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DeepSeek V3.1 (Non-reasoning)

DeepSeekDeepSeekOpen WeightMIT · Uso Comercial

Descripción

DeepSeek-V3.1 is a hybrid model supporting both thinking and non-thinking modes through different chat templates. Built on DeepSeek-V3.1-Base with a two-phase long context extension (32K phase: 630B tokens, 128K phase: 209B tokens), it features 671B total parameters with 37B activated. Key improvements include smarter tool calling through post-training optimization, higher thinking efficiency achieving comparable quality to DeepSeek-R1-0528 while responding more quickly, and UE8M0 FP8 scale data format for model weights and activations. The model excels in both reasoning tasks (thinking mode) and practical applications (non-thinking mode), with particularly strong performance in code agent tasks, math competitions, and search-based problem solving.

Fecha de lanzamiento
2025-08-21
Parámetros
671.0B
Longitud del contexto
164K
Modalidades
text

Radar de capacidades

38
general
53
coding
54
reasoning
47
science
30
agents
0
multimodal

Rankings

Dominio#PosiciónPuntuaciónFuente
Capacidad agéntica129
33.0
LS
Ranking de codificación217
52.0
AA
Ranking general276
46.0
AA
Ciencia252
49.0
AA

Puntuaciones de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

Agents

Terminal-Bench31.3%Aut.
BrowseCompOpenAI (2025)30.0%Aut.

Biology

GPQANYU + Cohere + Anthropic (2023)74.9%Aut.

Code

Aider-Polyglot68.4%Aut.
SWE-Bench Verified66.0%Aut.
LiveCodeBench56.4%Aut.
SWE-bench Multilingual54.5%Aut.

Factuality

SimpleQA93.4%Aut.

Finance

MMLU-Pro83.7%Aut.

General

MMLU-Redux91.8%Aut.

Math

CodeForces0.70 / 3000Aut.
AIME 202466.3%Aut.
AIME 202549.8%Aut.
HMMT 202533.5%Aut.
Humanity's Last Exam15.9%Aut.

Reasoning

BrowseComp-zh49.2%Aut.

Índices de evaluación AA

(Artificial Analysis)
Math Index(Artificial Analysis)
49.7
Intelligence Index(Artificial Analysis)
21.4
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.8
Gpqa(NYU + Cohere + Anthropic (2023))
0.7
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.6
Aime 25(MAA (Mathematical Association of America))
0.5
Lcr(Artificial Analysis)
0.5
Ifbench(Google Research (2023))
0.4
Scicode(UIUC + Argonne National Lab (2024))
0.4
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.3
Terminalbench Hard(Stanford × Laude Institute (2026))
0.2
Hle(Center for AI Safety + Scale AI (2025))
0.1

Puntuaciones por categoría LLM Stats

(LLM Stats (zeroeval))
Language
90
Factuality
90
Legal
80
Finance
80
Healthcare
80
Physics
70
Frontend Development
70
Biology
70
Chemistry
70
Math
60
Reasoning
60
General
60
Code
60
Search
40
Agents
30
Vision
20

Precios

Precio de entrada$0.56 / 1M tokens
Precio de salida$1.68 / 1M tokens
Precio mixto (3:1)$0.84 / 1M tokens
Precio de lectura caché$0.13 / 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

2 proveedores

Más barato: DeepSeekMás caro: Alibaba (China)
ProveedorEntradaSalida
1DeepSeekPRINCIPAL
$0.56
$1.68
2Alibaba (China)
$0.574
$1.721

Comparar precios entre diferentes proveedores de API para este modelo.

Fuentes externas