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DeepSeek-R1T2-Chimera

輸入:$0.2416/M
輸出:$0.2416/M
發布時間:Oct 1, 2025

一個 671B 參數的專家混合文本生成模型,由 DeepSeek-AI 的 R1-0528、R1 與 V3-0324 合併而成,支援最長 60k tokens 的上下文。

商業用途

DeepSeek-R1T2-Chimera 的 Playground

探索 DeepSeek-R1T2-Chimera 的 Playground — 一個互動式環境,可測試模型並即時執行查詢。嘗試提示、調整參數,並立即迭代以加速開發並驗證使用案例。

Technical Specifications of deepseek-r1t2-chimera

SpecificationDetails
Model IDdeepseek-r1t2-chimera
Model typeText generation
ArchitectureMixture of Experts (MoE)
Parameter count671B parameters
Context lengthUp to 60,000 tokens
Training / merge basisMerged from DeepSeek-AI's R1-0528, R1, and V3-0324
Primary modalityText
Best forLong-context reasoning, structured generation, summarization, analysis, and general-purpose text completion

What is deepseek-r1t2-chimera?

deepseek-r1t2-chimera is a large-scale 671B parameter Mixture of Experts text generation model available through CometAPI. It is built from a merged model lineage combining DeepSeek-AI's R1-0528, R1, and V3-0324, giving developers a single endpoint designed for advanced text generation and reasoning workloads.

With support for up to 60k tokens of context, deepseek-r1t2-chimera is suitable for applications that need to process long prompts, extended conversations, large documents, or multi-step instructions. It can be used for tasks such as content generation, analytical writing, summarization, classification through prompting, and other natural language workflows where broad context retention matters.

Main features of deepseek-r1t2-chimera

  • 671B MoE architecture: Delivers large-scale generative capability through a Mixture of Experts design aimed at handling complex language tasks efficiently.
  • 60k token context window: Supports long-form interactions and large prompt payloads, making it useful for document-heavy and multi-turn use cases.
  • Merged DeepSeek lineage: Combines elements from DeepSeek-AI's R1-0528, R1, and V3-0324 to provide a unified model endpoint.
  • General-purpose text generation: Suitable for drafting, rewriting, summarizing, extracting structured outputs, and instruction-following workflows.
  • Reasoning-oriented usage: Well suited for prompts that require multi-step analysis, synthesis, and detailed written responses.
  • CometAPI compatibility: Accessible through CometAPI using the stable platform model identifier deepseek-r1t2-chimera.

How to access and integrate deepseek-r1t2-chimera

Step 1: Sign Up for API Key

To get started, create a CometAPI account and generate your API key from the dashboard. Once you have your key, store it securely and use it to authenticate requests to the CometAPI endpoint.

Step 2: Send Requests to deepseek-r1t2-chimera API

After obtaining your API key, send chat completion requests to the CometAPI API using the model ID deepseek-r1t2-chimera.

curl --request POST \
  --url https://api.cometapi.com/v1/chat/completions \
  --header "Authorization: Bearer $COMETAPI_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "deepseek-r1t2-chimera",
    "messages": [
      {
        "role": "user",
        "content": "Write a concise summary of the following article."
      }
    ]
  }'
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_COMETAPI_API_KEY",
    base_url="https://api.cometapi.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-r1t2-chimera",
    messages=[
        {"role": "user", "content": "Write a concise summary of the following article."}
    ]
)

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

Step 3: Retrieve and Verify Results

Once the API returns a response, parse the generated output from the response object and validate it according to your application's needs. For production workflows, you may also want to add retries, output checks, schema validation, and human review for sensitive or high-importance use cases.

DeepSeek-R1T2-Chimera 的定價

探索 DeepSeek-R1T2-Chimera 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-R1T2-Chimera 如何在保持成本可控的同時提升您的專案效果。

Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
輸入:$0.2416/M
輸出:$0.2416/M
輸入:$0.302/M
輸出:$0.302/M
-20%

DeepSeek-R1T2-Chimera 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-R1T2-Chimera 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-R1T2-Chimera 的潛力。

#!/bin/bash
# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here

curl https://api.cometapi.com/v1/chat/completions \
     --header "Authorization: Bearer $COMETAPI_KEY" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "deepseek-r1t2-chimera",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
}'

cURL Code Example

#!/bin/bash
# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here

curl https://api.cometapi.com/v1/chat/completions \
     --header "Authorization: Bearer $COMETAPI_KEY" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "deepseek-r1t2-chimera",
    "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-r1t2-chimera",
    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 COMETAPI_KEY = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const BASE_URL = "https://api.cometapi.com/v1";

const client = new OpenAI({
  apiKey: COMETAPI_KEY,
  baseURL: BASE_URL,
});

async function main() {
  const completion = await client.chat.completions.create({
    model: "deepseek-r1t2-chimera",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "Hello!" },
    ],
  });

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

main();