ก 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
โ€” 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 โ€” 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   โ€” a qubit holds multiple values simultaneously until measured
  Entanglement    โ€” 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:

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

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

Playground Features

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

๐Ÿง  System Prompt

Define the modelโ€™s role, reasoning depth, and output constraints before the conversation begins โ€” 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.

๐ŸŽฏ Few-Shot Examples

Guide the modelโ€™s reasoning approach and output format with concrete examples โ€” no fine-tuning, no retraining required.
User InputAssistant Response
Prove that there are infinitely many prime numbersAssume finitely many primes pโ‚...pโ‚™. Let N = (pโ‚ร—pโ‚‚ร—...ร—pโ‚™) + 1. N is either prime (contradiction) or divisible by a prime not in our list (contradiction). Therefore infinitely many primes exist. โˆŽ
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
๐Ÿ’ก Stack multiple few-shot examples in the Qubrid Playground to dial in reasoning depth, output format, and domain focus โ€” 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 โ€” 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 โ€” 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 โ€” fully open source

Why Qubrid AI?

  • ๐Ÿš€ No infrastructure setup โ€” 685B MoE served serverlessly, pay only for what you use
  • ๐Ÿ” OpenAI-compatible โ€” drop-in replacement using the same SDK, just swap the base URL
  • ๐Ÿ’ฐ Cached input pricing โ€” $0.28/1M for cached tokens, dramatically reducing costs on repeated long contexts
  • ๐Ÿงช Built-in Playground โ€” prototype with system prompts and few-shot examples instantly at platform.qubrid.com
  • ๐Ÿ“Š Full observability โ€” API logs and usage tracking built into the Qubrid dashboard
  • ๐ŸŒ Multi-language support โ€” Python, JavaScript, Go, cURL out of the box

Resources

ResourceLink
๐Ÿ“– Qubrid Docsdocs.platform.qubrid.com
๐ŸŽฎ PlaygroundTry DeepSeek V3.2 live
๐Ÿ”‘ API KeysGet your API Key
๐Ÿค— Hugging Facedeepseek-ai/DeepSeek-V3.2
๐Ÿ’ฌ DiscordJoin the Qubrid Community

Built with โค๏ธ by Qubrid AI

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