
Deepseek R1 (Jan '25)
The R1 inference model released by DeepSeek in January 2025 is specifically optimized for complex reasoning tasks. It adopts an advanced inference architecture and performs excellently in mathematical reasoning, logical analysis, and problem-solving. Although the inference speed is relatively slow (about 25 tokens per second), it demonstrates excellent reasoning ability in pure mathematical benchmark tests. It is suitable for professional applications that require high-quality reasoning and in-depth analysis.
Intelligence(Medium)
Speed(Slow)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
131,072
Maximum Output Tokens
-
Knowledge Cutoff
Pricing
¥4.03 /M tokens
Input
¥15.98 /M tokens
Output
¥17.01 /M tokens
Blended Price
Quick Simple Comparison
DeepSeek-Coder-V2
DeepSeek V3 0324 (Mar' 25)
DeepSeek V3 0324 (Mar '25)
Basic Parameters
DeepSeek R1 (Jan '25)Technical Parameters
Parameter Count
671,000.0M
Context Length
128.00k tokens
Training Data Cutoff
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
9
Release Date
2025-01-20
Response Speed
0 tokens/s
Benchmark Scores
Below is the performance of DeepSeek R1 (Jan '25) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
60.22
Large Language Model Intelligence Level
Coding Index
48.7
Indicator of AI model performance on coding tasks
Math Index
82.47
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
84.4
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
8100
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
7160
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
7330
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
35.7
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
97.7
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
96.6
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
8750
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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