Moonshot AI
Kimi K3.1 Highspeed开源
Latency-optimized serving tier of Kimi K3.1 — 1M context at flash-class throughput.
智商指数
91.5 #13
AAQI 60.0
输出速度
130 t/s
ITL 7.7 ms/tok
混合价格 (3:1)
$3.13 /1M
输入 $1.5/M · 输出 $8/M
上下文
1.0M
≈ 2,500 页 A4 · 最大输出 65,536
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Kimi K3.1 Highspeed
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- 130 tok/s — fastest 1M-context open model
- Roughly half the Pro tier’s blended price
- Same agentic tool-use lineage as K3.1 Pro
需要注意的权衡
- Caps reasoning effort vs. Pro — drops on the hardest tasks
- 65K output window vs. 131K on Pro
- Still needs multi-node serving to self-host
杀手级用例
Real-time agent loops
High-throughput tool calling for interactive copilots.
High-volume RAG
Cheap 1M-context reads over large corpora.
Voice-adjacent LLM backends
Sub-second first token keeps spoken agents snappy.
开发者快速上手与 API
请先设置
MOONSHOT_API_KEY — 获取 API 密钥curl https://api.moonshot.ai/v1/chat/completions \
-H "Authorization: Bearer $MOONSHOT_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "kimi-k3-1-highspeed",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.moonshot.ai/v1",
api_key=os.environ["MOONSHOT_API_KEY"],
)
resp = client.chat.completions.create(
model="kimi-k3-1-highspeed",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.moonshot.ai/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.MOONSHOT_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "kimi-k3-1-highspeed",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时对决
实时规格与架构
底层解析
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
- Same K3.1 weights served with aggressive speculative decoding
- Sub-600ms TTFT at 130 tok/s on dedicated capacity
- 1M context retained; 65K max output
- Lower reasoning-effort ceiling than the Pro tier
发布日期
2026年10月20日
架构
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
参数量
2.8T MoE
开源协议
Open Weights
上下文窗口
1,000,000 tokens
最大输出
65,536 tokens
模态
text + image
缓存价格
读 $0.15 · 写 $1.88 /1M
推理
思考力度档位
输入价格
$1.5/M
输出价格
$8/M
输出速度
130 t/s
首Token延迟
—
数据更新
2026年10月22日
硬件与本地推理
数据来源:Convly AI(CC BY 4.0) ↗最低 GPU
Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026kimik31highspeed,
title = {Benchmark Analysis and Intelligence Evaluation of Kimi K3.1 Highspeed},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/kimi-k3-1-highspeed}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
预览
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