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DeepSeek-V3.1

輸入:$0.44/M
輸出:$1.32/M
DeepSeek V3.1 是 DeepSeek 的 V-series 升級版本：一款混合式「思考／非思考」大型語言模型，旨在以高吞吐量、低成本提供通用智能與代理式工具使用能力。它保持 OpenAI 風格的 API 相容性，加入更智慧的工具呼叫，並——據該公司表示——實現更快的生成速度與更高的代理可靠性。
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功能
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版本

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 的功能

探索 DeepSeek-V3.1 的核心功能，專為提升效能和可用性而設計。了解這些功能如何為您的專案帶來效益並改善使用者體驗。

DeepSeek-V3.1 的定價

探索 DeepSeek-V3.1 的競爭性定價，專為滿足各種預算和使用需求而設計。我們靈活的方案確保您只需為實際使用量付費，讓您能夠隨著需求增長輕鬆擴展。了解 DeepSeek-V3.1 如何在保持成本可控的同時提升您的專案效果。
彗星價格 (USD / M Tokens)官方價格 (USD / M Tokens)
輸入:$0.44/M
輸出:$1.32/M
輸入:$0.55/M
輸出:$1.65/M

DeepSeek-V3.1 的範例程式碼和 API

存取完整的範例程式碼和 API 資源，以簡化您的 DeepSeek-V3.1 整合流程。我們詳盡的文件提供逐步指引，協助您在專案中充分發揮 DeepSeek-V3.1 的潛力。
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的版本

DeepSeek-V3.1擁有多個快照的原因可能包括：更新後輸出結果存在差異需保留舊版快照以確保一致性、為開發者提供適應與遷移的過渡期，以及不同快照對應全球或區域端點以優化使用者體驗等潛在因素。各版本間的具體差異請參閱官方文件說明。

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