Moonshot AI
Kimi K3.1 Pro开源
Moonshot’s 2.8T-parameter MoE flagship with 1M context, native multimodality and frontier reasoning.
智商指数
94.2 #3
AAQI 68.0
输出速度
45 t/s
ITL 22.2 ms/tok
混合价格 (3:1)
$8 /1M
输入 $4/M · 输出 $20/M
上下文
1.0M
≈ 2,500 页 A4 · 最大输出 131,072
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Kimi K3.1 Pro
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
- Top-3 podium score — frontier reasoning and agentic tool use
- 1M context with open weights
- Strong SWE-bench / Terminal-Bench results for long-horizon coding
- Multimodal document and chart understanding at 96+
需要注意的权衡
- Multi-node cluster required for self-hosting (2.8T MoE)
- 45 tok/s output — slower than flash-tier peers
- Premium pricing vs. smaller open models
杀手级用例
Long-horizon coding agents
131K output window rewrites whole files; leads SWE-bench-class leaderboards.
Agentic browsing & research
Tool-use loops over 1M-token contexts — the K-line’s signature workload.
Multimodal enterprise RAG
Charts, scans and PDFs ingested natively alongside text.
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-pro",
"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-pro",
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-pro",
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 Pro — 2.8T MoE, ~40B active
- Sparse MoE routing — ~40B active per token out of 2.8T total
- Native multimodality: text + vision in a single weight set
- Reasoning effort levels with a 131K max output window
- INT4 checkpoints for cheaper multi-node serving
发布日期
2026年10月20日
架构
Kimi K3.1 Pro — 2.8T MoE, ~40B active
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
131,072 tokens
模态
text + image
缓存价格
读 $0.4 · 写 $5 /1M
推理
思考力度档位
输入价格
$4/M
输出价格
$20/M
输出速度
45 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{llmpodium2026kimik31pro,
title = {Benchmark Analysis and Intelligence Evaluation of Kimi K3.1 Pro},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/kimi-k3-1-pro}
}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
Markdown
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