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DeepSeek-V3.1

입력:$0.44/M
출력:$1.32/M
출시일:Oct 1, 2025

DeepSeek의 V-시리즈 업그레이드인 DeepSeek V3.1은 고처리량·저비용의 범용 지능과 에이전트 기반 도구 사용을 목표로 하는 ‘사고/비사고’ 하이브리드 대규모 언어 모델이다. OpenAI 스타일 API 호환성을 유지하고 더 지능적인 도구 호출을 추가했으며—회사 측에 따르면—더 빠른 생성과 향상된 에이전트 신뢰성을 제공한다.

새로운
상업적 사용

Basic features (what it offers)

  • Dual inference modes: deepseek-chat (non-thinking / faster) and deepseek-reasoner (thinking / stronger chain-of-thought/agent skills). The UI exposes a “DeepThink” toggle for end users.
  • Long context: official materials and community reports emphasize a 128k token context window for the V3 family lineage. This enables end-to-end processing of very long documents.
  • Improved tool/agent handling: post-training optimization targeted at reliable tool calling, multi-step agent workflows, and plugin/tool integrations.

Technical details (architecture, training, and implementation)

Training corpus & long-context engineering. The Deepseek V3.1 update emphasizes a two-phase long-context extension on top of earlier V3 checkpoints: public notes indicate major additional tokens devoted to 32k and 128k extension phases (DeepSeek reports hundreds of billions of tokens used in the extension steps). The release also updated the tokenizer configuration to support the larger context regimes.

Model size and micro-scaling for inference. Public and community reports give somewhat different parameter tallies (a result common to new releases): third-party indexers and mirrors list ~671B parameters (37B active) in some runtime descriptions, while other community summaries report ~685B as the hybrid reasoning architecture’s nominal size.

Inference modes & engineering tradeoffs. Deepseek V3.1 exposes two pragmatic inference modes: deepseek-chat (optimized for standard turn-based chat, lower latency) and deepseek-reasoner (a “thinking” mode that prioritizes chain-of-thought and structured reasoning).

Limitations & risks

  • Benchmark maturity & reproducibility: many performance claims are early, community-driven, or selective. Independent, standardized evaluations are still catching up. (Risk: overclaiming).
  • Safety & hallucination: like all large LLMs, Deepseek V3.1 is subject to hallucination and harmful-content risks; stronger reasoning modes can sometimes produce confident but incorrect multi-step outputs. Users should apply safety layers and human review on critical outputs. (No vendor or independent source claims elimination of hallucination.)
  • Inference cost & latency: the reasoning mode trades latency for capability; for large-scale consumer inference this adds cost. Some commentators note that the market reaction to open, cheap, high-speed models can be volatile.

Common & compelling use cases

Long-document analysis & summarization: law, R\&D, literature reviews — leverage the 128k token window for end-to-end summaries.

Agent workflows and tool orchestration: automations that require multi-step tool calls (APIs, search, calculators). Deepseek V3.1’s post-training agent tuning is intended to improve reliability here.

Code generation & software assistance: early benchmark reports emphasize strong programming performance; suitable for pair-programming, code review, and generation tasks with human oversight.

Enterprise deployment where cost/latency choice matters: choose chat mode for cheap/faster conversational assistants and reasoner for offline or premium deep reasoning tasks.

How to access deepseek-v3.1 API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. 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.

Step 2: Send Requests to deepseek-v3.1 API

Select the “deepseek-v3.1” 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. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. base url is Chat format.

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

DeepSeek-V3.1 가격

[모델명]의 경쟁력 있는 가격을 살펴보세요. 다양한 예산과 사용 요구에 맞게 설계되었습니다. 유연한 요금제로 사용한 만큼만 지불하므로 요구사항이 증가함에 따라 쉽게 확장할 수 있습니다. [모델명]이 비용을 관리 가능한 수준으로 유지하면서 프로젝트를 어떻게 향상시킬 수 있는지 알아보세요.

ModelComet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
deepseek-v3.1
입력:$0.44/M
출력:$1.32/M
입력:$0.55/M
출력:$1.65/M
-20%

DeepSeek-V3.1의 샘플 코드 및 API

[모델 이름]의 포괄적인 샘플 코드와 API 리소스에 액세스하여 통합 프로세스를 간소화하세요. 자세한 문서는 단계별 가이드를 제공하여 프로젝트에서 [모델 이름]의 모든 잠재력을 활용할 수 있도록 돕습니다.

curl https://api.cometapi.com/v1/chat/completions \
     --header "Authorization: Bearer $COMETAPI_KEY" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "deepseek-v3.1",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
}'

cURL Code Example

curl https://api.cometapi.com/v1/chat/completions \
     --header "Authorization: Bearer $COMETAPI_KEY" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "deepseek-v3.1",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
}'

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

completion = client.chat.completions.create(
    model="deepseek-v3.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

print(completion.choices[0].message.content)

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
const api_key = process.env.COMETAPI_KEY;
const base_url = "https://api.cometapi.com/v1";

const openai = new OpenAI({
  apiKey: api_key,
  baseURL: base_url,
});

const completion = await openai.chat.completions.create({
  model: "deepseek-v3.1",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Hello!" },
  ],
});

console.log(completion.choices[0].message.content);

DeepSeek-V3.1의 버전

DeepSeek-V3.1에 여러 스냅샷이 존재하는 이유는 업데이트 후 출력 변동으로 인해 일관성을 유지하기 위해 이전 스냅샷을 보관하거나, 개발자에게 적응 및 마이그레이션을 위한 전환 기간을 제공하거나, 글로벌 또는 지역별 엔드포인트에 따라 다양한 스냅샷을 제공하여 사용자 경험을 최적화하기 위한 것 등이 포함될 수 있습니다. 버전 간 상세한 차이점은 공식 문서를 참고해 주시기 바랍니다.

Version
deepseek-v3.1