阿
Qwen3.6 27B
阿里百炼 · Qwen3.6 27B (Reasoning)
轻量模型,代码能力强
Input
¥3.0
/M Tokens
Output
¥18.0
/M Tokens
Context
262K
Tokens
Capabilities
对话代码
Best For
代码生成
查看官方定价页面
M
在 ModelSage 接入此模型Quick Cost Estimate
¥69.0/月
10,000 次调用 · 输入 500 tokens · 输出 300 tokens
自建可行性
vLLM 官方 · 2026-10-14
Qwen3.6 27B 私有化部署,NVFP4 量化下需显存约 27GB, 至少 1 张 RTX 5090,聚合吞吐约 1,361 tok/s。
NVFP427GB
1×RTX 5090 / 1×RTX PRO 6000
FP833GB
1×RTX PRO 6000 / 1×DGX Spark (GB10)
REDHAT-FP833GB
1×RTX PRO 6000 / 1×DGX Spark (GB10)
vLLM 官方验证过的硬件
DGX Spark (GB10)RTX PRO 6000DGX Station (GB300)RTX 5090昇腾 950DT昇腾 910CArc Pro B60Arc Pro B70MI300X
同样的量走 API:日均 2 亿 token 约 ¥40,500/月。自建是否更便宜取决于卡价与实际利用率。
按实际卡价测算回本周期显存需求与硬件兼容性取自 vLLM 官方 recipes;卡数按「装得下」估算, 未计入张量并行度、互联拓扑与跨节点开销,实际只多不少。吞吐为公式估算值,非实测。
Price Comparison同价位的其他厂商,点进去看详情
Qwen3.6 27BCURRENT
¥3.0 / ¥18.0INPUT
OUTPUT
GLM-4.5
¥3.0 / ¥14.0INPUT
OUTPUT
Mixtral 8x7B
¥3.0 / ¥4.7INPUT
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DeepSeek V4 Flash Vision (max)
¥2.9 / ¥8.8INPUT
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MiMo-V2.6-Pro
¥2.9 / ¥5.8INPUT
OUTPUT
阿里百炼 Models
Qwen3.8 Max (0902)
¥12.0 / ¥36.0Qwen3.8 Max
¥12.0 / ¥36.0Qwen3.8 2.4T A95B
¥12.0 / ¥36.0Qwen3.8-Flash-Next
¥1.0 / ¥3.1Qwen3.8 27B (xhigh)
¥3.0 / ¥12.0Qwen3.7 Max
¥12.0 / ¥36.0Qwen3.6 Max Preview
¥9.0 / ¥54.0Qwen3.8 27B (medium)
¥3.0 / ¥12.0Qwen3.6 Plus
¥2.0 / ¥12.0Qwen3.8 27B (low)
¥3.0 / ¥12.0Qwen3.7 Plus
¥2.0 / ¥8.0Qwen3.5 27B
¥0.60 / ¥4.8Qwen3.5 397B A17B (non-reasoning)
¥1.2 / ¥7.2Qwen3 Max Thinking
¥2.5 / ¥10.0Qwen3.5 Omni Plus
¥7.0 / ¥40.0Qwen3.8 27B (non-reasoning)
¥3.0 / ¥12.0Qwen3.6 27B (non-reasoning)
¥3.0 / ¥18.0Qwen3.5 27B (non-reasoning)
¥0.60 / ¥4.8Qwen3.5 35B A3B
¥0.40 / ¥3.2Qwen3.5 397B A17B
¥1.2 / ¥7.2Qwen3.6 35B A3B
¥1.8 / ¥10.8Qwen3.5 122B A10B (non-reasoning)
¥0.80 / ¥6.4Qwen3 Max Thinking (Preview)
¥2.5 / ¥10.0Qwen3 Max
¥2.5 / ¥10.0Qwen3.5 122B A10B
¥0.80 / ¥6.4Qwen3.6 35B A3B (non-reasoning)
¥1.8 / ¥10.8Qwen3.5 35B A3B (non-reasoning)
¥0.40 / ¥3.2Qwen3 VL 235B A22B (推理)
¥1.0 / ¥10.0Qwen3.5 9B (non-reasoning)
¥0.50 / ¥2.0Qwen3.5 4B
¥0.30 / ¥1.2Qwen3 235B A22B 2507
¥2.0 / ¥20.0Qwen3 Max (Preview)
¥2.5 / ¥10.0Qwen3.5 Omni Flash
¥2.2 / ¥13.3Qwen3 235B 2507
¥2.0 / ¥8.0Qwen3 Coder 480B
¥6.0 / ¥24.0Qwen3 VL 32B (推理)
¥1.0 / ¥10.0Qwen3.5 9B
¥0.50 / ¥2.0Qwen3 Next 80B A3B (推理)
¥1.0 / ¥4.0Qwen3.5 4B (non-reasoning)
¥0.30 / ¥1.2Qwen3 VL 235B A22B (指令)
¥1.0 / ¥10.0Qwen3 30B A3B 2507
¥0.75 / ¥7.5Qwen3 Next 80B A3B (指令)
¥1.0 / ¥4.0Qwen3 Coder 30B A3B
¥1.0 / ¥4.0Qwen3 235B
¥2.0 / ¥20.0Qwen3 VL 30B A3B (推理)
¥0.15 / ¥1.5QwQ-32B
¥2.0 / ¥6.0Qwen3 Coder Next
¥1.0 / ¥4.0Qwen3 32B
¥2.0 / ¥20.0Qwen3 VL 32B (指令)
¥1.0 / ¥10.0Qwen3 235B (non-reasoning)
¥2.0 / ¥8.0Qwen3 14B
¥1.0 / ¥10.0Qwen3 VL 8B (推理)
¥0.15 / ¥1.5Qwen2.5 Max
¥2.4 / ¥9.6Qwen3 VL 30B A3B (指令)
¥0.15 / ¥1.5Qwen3 Omni 30B A3B (推理)
¥1.8 / ¥12.7Qwen2.5 72B
¥2.0 / ¥8.0Qwen3 30B
¥0.75 / ¥7.5Qwen3 30B A3B 2507 (non-reasoning)
¥0.75 / ¥3.0Qwen3 32B (non-reasoning)
¥2.0 / ¥8.0Qwen3 VL 8B (指令)
¥0.15 / ¥1.5Qwen3 8B
¥0.50 / ¥5.0Qwen3 4B
¥0.30 / ¥3.0Qwen3.5 2B
¥0.30 / ¥1.2Qwen3 14B (non-reasoning)
¥1.0 / ¥4.0Qwen3 4B (non-reasoning)
¥0.30 / ¥1.2Qwen3 30B (non-reasoning)
¥0.75 / ¥3.0Qwen2.5 Turbo
¥0.30 / ¥0.60Qwen3.5 2B (non-reasoning)
¥0.30 / ¥1.2Qwen3.5 0.8B
¥0.30 / ¥1.2Qwen3 Omni 30B A3B (指令)
¥1.8 / ¥12.7Qwen3 8B (non-reasoning)
¥0.50 / ¥2.0Qwen3.5 0.8B (non-reasoning)
¥0.30 / ¥1.2Qwen3 1.7B
¥0.30 / ¥3.0Qwen3 0.6B (non-reasoning)
¥0.30 / ¥1.2Qwen3 1.7B (non-reasoning)
¥0.30 / ¥1.2Qwen3 0.6B
¥0.30 / ¥3.0LAST UPDATED: 2026-10-14