Qwen3-Next-80B-A3B-Instruct

Qwen3-Next-80B-A3B-Instruct is the first in the Qwen3-Next series, featuring groundbreaking architectural innovations. It uses Hybrid Attention combining Gated DeltaNet and Gated Attention for efficient ultra-long context modeling, High-Sparsity MoE with 512 experts (10 activated + 1 shared) achieving extreme low activation ratio, and Multi-Token Prediction for improved performance and faster inference. With 80B total parameters and only 3B activated, it outperforms Qwen3-32B-Base with 10% training cost and 10x throughput for 32K+ contexts. The model performs on par with Qwen3-235B-A22B-Instruct-2507 while excelling at ultra-long-context tasks up to 256K tokens (extensible to 1M with YaRN). Architecture: 48 layers, 15T training tokens, hybrid layout of 12*(3*(Gated DeltaNet->MoE)->(Gated Attention->MoE)).

Benchmark results

Benchmark Score Tags Source
Aider-Polyglot 49.8% self-reported llm-stats link →
AIME 2025 69.5% self-reported llm-stats link →
Arena-Hard v2 82.7% self-reported llm-stats link →
BFCL-v3 70.3% self-reported llm-stats link →
Creative Writing v3 85.3 self-reported llm-stats link →
GPQA 72.9% self-reported llm-stats link →
HMMT25 54.1% self-reported llm-stats link →
IFEval 87.6% self-reported llm-stats link →
Include 78.9% self-reported llm-stats link →
LiveBench 20241125 75.8% self-reported llm-stats link →
LiveCodeBench v6 56.6% self-reported llm-stats link →
MMLU-Pro 80.6% self-reported llm-stats link →
MMLU-ProX 76.7% self-reported llm-stats link →
MMLU-Redux 90.9% self-reported llm-stats link →
Multi-IF 75.8% self-reported llm-stats link →
MultiPL-E 87.8% self-reported llm-stats link →
PolyMATH 45.9% self-reported llm-stats link →
SuperGPQA 58.8% self-reported llm-stats link →
TAU-bench Airline 44.0% self-reported llm-stats link →
TAU-bench Retail 60.9% self-reported llm-stats link →
Tau2 Airline 45.5% self-reported llm-stats link →
Tau2 Retail 57.3% self-reported llm-stats link →
Tau2 Telecom 13.2% self-reported llm-stats link →
WritingBench 87.3% self-reported llm-stats link →