Deepseek V4.1 Flash
DeepSeek V4.1 Flash 是 DeepSeek 推出的稀疏混合专家(MoE)模型,也是该公司首款采用「因果编码器-解码器」(Causal Encoder-Decoder, CED)架构的模型。输入端激活 80 亿参数、输出端激活 160 亿参数,总参数规模 5520 亿,这种非对称设计让单 token 计算量相对总参数量保持较低。图像理解能力原生集成在架构中,视觉与文本嵌入从预训练一开始就联合训练,而不是像此前的实验性版本 V4 Flash Vision Exp 那样后期附加。
模态
上下文
1.05Mtokens
最大输出 393K tokens
排名与分数走势
排名
Elo 评分
能力
工具调用 (Tools)
结构化输出
并行工具调用
视觉输入
音频输入
多服务商价格对比数据来自 OpenRouter
| 服务商 | 量化精度 | 上下文 | 输入/M | 输出/M | 缓存读取/M | 近1天可用率 |
|---|---|---|---|---|---|---|
O OpenInference | fp4 | 1.05M | ¥0.30 | ¥5.97 | ¥0.04 | 98.7% |
| — | 1.05M | ¥0.34 | ¥4.02 | ¥0.05 | 98.0% | |
| fp8 | 1.05M | ¥0.40 | ¥4.36 | ¥0.06 | 100.0% | |
I InferenceNet | fp8 | 1.04M | ¥0.52 | ¥3.69 | ¥0.07 | 100.0% |
D Decart | fp4 | 1.05M | ¥0.60 | ¥1.21 | ¥0.12 | 100.0% |
S Sail Research | fp4 | 1.05M | ¥0.60 | ¥2.01 | ¥0.04 | 99.9% |
I Ionstream | — | 1.05M | ¥0.67 | ¥5.03 | ¥0.07 | 97.8% |
W Wafer | — | 1.05M | ¥0.87 | ¥6.64 | ¥0.23 | 100.0% |
| fp8 | 1.05M | ¥0.94 | ¥2.82 | ¥0.03 | 100.0% | |
| fp8 | 1.02M | ¥0.99 | ¥3.94 | ¥0.02 | 100.0% | |
| — | 1.05M | ¥1.11 | ¥4.43 | ¥0.04 | 100.0% | |
G GMICloud限时 -40% | fp8 | 1.05M | ¥1.21 | ¥4.83 | ¥0.02 | 100.0% |
| fp8 | 1.05M | ¥1.31 | ¥5.23 | ¥0.03 | 100.0% | |
C CoreWeave | fp8 | 1.05M | ¥1.34 | ¥4.36 | ¥0.20 | 100.0% |
P Phala限时 -30% | — | 1.05M | ¥1.41 | ¥5.63 | ¥0.03 | 100.0% |
M Makora | fp8 | 1.05M | ¥1.81 | ¥7.71 | ¥0.06 | 100.0% |
| fp8 | 1.05M | ¥1.94 | ¥8.05 | ¥0.05 | 99.8% | |
| fp8 | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 97.6% | |
| — | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 100.0% | |
| fp8 | 1.05M | ¥2.01 | ¥8.05 | ¥0.20 | 100.0% | |
| fp8 | 1.05M | ¥2.01 | ¥8.05 | ¥0.05 | 100.0% | |
| — | 1.00M | ¥2.01 | ¥8.05 | ¥0.20 | 98.1% | |
| — | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 100.0% | |
S SiliconFlow | fp8 | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 99.7% |
| — | 1.05M | ¥2.01 | ¥8.05 | ¥0.20 | 100.0% | |
| fp8 | 1.05M | ¥2.01 | ¥8.05 | ¥0.05 | 100.0% | |
| fp8 | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 99.9% | |
| — | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 100.0% | |
| fp8 | 1.00M | ¥2.01 | ¥8.05 | ¥0.05 | 100.0% | |
| — | 1.05M | ¥2.01 | ¥8.05 | ¥0.04 | 100.0% | |
| — | 1.05M | ¥3.02 | ¥12.07 | ¥0.06 | 100.0% | |
| fp32 | 1.05M | ¥4.02 | ¥16.09 | ¥0.09 | 99.2% |
O
OpenInferencefp4¥0.30
输入/M
¥5.97
输出/M
¥0.04
缓存/M
98.7%
可用率
¥0.34
输入/M
¥4.02
输出/M
¥0.05
缓存/M
98.0%
可用率
¥0.40
输入/M
¥4.36
输出/M
¥0.06
缓存/M
100.0%
可用率
I
InferenceNetfp8¥0.52
输入/M
¥3.69
输出/M
¥0.07
缓存/M
100.0%
可用率
D
Decartfp4¥0.60
输入/M
¥1.21
输出/M
¥0.12
缓存/M
100.0%
可用率
S
Sail Researchfp4¥0.60
输入/M
¥2.01
输出/M
¥0.04
缓存/M
99.9%
可用率
I
Ionstream¥0.67
输入/M
¥5.03
输出/M
¥0.07
缓存/M
97.8%
可用率
W
Wafer¥0.87
输入/M
¥6.64
输出/M
¥0.23
缓存/M
100.0%
可用率
¥0.94
输入/M
¥2.82
输出/M
¥0.03
缓存/M
100.0%
可用率
¥0.99
输入/M
¥3.94
输出/M
¥0.02
缓存/M
100.0%
可用率
¥1.11
输入/M
¥4.43
输出/M
¥0.04
缓存/M
100.0%
可用率
G
GMICloudfp8限时 -40%¥1.21
输入/M
¥4.83
输出/M
¥0.02
缓存/M
100.0%
可用率
¥1.31
输入/M
¥5.23
输出/M
¥0.03
缓存/M
100.0%
可用率
C
CoreWeavefp8¥1.34
输入/M
¥4.36
输出/M
¥0.20
缓存/M
100.0%
可用率
P
Phala限时 -30%¥1.41
输入/M
¥5.63
输出/M
¥0.03
缓存/M
100.0%
可用率
M
Makorafp8¥1.81
输入/M
¥7.71
输出/M
¥0.06
缓存/M
100.0%
可用率
¥1.94
输入/M
¥8.05
输出/M
¥0.05
缓存/M
99.8%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
97.6%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.20
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.05
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.20
缓存/M
98.1%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
100.0%
可用率
S
SiliconFlowfp8¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
99.7%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.20
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.05
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
99.9%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.05
缓存/M
100.0%
可用率
¥2.01
输入/M
¥8.05
输出/M
¥0.04
缓存/M
100.0%
可用率
¥3.02
输入/M
¥12.07
输出/M
¥0.06
缓存/M
100.0%
可用率
¥4.02
输入/M
¥16.09
输出/M
¥0.09
缓存/M
99.2%
可用率
📊 Artificial Analysis 综合指数
39.5
智能指数
编程指数
Agent 指数