Qwen3 235B A22B (Non-reasoning)
AlibabaQwenOpen WeightApache 2.0 · Uso Comercial
Descripción
Qwen3 235B A22B is a large language model developed by Alibaba, featuring a Mixture-of-Experts (MoE) architecture with 235 billion total parameters and 22 billion activated parameters. It achieves competitive results in benchmark evaluations of coding, math, general capabilities, and more, compared to other top-tier models.
Fecha de lanzamiento
2025-04-28
Parámetros
235.0B
Longitud del contexto
131K
Modalidades
text
Radar de capacidades
28
general
34
coding
40
reasoning
44
science
70
agents
0
multimodal
Rankings
| Dominio | #Posición | Puntuación | Fuente |
|---|---|---|---|
| Ranking de codificación | 549 | 15.0 | AA |
| Ranking general | 422 | 33.0 | AA |
| Ciencia | 449 | 33.0 | AA |
Puntuaciones de benchmarks (LLM Stats)
(LLM Stats (zeroeval))General
Arena Hard
95.6%Aut.
MMLU
87.8%Aut.
Include
73.5%Aut.
MultiLF
71.9%Aut.
BFCL
70.8%Aut.
MultiPL-E
65.9%Aut.
Language
MMLU-Redux
87.4%Aut.
MMMLU
86.7%Aut.
MMLU-Pro
68.2%Aut.
Math
GSM8k
94.4%Aut.
AIME 2024
85.7%Aut.
MGSM
83.5%Aut.
AIME 2025
81.5%Aut.
LiveBench
77.1%Aut.
MATH
71.8%Aut.
Reasoning
BBH
88.9%Aut.
MBPP
0.81 / 100Aut.
CRUX-O
0.79 / 100Aut.
EvalPlus
0.78 / 100Aut.
LiveCodeBench
70.7%Aut.
Aider
61.8%Aut.
GPQANYU + Cohere + Anthropic (2023)
47.5%Aut.
SuperGPQA
44.1%Aut.
Índices de evaluación AA
(Artificial Analysis)Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))90.2
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))76.2
Gpqa(NYU + Cohere + Anthropic (2023))61.3
Ifbench(Google Research (2023))36.6
Livecodebench(UC Berkeley + MIT + Cornell (2024))34.3
Aime(MAA (Mathematical Association of America))32.7
Tau2(Sierra + U Toronto + Vector Institute (2025))27.2
Math Index(Artificial Analysis)23.7
Aime 25(MAA (Mathematical Association of America))23.7
Intelligence Index(Artificial Analysis)8.3
Terminalbench Hard(Stanford × Laude Institute (2026))6.1
Hle(Center for AI Safety + Scale AI (2025))4.2
Lcr(Artificial Analysis)0.0
Puntuaciones por categoría LLM Stats
(LLM Stats (zeroeval))Chat100
Creativity100
Writing100
Language80
Math80
Reasoning80
General80
Legal70
Finance70
Healthcare70
Code70
Tool Calling70
Physics50
Biology50
Chemistry50
Economics40
Precios
Precio de entrada$0.7 / 1M tokens
Precio de salida$2.8 / 1M tokens
Precio mixto (3:1)$1.225 / 1M tokens
Velocidad
Tokens/seg0.0
Retraso del primer token0.00s
Tiempo hasta la respuesta0.00s
Ranking de Precios por Proveedor
Ranking de Precios por Proveedor
3 proveedores
Más barato: Alibaba (China)Más caro: Alibaba
ProveedorEntradaSalida
1Alibaba (China)Más barato
$0.287
$1.147
2302.AI
$0.29
$2.86
3AlibabaPRINCIPAL
$0.7
$2.8
Comparar precios entre diferentes proveedores de API para este modelo.