Qwen3 Coder 480B A35B Instruct
AlibabaQwenOpen WeightApache 2.0 · Uso Comercial
Descripción
Qwen3-Coder-480B-A35B-Instruct is Qwen's most agentic code model to date, featuring 480 billion total parameters with 35 billion activated parameters using MoE architecture. It achieves significant performance among open models on Agentic Coding, Agentic Browser-Use, and foundational coding tasks, with results comparable to Claude Sonnet. Features native 256K token context length (extendable to 1M tokens with Yarn), optimized for repository-scale understanding, and specialized function call format for agentic coding platforms like Qwen Code and CLINE.
Fecha de lanzamiento
2025-07-22
Parámetros
480.0B
Longitud del contexto
262K
Modalidades
text
Radar de capacidades
34
general
53
coding
52
reasoning
41
science
60
agents
0
multimodal
Rankings
| Dominio | #Posición | Puntuación | Fuente |
|---|---|---|---|
| Capacidad agéntica | 116 | 36.0 | LS |
| Ranking de codificación | 238 | 49.0 | AA |
| Ranking general | 292 | 45.0 | AA |
| Ciencia | 332 | 42.0 | AA |
Puntuaciones de benchmarks (LLM Stats)
(LLM Stats (zeroeval))Agents
BFCL-v3
68.7%Aut.
MM-Mind2Web
55.8%Aut.
Terminal-Bench 2.0Stanford × Laude Institute (2026)
37.5%Aut.
Code
SWE-Bench Verified
69.6%Aut.
Aider-Polyglot
61.8%Aut.
SWE-bench Multilingual
54.7%Aut.
Multi-SWE-Bench
25.8%Aut.
Communication
TAU-bench Retail
77.5%Aut.
TAU-bench Airline
60.0%Aut.
Language
Spider
31.1%Aut.
Índices de evaluación AA
(Artificial Analysis)Math Index(Artificial Analysis)39.3
Intelligence Index(Artificial Analysis)18.2
Math 500(OpenAI (2024), subset of Hendrycks et al. MATH (2021))0.9
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))0.8
Gpqa(NYU + Cohere + Anthropic (2023))0.6
Livecodebench(UC Berkeley + MIT + Cornell (2024))0.6
Aime(MAA (Mathematical Association of America))0.5
Lcr(Artificial Analysis)0.5
Tau2(Sierra + U Toronto + Vector Institute (2025))0.4
Ifbench(Google Research (2023))0.4
Aime 25(MAA (Mathematical Association of America))0.4
Scicode(UIUC + Argonne National Lab (2024))0.4
Terminalbench Hard(Stanford × Laude Institute (2026))0.2
Hle(Center for AI Safety + Scale AI (2025))0.0
Puntuaciones por categoría LLM Stats
(LLM Stats (zeroeval))Structured Output70
Finance70
Communication70
Multimodal60
Reasoning60
Frontend Development60
General60
Tool Calling60
Agents50
Code50
Precios
Precio de entrada$1.5 / 1M tokens
Precio de salida$7.5 / 1M tokens
Precio mixto (3:1)$3 / 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
4 proveedores
Más barato: LLM GatewayMás caro: Alibaba
ProveedorEntradaSalida
1LLM GatewayMás barato
$0.3
$1.3
2302.AI
$0.86
$3.43
3Alibaba (China)
$0.861
$3.441
4AlibabaPRINCIPAL
$1.5
$7.5
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