Deepseek R1 Distill Qwen 32B

DeepSeek-R1 is the first-generation inference model built on DeepSeek-V3 (with a total of 671 billion parameters and 37 billion activated parameters per token). It combines large-scale reinforcement learning (RL) to enhance chain-of-thought and reasoning abilities, and performs excellently in mathematics, code, and multi-step reasoning tasks.

Intelligence(Medium)

Speed(Slow)

Input Supported Modalities

Is Reasoning Model

128,000

Context Window

128,000

Maximum Output Tokens

Knowledge Cutoff

Go Compare

Pricing

￥0.86 /M tokens

Input

￥1.3 /M tokens

Output

￥2.16 /M tokens

Blended Price

Quick Simple Comparison

Input

Output

DeepSeek-Coder-V2

DeepSeek V3 0324 (Mar' 25)

DeepSeek V3 0324 (Mar '25)

Basic Parameters

DeepSeek R1 Distill Qwen 32BTechnical Parameters

Parameter Count

32,800.0M

Context Length

128.00k tokens

Training Data Cutoff

Open Source Category

Open Weights (Permissive License)

Multimodal Support

Text Only

Throughput

Release Date

2025-01-20

Response Speed

40.50,095 tokens/s

Benchmark Scores

Below is the performance of DeepSeek R1 Distill Qwen 32B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

51.9

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

32.28

Indicator of AI model performance on coding tasks

Math Index

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

81.37

Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks

MMLU Pro

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

73.9

Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video

GPQA

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

61.5

Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions

HLE

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

5.5

The model's comprehensive average score on the Hugging Face Open LLM Leaderboard

LiveCodeBench

Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems

SciCode

The model's capability in code generation for scientific computing or specific scientific domains

37.6

The model's capability in code generation for scientific computing or specific scientific domains

HumanEval

Score achieved by the AI model on the specific HumanEval benchmark test set

94.8

Score achieved by the AI model on the specific HumanEval benchmark test set

Math 500 Score

Score on the first 500 larger, more well-known mathematical benchmark tests

94.1

Score on the first 500 larger, more well-known mathematical benchmark tests

AIME Score

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

68.7

An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

Output tokens/million

131k

Context Length

Gemini 2.0 Flash Lite (Preview)

Output tokens/million

Output tokens/million

33k

Context Length

Qwen2.5 Coder Instruct 32B

Output tokens/million

131k

Context Length

Empowering the Future, Your AI Solution Knowledge Base

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