Deepseek R1 Distill Qwen 14B

DeepSeek - R1は、DeepSeek - V3（合計6710億のパラメータ、各トークンで370億のパラメータが活性化）をベースに構築された初代推論モデルです。大規模強化学習（RL）を組み合わせることで、思考連鎖と推論能力を強化し、数学、コード、多段階推論タスクで優れた性能を発揮します。

Intelligence(Medium)

Speed(Relatively Slow)

Input Supported Modalities

Is Reasoning Model

128,000

Context Window

128,000

Maximum Output Tokens

Knowledge Cutoff

Go Compare

Pricing

- /M tokens

Input

- /M tokens

Output

￥1.44 /M tokens

Blended Price

Quick Simple Comparison

Input

Output

DeepSeek V3 0324 (Mar '25)

DeepSeek R1

￥0.55

DeepSeek R1 (Jan '25)

￥0.56

Basic Parameters

DeepSeek R1 Distill Qwen 14BTechnical Parameters

Parameter Count

14,800.0M

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

75.49,411 tokens/s

Benchmark Scores

Below is the performance of DeepSeek R1 Distill Qwen 14B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.

Intelligence Index

Large Language Model Intelligence Level

Coding Index

Indicator of AI model performance on coding tasks

30.72

Indicator of AI model performance on coding tasks

Math Index

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

80.77

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

GPQA

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

48.4

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

4.4

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

37.6

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

23.9

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

93.4

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

94.9

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)

66.7

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

Output tokens/million

33k

Context Length

Gemini 2.0 Flash Lite (Preview)

Output tokens/million

Output tokens/million

8192

Context Length

Empowering the Future, Your AI Solution Knowledge Base

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