Deepseek R1 0528 Qwen3 8B
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Deepseek R1 0528 Qwen3 8B

DeepSeek-R1-0528-Qwen3-8B: A distillation inference powerhouse open-sourced on May 30, 2025. With 8B parameters, its performance rivals that of models with 235B parameters. In the AIME24 math test, it shows an increase from 86% to 96%. It supports a 64K context, is free for commercial use under the MIT license, and can run on a local machine with 8GB of VRAM.
Intelligence(Medium)
Speed(Relatively Slow)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
131,072
Maximum Output Tokens
-
Knowledge Cutoff

Pricing

¥3.6 /M tokens
Input
¥15.48 /M tokens
Output
¥0.47 /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 0528 Qwen3 8BTechnical 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
45
Release Date
2025-05-29
Response Speed
91.096,115 tokens/s

Benchmark Scores

Below is the performance of DeepSeek R1 0528 Qwen3 8B in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
52.19
Large Language Model Intelligence Level
Coding Index
35.85
Indicator of AI model performance on coding tasks
Math Index
79.1
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
73.9
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
61.2
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
5.6
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
51.3
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
20.4
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
90.5
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
93.2
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
65
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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