GLM 5.3 Flash
GLM-5.3-Flash 是智谱(Z.ai)推出的原生多模态模型,适用于高效编码与长周期 Agent 任务,其混合稀疏与线性注意力架构在保持长上下文准确性方面表现出色。
模态
上下文
1.05Mtokens
最大输出 131K 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 |
|---|---|---|---|---|---|---|
| — | 1.05M | ¥0.12 | ¥0.81 | ¥0.12 | 99.9% | |
O OpenInference | fp4 | 1.05M | ¥0.13 | ¥0.81 | ¥0.13 | 99.9% |
| fp8 | 1.02M | ¥0.29 | ¥0.97 | ¥0.06 | 97.2% | |
W Wafer | nvfp4 | 1.05M | ¥0.50 | ¥1.34 | ¥0.40 | 100.0% |
G GMICloud限时 -50% | fp8 | 1.05M | ¥0.50 | ¥1.67 | ¥0.10 | 98.6% |
S Sail Research | fp4 | 1.05M | ¥0.52 | ¥1.67 | ¥0.17 | 100.0% |
S Sail Research | fp4 | 1.05M | ¥0.52 | ¥1.67 | ¥0.17 | 100.0% |
N Near AI | fp8 | 1.05M | ¥0.54 | ¥3.34 | ¥0.08 | 92.7% |
R Reka | — | 1.05M | ¥0.54 | ¥10.70 | ¥0.27 | 96.7% |
| fp8 | 1.05M | ¥0.56 | ¥1.87 | ¥0.11 | 99.9% | |
I InferenceNet | fp4 | 1.05M | ¥0.80 | ¥2.60 | ¥0.21 | 99.7% |
I InferenceNet | fp4 | 1.05M | ¥0.80 | ¥2.60 | ¥0.21 | 98.5% |
P Phala限时 -15% | nvfp4 | 1.05M | ¥0.85 | ¥2.84 | ¥0.17 | 99.4% |
| — | 1.05M | ¥0.88 | ¥2.93 | ¥0.20 | 99.7% | |
M Makora | — | 1.05M | ¥0.94 | ¥3.14 | ¥0.16 | 76.6% |
P PrimeIntellect | — | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 97.3% |
| nvfp4 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | - | |
| fp8 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | - | |
| fp4 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 83.3% | |
C CoreWeave | nvfp4 | 1.05M | ¥1.00 | ¥3.34 | ¥0.13 | 99.9% |
| fp8 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 95.5% | |
| — | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 99.3% | |
| — | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 100.0% | |
S SiliconFlow | fp8 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 99.0% |
| — | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 98.1% | |
| fp4 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 99.9% | |
| fp8 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | - | |
| — | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 100.0% | |
| fp4 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 99.8% | |
| fp8 | 1.05M | ¥1.00 | ¥3.34 | ¥0.20 | 100.0% | |
I Inceptron | fp8 | 1.05M | ¥1.00 | ¥4.62 | ¥0.50 | 99.9% |
N NextBit | fp4 | 1.05M | ¥1.00 | ¥5.02 | ¥0.33 | 84.0% |
| fp8 | 1.05M | ¥1.06 | ¥4.35 | ¥0.30 | 98.2% | |
| fp4 | 1.05M | ¥1.25 | ¥4.18 | ¥0.25 | 99.7% | |
| — | 1.05M | ¥1.51 | ¥5.02 | ¥0.30 | 100.0% |
¥0.12
输入/M
¥0.81
输出/M
¥0.12
缓存/M
99.9%
可用率
O
OpenInferencefp4¥0.13
输入/M
¥0.81
输出/M
¥0.13
缓存/M
99.9%
可用率
¥0.29
输入/M
¥0.97
输出/M
¥0.06
缓存/M
97.2%
可用率
W
Wafernvfp4¥0.50
输入/M
¥1.34
输出/M
¥0.40
缓存/M
100.0%
可用率
G
GMICloudfp8限时 -50%¥0.50
输入/M
¥1.67
输出/M
¥0.10
缓存/M
98.6%
可用率
S
Sail Researchfp4¥0.52
输入/M
¥1.67
输出/M
¥0.17
缓存/M
100.0%
可用率
S
Sail Researchfp4¥0.52
输入/M
¥1.67
输出/M
¥0.17
缓存/M
100.0%
可用率
N
Near AIfp8¥0.54
输入/M
¥3.34
输出/M
¥0.08
缓存/M
92.7%
可用率
R
Reka¥0.54
输入/M
¥10.70
输出/M
¥0.27
缓存/M
96.7%
可用率
¥0.56
输入/M
¥1.87
输出/M
¥0.11
缓存/M
99.9%
可用率
I
InferenceNetfp4¥0.80
输入/M
¥2.60
输出/M
¥0.21
缓存/M
99.7%
可用率
I
InferenceNetfp4¥0.80
输入/M
¥2.60
输出/M
¥0.21
缓存/M
98.5%
可用率
P
Phalanvfp4限时 -15%¥0.85
输入/M
¥2.84
输出/M
¥0.17
缓存/M
99.4%
可用率
¥0.88
输入/M
¥2.93
输出/M
¥0.20
缓存/M
99.7%
可用率
M
Makora¥0.94
输入/M
¥3.14
输出/M
¥0.16
缓存/M
76.6%
可用率
P
PrimeIntellect¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
97.3%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
-
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
-
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
83.3%
可用率
C
CoreWeavenvfp4¥1.00
输入/M
¥3.34
输出/M
¥0.13
缓存/M
99.9%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
95.5%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
99.3%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
100.0%
可用率
S
SiliconFlowfp8¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
99.0%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
98.1%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
99.9%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
-
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
100.0%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
99.8%
可用率
¥1.00
输入/M
¥3.34
输出/M
¥0.20
缓存/M
100.0%
可用率
I
Inceptronfp8¥1.00
输入/M
¥4.62
输出/M
¥0.50
缓存/M
99.9%
可用率
N
NextBitfp4¥1.00
输入/M
¥5.02
输出/M
¥0.33
缓存/M
84.0%
可用率
¥1.06
输入/M
¥4.35
输出/M
¥0.30
缓存/M
98.2%
可用率
¥1.25
输入/M
¥4.18
输出/M
¥0.25
缓存/M
99.7%
可用率
¥1.51
输入/M
¥5.02
输出/M
¥0.30
缓存/M
100.0%
可用率
📊 Artificial Analysis 综合指数
41.8
智能指数
71.5
编程指数
50.9
Agent 指数