Based on the new Qwen3‑Next architecture (hybrid attention, highly sparse MoE, training‑stability optimizations, and multi‑token prediction), the Qwen3‑Next‑80B‑A3B‑Instruct model delivers extreme efficiency with only 3B active parameters per pass. It performs comparably to Qwen3‑235B‑A22B‑Instruct‑2507 and shows clear advantages on ultra‑long context tasks (up to 256K tokens).
Added Sep 11, 2025
Model weightsContext Window
256.0K
Max Output
262.1K
Input Price (Auto)
$0.16/1M
Output Price (Auto)
$0.68/1M
Cache Read (Auto)
$0.079/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
13.7
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
73.8%
Better than 63% of models compared
HLE
Humanity's Last Exam
7.3%
Better than 56% of models compared
IFBench
Instruction-following benchmark
39.7%
Better than 39% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
21.6%
Better than 23% of models compared
AA-LCR
Long context reasoning evaluation
51.3%
Better than 63% of models compared
CritPt
Research-level physics reasoning
0.0%
Coding
SciCode
Python programming for scientific computing
30.7%
Better than 48% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
7.6%
Better than 41% of models compared
LiveCodeBench
Contamination-free coding benchmark
68.4%
Better than 76% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
66.3%
Better than 61% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
81.9%
Better than 77% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
17.2%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
92.7%
Last updated Jun 28, 2026
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