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DeepSeek V3.1 (Non-reasoning)

DeepSeekDeepSeek開源權重MIT · 商用許可

描述

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.

發布日期
2025-08-21
參數規模
671.0B
上下文長度
164K
支援模態
text

能力雷達圖

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

排行榜排名

領域#排名分數來源
程式碼能力榜297
51.0
AA
通用能力榜326
41.0
AA
科學能力350
42.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Factuality

SimpleQA93.4%自報

General

Aider-Polyglot68.4%自報

Language

MMLU-Redux91.8%自報
MMLU-Pro83.7%自報

Math

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

Reasoning

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

AA 評測指數

(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

LLM Stats 分類評分

(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

定價

輸入價格$0.56 / 1M tokens
輸出價格$1.68 / 1M tokens
混合價格(3:1)$0.84 / 1M tokens
快取讀取價格$0.13 / 1M tokens

速度

Tokens/秒0.0
首Token延遲0.00s
首回答延遲0.00s

供應商價格排行

供應商價格排行

3 個供應商

最便宜: DeepInfra最貴: Alibaba (China)
供應商輸入輸出
1DeepInfra最便宜
$0
$0
2DeepSeek主要
$0.56
$1.68
3Alibaba (China)
$0.574
$1.721

比較該模型在不同 API 供應商之間的定價。

外部連結