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DeepSeek V3.1 (Non-reasoning)

DeepSeekDeepSeekOpen WeightMIT · Usage Commercial

Description

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.

Date de sortie
2025-08-21
Paramètres
671.0B
Longueur du contexte
164K
Modalités
text

Radar de capacités

34
general
58
coding
54
reasoning
53
science
30
agents
0
multimodal

Classements

Domaine#RangScoreSource
Classement codage295
51.0
AA
Classement général325
41.0
AA
Science348
42.0
AA

Scores de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

Factuality

SimpleQA93.4%Aut.

General

Aider-Polyglot68.4%Aut.

Language

MMLU-Redux91.8%Aut.
MMLU-Pro83.7%Aut.

Math

CodeForces0.70 / 3000Aut.
AIME 202466.3%Aut.
AIME 202549.8%Aut.
HMMT 202533.5%Aut.

Reasoning

GPQANYU + Cohere + Anthropic (2023)74.9%Aut.
SWE-Bench Verified66.0%Aut.
LiveCodeBench56.4%Aut.
SWE-bench Multilingual54.5%Aut.
BrowseComp-zh49.2%Aut.
Terminal-Bench31.3%Aut.
BrowseCompOpenAI (2025)30.0%Aut.
Humanity's Last Exam15.9%Aut.

Indices d'évaluation AA

(Artificial Analysis)
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
83.3
Gpqa(NYU + Cohere + Anthropic (2023))
73.5
Livecodebench(UC Berkeley + MIT + Cornell (2024))
57.7
Math Index(Artificial Analysis)
49.7
Aime 25(MAA (Mathematical Association of America))
49.7
Lcr(Artificial Analysis)
47.0
Ifbench(Google Research (2023))
37.8
Tau2(Sierra + U Toronto + Vector Institute (2025))
34.8
Terminalbench Hard(Stanford × Laude Institute (2026))
24.2
Intelligence Index(Artificial Analysis)
13.7
Hle(Center for AI Safety + Scale AI (2025))
6.7

Scores par catégorie 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

Tarification

Prix d'entrée$0.56 / 1M tokens
Prix de sortie$1.68 / 1M tokens
Prix mixte (3:1)$0.84 / 1M tokens
Prix de lecture cache$0.13 / 1M tokens

Vitesse

Tokens/sec0.0
Délai du premier token0.00s
Temps de réponse0.00s

Classement des Prix par Fournisseur

Classement des Prix par Fournisseur

3 fournisseurs

Moins cher: DeepInfraPlus cher: Alibaba (China)
FournisseurEntréeSortie
1DeepInfraMoins cher
$0
$0
2DeepSeekPRINCIPAL
$0.56
$1.68
3Alibaba (China)
$0.574
$1.721

Comparer les prix entre différents fournisseurs API pour ce modèle.

Sources externes