MUE architecture comparison: QWEN3 30B-A3B vs. GPT-oss 20b

This article provides a technical comparison of two recently released Experts (MOE) transformer models: Alibaba’s QWEN3 30B-A3B (issued in April 2025) and OpenAI’s GPT-oss 20B (issued in August 2025). Both models represent different approaches to MOE architecture design, balancing computing efficiency with the performance of different deployment solutions.

Model Overview

Source: QWEN3 official documents, OpenAI GPT-oss documentation

QWEN3 30B-A3B technical specifications

Architecture details

QWEN3 30B-A3B adopts a deep transformer architecture 48th flooreach containing a mixture configuration with experts 128 experts per floor. Model activation 8 experts for each token During inference, a balance is reached between professionalism and computational efficiency.

Pay attention to the mechanism

This model uses Group Query Attention (GQA) and 32 query headers and 4 key value headers³. The design optimizes memory usage while maintaining attention quality, which is particularly beneficial for novel processing.

Context and multilingual support

• Local context length: 32,768 tokens
• Extended context: Up to 262,144 tokens (latest version)
• Multilingual support:119 Languages and Dialects
• vocabulary: 151,936 tokens are used tokenize

Unique features

Qwen3 merge a Hybrid inference system Supports “thinking” and “non-thinking” modes, allowing users to control calculation overhead based on task complexity.

GPT-oss 20b technical specifications

Architecture details

GPT-oss 20b has 24-layer transformer and 32 experts from the Ministry of Education per floor⁸. Model activation 4 experts for each tokenemphasizing a wider range of expert capabilities rather than fine-grained specialization.

Pay attention to the mechanism

Model implementation Grouping multiple Query attention and 64 query heads and 8 key heads are divided into 8 people. This configuration supports effective reasoning while maintaining the attention quality throughout the wider architecture.

Context and optimization

• Local context length: 128,000 tokens
• Quantification:Native MXFP4 (4.25-bit precision)
• Memory efficiency: Run on 16GB of memory by quantization
• Token:O200K_HARMONY (Superset of GPT-4O Tokenizer)

Performance characteristics

GPT-oss 20b use Alternating dense and locally sparse attention patterns Similar to gpt-3, with Rotating position embed (rope) Used for location encoding.

Comparison of architectural philosophy

Depth and width strategy

QWEN3 30B-A3B emphasize Depth and expert diversity:

• 48-layer enables multi-stage reasoning and layered abstraction
• 128 experts per layer provide fine-grained specialization
• Suitable for complex inference tasks that require in-depth processing

GPT-oss 20b priority Width and calculated density:

• 24 layers with larger experts maximize the representative capacity of each layer
• Fewer but stronger experts (32 vs 128) improves individual expert competence
• Optimized for effective single-pass reasoning

MUE routing policy

qwen3: Route through 8 out of 128 expertsencourages a wide variety of context-sensitive processing paths and modular decision-making.

gpt-oss: Route through 4 out of 32 expertsmaximizes expert computing power and provides centralized processing for each reasoning step.

Memory and deployment considerations

QWEN3 30B-A3B

• Memory requirements: Variables based on precision and context length
• deploy: Optimized for cloud and edge deployments with flexible context extension
• Quantification: Support various quantitative solutions after training

GPT-oss 20b

• Memory requirements: 16GB with native MXFP4 quantization, ~48GB in Bfloat16
• deploy: Designed for consumer hardware compatibility
• Quantification:Native MXFP4 training can effectively reason without quality degradation

Performance characteristics

QWEN3 30B-A3B

• Good at Mathematical reasoning, coding and complex logical tasks
• Strong performance Multilingual scenes Cross 119 languages
• Thinking mode Provide enhanced reasoning skills for complex problems

GPT-oss 20b

• Achievement Performance is comparable to Openai O3-Mini On standard benchmarks
• optimization Tool usage, web browsing and feature calls
• Strong Thinking reasoning With adjustable inference work level

Use case suggestions

Select qwen3 30b-a3b for:

• Complex reasoning tasks that require multi-stage processing
• Multilingual applications across different languages
• Solutions that require flexible context length expansion
• Applying the transparency of thinking/reasoning

Select GPT-oss 20b for:

• Resource-constrained deployment requires efficiency
• Tools and proxy applications
• Quick inference and consistent performance
• Edge deployment scenarios are limited

in conclusion

QWEN3 30B-A3B and GPT-oss 20b represent complementary methods of MOE architecture design. Qwen3 emphasizes depth, expert diversity and multilingual capabilities, making it suitable for complex reasoning applications. GPT-oss 20B prioritizes efficiency, tool integration and deployment flexibility, positioning it as a practical production environment with resource limitations.

Both models show the evolution of MOE architecture beyond simple parameter scaling and combine complex design choices that combine architectural decisions with expected use cases and deployment scenarios.

Note: This article is inspired by Reddit posts and images shared by Sebastian Raschka.

source

1. QWEN3 30B-A3B Model Card – Hug the Face
2. QWEN3 Technology Blog
3. QWEN3 30B-A3B basic specifications
4. QWEN3 30B-A3B Instruction 2507
5. QWEN3 official documents
6. QWEN Tokenizer Documentation
7. QWEN3 model function
8. Introduction to Openai GPT-oss
9. gpt-oss github repository
10. GPT-oss 20b – GROQ Documentation
11. Openai GPT-Oss technical details
12. Embrace Face GPT-sss Blog
13. Openai GPT-oss 20b model card
14. Introduction to Openai GPT-oss
15. NVIDIA GPT-OSS Technology Blog
16. Embrace Face GPT-sss Blog
17. QWEN3 performance analysis
18. Openai GPT-oss model card
19. GPT-oss 20b function