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deepseek

DeepSeek V3.2 Exp API

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  • Flexible Solution
  • Constant Updates 
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key="<YOUR_API_KEY>",    
)

response = client.chat.completions.create(
    model="DeepSeek-V3.2-Exp",
    messages=[
        {
            "role": "system",
            "content": "You are an AI assistant who knows everything.",
        },
        {
            "role": "user",
            "content": "Tell me, why is the sky blue?"
        },
    ],
)

message = response.choices[0].message.content

print(f"Assistant: {message}")

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deepseek

DeepSeek V3.2 Exp API

DeepSeek V3.2 Exp is an experimental iteration built on the V3.1 “Terminus” lineage and intended as a stepping stone toward DeepSeek’s next-generation architecture. It focuses on efficiency (lower training/inference cost) for long-context workloads while aiming to preserve V3-class output quality. The company positioned it as an intermediate release meant to validate new attention and routing ideas rather than a full generational leap.

Key features

  • DeepSeek Sparse Attention (DSA): a selective attention mechanism that computes attention over a subset of tokens to lower computational cost for long contexts.
  • Two endpoint modes: deepseek-chat (non-thinking/chat) and deepseek-reasoner (generates Chain-of-Thought prior to answers).
  • Release type: Experimental / “intermediate” public release (developer forum + Hugging Face).
  • lower compute, better long-context handling, faster training/inference, and substantially reduced API output costs (company announced a ~50%+ API price cut for this model).

Technical details

DeepSeek Sparse Attention (DSA). DeepSeek V3.2 Exp integrates a sparse-attention mechanism that selectively attends to a subset of prior tokens (rather than full dense self-attention). The release notes and model page emphasize that the training configuration was deliberately aligned with V3.1-Terminus in order to isolate the impact of the sparse mechanism. That alignment lets DeepSeek claim similar benchmark scores while reducing compute and inference.

Benchmark performance

DeepSeek V3.2 Exp

Limitations & risks

  • Experimental status: by name and DeepSeek’s messaging the release is experimental — good for testing and cost-sensitive deployment but not necessarily a drop-in replacement for every production workload.
  • Narrow regressions: small performance dips on some reasoning-heavy benchmarks were reported; developers should validate their specific task suite before switching.

Use cases

  • Cost-sensitive long-context apps: document search, legal or scientific long-document summarization, multi-document retrieval-augmented generation where long context matters.
  • Chatbots at scale: consumer or enterprise chat services that prioritize throughput and price per token.
  • Tooling and automation: code assistants, JSON-structured tool calling and multi-turn workflows where reduced per-token cost helps economics.

Comparison with peer models

  • DeepSeek V3 / R1 (prior DeepSeek releases): DeepSeek V3.2 Exp is iterative — it keeps V3’s MoE strengths but pushes sparsity for efficiency rather than raw capability increases.
  • Anthropic Claude: Claude focuses on safety and instruction alignment; DeepSeek positions DeepSeek V3.2 Exp to win on cost + long-context throughput if parity holds. Organizations choosing between them will trade alignment/safety tooling (Claude) versus cost/long-context throughput (DeepSeek).
  • OpenAI GPT-class (GPT-4 family): GPT offerings emphasize broad safety guardrails and large-scale ecosystem integrations; DeepSeek attempts to compete on affordability and long sequence scaling rather than a one-to-one feature parity on every safety/guardrail metric.

How to call DeepSeek V3.2 Exp API from CometAPI

Model version:

deepseek-v3.2-exp
DeepSeek-V3.2-Exp-nothinking
DeepSeek-V3.2-Exp-thinking

DeepSeek V3.2 Exp API Pricing in CometAPI，20% off the official price:

  • Input Tokens: $0.22/ M tokens
  • Output Tokens: $0.35/ M tokens

Required Steps

  • Log in to cometapi.com. If you are not our user yet, please register first
  • Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
  • Get the url of this site: https://api.cometapi.com/

Use Method

  1. Select the “deepseek-v3.2-exp” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience.
  2. Replace <YOUR_API_KEY> with your actual CometAPI key from your account.
  3. Insert your question or request into the content field—this is what the model will respond to.
  4. Process the API response to get the generated answer.

CometAPI provides a fully compatible REST API—for seamless migration. Key details:

  • Base URL: https://api.cometapi.com/v1/messages
  • Model Names: “ “deepseek-v3.2-exp”or “DeepSeek-V3.2-Exp-nothinking","DeepSeek-V3.2-Exp-thinking”
  • Authentication: Bearer token via Authorization: Bearer YOUR_CometAPI_API_KEY header
  • Content-Type: application/json .

See Also DeepSeek V3.1

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