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DeepSeek-V3.1

Indtast:$0.44/M
Output:$1.32/M
Udgivet:Oct 1, 2025

DeepSeek V3.1 er opgraderingen i DeepSeeks V-serie: en hybrid “tænkende / ikke-tænkende” stor sprogmodel, der er rettet mod generel intelligens med høj gennemstrømning og lave omkostninger samt agentbaseret værktøjsbrug. Den bevarer OpenAI-stil API-kompatibilitet, tilføjer smartere værktøjskald og — ifølge virksomheden — leverer hurtigere generering og forbedret agentpålidelighed.

Ny
Kommersiel brug

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.

Priser for DeepSeek-V3.1

Udforsk konkurrencedygtige priser for DeepSeek-V3.1, designet til at passe til forskellige budgetter og brugsbehov. Vores fleksible planer sikrer, at du kun betaler for det, du bruger, hvilket gør det nemt at skalere, efterhånden som dine krav vokser. Opdag hvordan DeepSeek-V3.1 kan forbedre dine projekter, mens omkostningerne holdes håndterbare.

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

Eksempelkode og API til DeepSeek-V3.1

Få adgang til omfattende eksempelkode og API-ressourcer for DeepSeek-V3.1 for at strømline din integrationsproces. Vores detaljerede dokumentation giver trin-for-trin vejledning, der hjælper dig med at udnytte det fulde potentiale af DeepSeek-V3.1 i dine projekter.

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);

Versioner af DeepSeek-V3.1

Årsagen til, at DeepSeek-V3.1 har flere øjebliksbilleder kan omfatte potentielle faktorer såsom variationer i output efter opdateringer, der kræver ældre øjebliksbilleder for konsistens, at give udviklere en overgangsperiode til tilpasning og migration, og at forskellige øjebliksbilleder svarer til globale eller regionale slutpunkter for at optimere brugeroplevelsen. For detaljerede forskelle mellem versioner, henvises der til den officielle dokumentation.

Version
deepseek-v3.1