Model comparison

GPT-4.1 nano vs Qwen3-Next 80B-A3B Instruct

Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 27.9 on the Noometry Index. GPT-4.1 nano costs 5.0× less per token, which makes it the better buy when Qwen3-Next 80B-A3B Instruct's lead doesn't matter for your workload.

Last verified October 10, 2026. 21 shared benchmarks.

GPT-4.1 nano OpenAI

27.9

Rank #327 Confirmed

Qwen3-Next 80B-A3B Instruct

Alibaba (Qwen)

43.0

Rank #102 Confirmed

Summary

They share 21 benchmarks with published results for both. GPT-4.1 nano scores higher in 0 categories and Qwen3-Next 80B-A3B Instruct in 8 categories; 8 gaps are clear of the uncertainty.
The widest gap is in reasoning, where Qwen3-Next 80B-A3B Instruct leads 31.1 to 8.5.
The biggest single-benchmark swing is Kagi LLM Benchmark: 33.3% for GPT-4.1 nano and 66.7% for Qwen3-Next 80B-A3B Instruct.
GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.50 / $2 for Qwen3-Next 80B-A3B Instruct.
GPT-4.1 nano accepts more context: 1.05M tokens versus 131K.
Qwen3-Next 80B-A3B Instruct has downloadable open weights; the other is API-only.

Side by side

GPT-4.1 nano and Qwen3-Next 80B-A3B Instruct specifications
	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
Provider	OpenAI	Alibaba (Qwen)
Noometry Index	27.9	43.0
Released	2025-04-14	2025-09
Weights	Proprietary	Open
Context window	1.05M	131K
Max output	33K	33K
Input $ / M tokens	$0.10	$0.50
Output $ / M tokens	$0.40	$2
Results tracked	38	25

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Category by category

Coding Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 24.1 (#330), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)

Coding benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
LMArena Coding	1306	1440
Aider Polyglot	8.9%	—
SciCode	25.9%	—
WeirdML	19%	—

Agentic & Tool Use Not comparable

GPT-4.1 nano: 26.5 (#104), Qwen3-Next 80B-A3B Instruct: —

Agentic & Tool Use benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
Berkeley Function Calling Leaderboard	33%	—

Reasoning Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 8.5 (#349), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)

Reasoning benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
Kagi LLM Benchmark	33.3%	66.7%
LMArena Hard Prompts	1286	1428
ARC-AGI-2	0%	—
ARC-AGI-1	0%	—
CritPt	0%	—
DTBench	52.5%	—
LMCA	5.5%	—
Epoch Capabilities Index	129.62	—

Math Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 26.9 (#252), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)

Math benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
Omni-MATH	36.7%	46.7%
LMArena Math	1274	1440
OTIS Mock AIME 2024-2025	28.9%	—
MATH Level 5	70%	—
FrontierMath (Feb 2025 set)	1%	—

Knowledge Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 21.8 (#273), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)

Knowledge benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
MMLU-Pro	55%	78.6%
GPQA (HELM)	50.7%	63%
LMArena Expert	1272	1417
GPQA Diamond	48.9%	—
SimpleQA Verified	6%	—
Vectara Hallucination Rate	—	9.3%

Multimodal Not comparable

GPT-4.1 nano: 29.2 (#113), Qwen3-Next 80B-A3B Instruct: —

Multimodal benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
LMArena Vision	1063	—

Multilingual Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 41.6 (#205), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)

Multilingual benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
LMArena Non-English	1260	1407
LMArena Chinese	1270	1460
LMArena German	1288	1417
LMArena Japanese	1198	1395
LMArena Russian	1261	1404
LMArena French	—	1413
LMArena Korean	—	1364
LMArena Spanish	—	1435

Instruction Following Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 67.8 (#193), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)

Instruction Following benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
IFEval	84.3%	81%
LMArena Instruction Following	1267	1389

Long Context Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 23.7 (#296), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)

Long Context benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
Fiction.LiveBench	25%	55.6%
LMArena Longer Query	1283	1403

Writing & Preference Qwen3-Next 80B-A3B Instruct leads

GPT-4.1 nano: 40.5 (#243), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)

Writing & Preference benchmarks
Benchmark	GPT-4.1 nano	Qwen3-Next 80B-A3B Instruct
LMArena Text	1285	1417
LMArena Creative Writing	1260	1334
WildBench	81.2%	80.7%
LMArena Multi-Turn	1277	1416
EQ-Bench Creative Writing	946	—

Frequently asked questions

Is GPT-4.1 nano better than Qwen3-Next 80B-A3B Instruct?

Which is cheaper, GPT-4.1 nano or Qwen3-Next 80B-A3B Instruct?

GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Qwen3-Next 80B-A3B Instruct lists at $0.50 and $2.

Is GPT-4.1 nano or Qwen3-Next 80B-A3B Instruct better for coding?

Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 24.1 in the Noometry coding category.

Which has the bigger context window?

GPT-4.1 nano does, with 1.05M tokens against 131K.

How many benchmarks do GPT-4.1 nano and Qwen3-Next 80B-A3B Instruct share?

21 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.