Home/Models/DeepSeek/DeepSeek-V3.1
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DeepSeek-V3.1

入力:$0.44/M
出力:$1.32/M
DeepSeek V3.1 は、DeepSeek の Vシリーズにおけるアップグレードであり、高スループットかつ低コストな汎用知能とエージェント的なツール活用を目指す、ハイブリッド型の「思考／非思考」大規模言語モデルです。OpenAI 互換の API を維持し、より賢いツール呼び出しを追加し、同社によれば、生成の高速化とエージェントの信頼性向上を実現しています。
新着
商用利用
概要
機能
料金プラン
API
バージョン

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.

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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の機能

DeepSeek-V3.1のパフォーマンスと使いやすさを向上させるために設計された主要機能をご紹介します。これらの機能がプロジェクトにどのようなメリットをもたらし、ユーザーエクスペリエンスを改善するかをご確認ください。

DeepSeek-V3.1の料金

DeepSeek-V3.1の競争力のある価格設定をご確認ください。さまざまな予算や利用ニーズに対応できるよう設計されています。柔軟なプランにより、使用した分だけお支払いいただけるため、要件の拡大に合わせて簡単にスケールアップできます。DeepSeek-V3.1がコストを管理しながら、お客様のプロジェクトをどのように強化できるかをご覧ください。
コメット価格 (USD / M Tokens)公式価格 (USD / M Tokens)
入力:$0.44/M
出力:$1.32/M
入力:$0.55/M
出力:$1.65/M

DeepSeek-V3.1のサンプルコードとAPI

DeepSeek-V3.1の包括的なサンプルコードとAPIリソースにアクセスして、統合プロセスを効率化しましょう。詳細なドキュメントでは段階的なガイダンスを提供し、プロジェクトでDeepSeek-V3.1の潜在能力を最大限に活用できるよう支援します。
Python
JavaScript
Curl
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)

DeepSeek-V3.1のバージョン

DeepSeek-V3.1に複数のスナップショットが存在する理由としては、アップデート後の出力変動により旧版スナップショットの一貫性維持が必要な場合、開発者に適応・移行期間を提供するため、グローバル/リージョナルエンドポイントに対応する異なるスナップショットによるユーザー体験最適化などが考えられます。各バージョンの詳細な差異については、公式ドキュメントをご参照ください。

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