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Pulses Integrates DeepSeek-R1 Model to Enhance AI-Powered Recruitment Matching Efficiency

Professional social networking platform, Pulse, recently announced the successful integration of the DeepSeek-R1 model. This move aims to further optimize candidate-job matching efficiency and comprehensively elevate the intelligence level of the recruitment process. Reportedly, Pulse began deploying the model in early February of this year and has completed relevant testing. Currently, AI technology deeply empowers Pulse's overall recruitment workflow, achieving significant efficiency improvements in several key areas, including job signal capture, application filtering, automatic follow-up questions, and intelligent greetings. In the talent assessment phase, the system can dynamically track user behavior and analyze it.

10.2k 11 hours ago
Pulses Integrates DeepSeek-R1 Model to Enhance AI-Powered Recruitment Matching Efficiency

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GPT-4.1 mini

Openai

GPT-4.1 mini

$2.8

Input tokens/M

$11.2

Output tokens/M

1k

Context Length

Claude 3 Sonnet

Anthropic

Claude 3 Sonnet

$21

Input tokens/M

$105

Output tokens/M

200

Context Length

GPT-5 mini

Openai

GPT-5 mini

$1.75

Input tokens/M

$14

Output tokens/M

400

Context Length

o4-mini

Openai

o4-mini

$7.7

Input tokens/M

$30.8

Output tokens/M

200

Context Length

GPT-4o mini

Openai

GPT-4o mini

$1.05

Input tokens/M

$4.2

Output tokens/M

128

Context Length

Gemma 2 27B

Google

Gemma 2 27B

-

Input tokens/M

-

Output tokens/M

-

Context Length

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