Home/Models/DeepSeek/DeepSeek-Reasoner
D

DeepSeek-Reasoner

Indtast:$0.44/M
Output:$1.752/M
DeepSeek-Reasoner er DeepSeek’s familie af LLM'er og API-endepunkter med fokus på ræsonnement, designet til at (1) gøre den interne chain-of-thought (CoT)-ræsonnering tilgængelig for klienter og (2) operere i “thinking”-tilstande, der er optimeret til planlægning i flere trin, matematik, kodning og brug af agenter/værktøjer.
Kommersiel brug
Oversigt
Funktioner
Priser
API
Versioner

What is DeepSeek-Reasoner?

DeepSeek-Reasoner is the reasoning (or “thinking”) mode/API name for DeepSeek’s reasoning-first models (currently aligned to the DeepSeek-V3.2 family). It is designed to produce an explicit chain of thought (CoT) before emitting a final answer—i.e., the model intentionally generates internal step-by-step reasoning which is exposed (or can be exposed) through the API so callers can inspect or distill it. DeepSeek positions the reasoner variant as the “thinking” counterpart to its non-thinking chat model and markets it for multi-step reasoning, math, coding and agent workflows.

Main features (user-facing)

  • Explicit Chain-of-Thought (CoT) output. API returns a separate reasoning_content field containing the model’s internal stepwise reasoning alongside the final content. This is designed for inspectability and downstream agent logic.
  • “Thinking” vs “Chat” modes. deepseek-reasoner (thinking mode) is distinct from deepseek-chat (non-thinking mode); both were upgraded to the V3.2 generation.
  • Large context windows. DeepSeek exposes very large context lengths . The Reasoner variants are marketed for long-form reasoning and agent memory.
  • JSON output / structured responses. Support for structured JSON outputs useful for programmatic consumption.
  • Agent/agent-builder focus. V3.2 and the Speciale variant are explicitly described as “reasoning-first models built for agents.”

Technical capabilities

  • Inputs: plain text prompts, structured JSON for tool/agent calls, files or long documents (via long context); tokens are standard NLP tokens.
  • Outputs: API returns both reasoning_content (CoT text) and content (final answer). API clients can request only CoT or only final answer by adjusting max_tokens or response parameters. (Practical note: extracting CoT may still be billable as model output.)
  • DeepSeek has iterated via a reasoning-specialized roadmap: base large models (R1 family) followed by focused post-training / reinforcement learning (RLHF-style) and policy-style fine-tuning to improve reasoning depth. The team also uses distillation to compress reasoning capability into smaller, deployable models.
  • The V3.2 series adds agentic post-training for tool-use, hybrid inference (Think / Non-Think), and optimizations for faster “thinking” iterations.
  • Inference efficiency is aided by a sparse attention method (reports call it DeepSeek Sparse Attention — DSA) that focuses compute on relevant segments rather than full dense attention across very long sequences; this reduces cost for very long contexts.

How to access deepseek-reasoner 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.

img

Step 2: Send Requests to deepseek-reasoner API

Select the “deepseek-reasoner” 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.

Funktioner til DeepSeek-Reasoner

Udforsk de vigtigste funktioner i DeepSeek-Reasoner, designet til at forbedre ydeevne og brugervenlighed. Opdag hvordan disse muligheder kan gavne dine projekter og forbedre brugeroplevelsen.

Priser for DeepSeek-Reasoner

Udforsk konkurrencedygtige priser for DeepSeek-Reasoner, 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-Reasoner kan forbedre dine projekter, mens omkostningerne holdes håndterbare.
Comet-pris (USD / M Tokens)Officiel Pris (USD / M Tokens)Rabat
Indtast:$0.44/M
Output:$1.752/M
Indtast:$0.55/M
Output:$2.19/M
-20%

Eksempelkode og API til DeepSeek-Reasoner

Få adgang til omfattende eksempelkode og API-ressourcer for DeepSeek-Reasoner for at strømline din integrationsproces. Vores detaljerede dokumentation giver trin-for-trin vejledning, der hjælper dig med at udnytte det fulde potentiale af DeepSeek-Reasoner i dine projekter.
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-r1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

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

Versioner af DeepSeek-Reasoner

Årsagen til, at DeepSeek-Reasoner 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-reasoner
deepseek-r1t2-chimera
deepseek-r1
deepseek-r1-0528

Flere modeller