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Qwen3 Coder 480B A35B

Active Parameters

480B

Context Length

262K

Modality

Text

Architecture

Mixture of Experts

KV Cache Rate

248 KB/tok

License

Apache 2.0

Release Date

22 Jul 2025

Knowledge Cutoff

Dec 2024

API Pricing (per 1M)

Input: $0.65 · Output: $3.25

Hardware Sizing

Precision

Context

Workload Memory: 963.70 GB

FP16 · 8K context

Base Weights: 959.26 GB

KV Cache: 1.94 GB

Runtime & Graphs: 2.50 GB

Minimum Required Hardware

8x NVIDIA H200 141GB

963.7 / 1015.2 GB usable · TP 8

Determine requirements for datacenter hardware:

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Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 6.1k · Context: 262K · Vocab: 151.9kx 62 layersRMSNormPre-AttentionMulti-Head Attention96Q / 8KV headsHead dim: 128+RMSNormPre-FFNSparse MoE FFN (8/160 experts)SwiGLUIntermediate: 2.6k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#142

BenchmarkScoreRank

Software Engineering

SWE-bench Verified

0.70

16

Web Development

WebDev Arena

1274

109

General Text

Text Arena

1388

122

Intelligence Index

Artificial Analysis

0.12

229

Rankings

Overall Rank

#142

Coding Rank

#99

About Qwen3 Coder 480B A35B

Qwen3 Coder 480B A35B is Alibaba's specialized agentic coding MoE model activating 35B parameters for repository-wide software development. Trained on 7.5T tokens with Agent RL, it natively powers terminal agents and automated PR generation over a 1M context.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

96

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

10,000,000

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

6,144

Number of Layers

62

FFN Intermediate Size (Dense)

2,560

Tokenizer

Vocabulary Size

151,936

Mixture of Experts

Total Expert Parameters

35.0B

Number of Experts

160

Active Experts

8

FFN Intermediate Size (per Expert)

2,560

About Qwen 3

The Alibaba Qwen 3 model family comprises dense and Mixture-of-Experts (MoE) architectures, with parameter counts from 0.6B to 235B. Key innovations include a hybrid reasoning system, offering 'thinking' and 'non-thinking' modes for adaptive processing, and support for extensive context windows, enhancing efficiency and scalability.


Other Qwen 3 Models