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DeepSeek V3.2 (Non-reasoning)

DeepSeekDeepSeekOpen WeightMIT · Uso Comercial

Descripción

DeepSeek-V3.2 is a 685B-parameter MoE model that harmonizes high computational efficiency with superior reasoning and agent performance. It introduces DeepSeek Sparse Attention (DSA) for efficient long-context processing, a scalable reinforcement learning post-training framework, and large-scale agentic task synthesis covering 1,800+ environments. V3.2 achieves GPT-5-level performance across reasoning, coding, and agentic benchmarks, with gold-medal results from its Speciale variant on IMO, IOI, ICPC World Finals, and CMO 2025.

Fecha de lanzamiento
2025-12-01
Parámetros
685.0B
Longitud del contexto
164K
Modalidades
text

Radar de capacidades

36
general
59
coding
62
reasoning
55
science
50
agents
0
multimodal

Rankings

Dominio#PosiciónPuntuaciónFuente
Capacidad agéntica94
36.0
LS
Ranking de codificación270
55.0
AA
Ranking general198
55.0
AA
Ciencia306
46.0
AA

Puntuaciones de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

Agents

t2-bench80.3%Aut.
MCP-Universe45.9%Aut.
MCP-Mark38.0%Aut.
Toolathlon35.2%Aut.

Factuality

SimpleQA97.1%Aut.

General

Aider-Polyglot74.5%Aut.

Language

MMLU-Pro85.0%Aut.

Math

AIME 202589.3%Aut.
HMMT 202583.6%Aut.
IMO-AnswerBench78.3%Aut.
CodeForces0.71 / 3000Aut.

Reasoning

GPQANYU + Cohere + Anthropic (2023)79.9%Aut.
LiveCodeBench74.1%Aut.
SWE-Bench Verified67.8%Aut.
SWE-bench Multilingual57.9%Aut.
BrowseComp-zh47.9%Aut.
Terminal-Bench 2.0Stanford × Laude Institute (2026)46.4%Aut.
BrowseCompOpenAI (2025)40.1%Aut.
Terminal-Bench37.7%Aut.
Humanity's Last Exam19.8%Aut.

Índices de evaluación AA

(Artificial Analysis)
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
83.7
Tau2(Sierra + U Toronto + Vector Institute (2025))
78.9
Gpqa(NYU + Cohere + Anthropic (2023))
75.1
Livecodebench(UC Berkeley + MIT + Cornell (2024))
59.3
Math Index(Artificial Analysis)
59.0
Aime 25(MAA (Mathematical Association of America))
59.0
Ifbench(Google Research (2023))
49.0
Lcr(Artificial Analysis)
45.7
Terminalbench Hard(Stanford × Laude Institute (2026))
32.6
Intelligence Index(Artificial Analysis)
16.0
Hle(Center for AI Safety + Scale AI (2025))
11.2

Puntuaciones por categoría LLM Stats

(LLM Stats (zeroeval))
Language
80
Legal
80
Math
80
Physics
80
Finance
80
Healthcare
80
Biology
80
Chemistry
80
Reasoning
70
Frontend Development
70
General
70
Code
70
Search
60
Agents
50
Tool Calling
50
Vision
40

Precios

Precio de entrada$0.28 / 1M tokens
Precio de salida$0.42 / 1M tokens
Precio mixto (3:1)$0.315 / 1M tokens
Precio de lectura caché$0.028 / 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

13 proveedores

Más barato: DeepInfraMás caro: Vercel AI Gateway
ProveedorEntradaSalida
1DeepInfraMás barato
$0
$0
2TokenGo
$0.2174
$0.326
3NovitaAI
$0.269
$0.4
4DeepSeekPRINCIPAL
$0.28
$0.42
5NanoGPT
$0.28
$0.42
6OpenRouter
$0.28
$0.42
7ZenMux
$0.28
$0.43
8Kilo Gateway
$0.28
$0.42
9Merge Gateway
$0.28
$0.4
10Ofox
$0.29
$0.43
11TensorX
$0.3
$0.5
12EmpirioLabs AI
$0.57
$1.71
13Vercel AI Gateway
$0.62
$1.85

Comparar precios entre diferentes proveedores de API para este modelo.

Fuentes externas