Moonshot AI

Kimi K3.1 Highspeed开源

Latency-optimized serving tier of Kimi K3.1 — 1M context at flash-class throughput.

#13发布于 2026年10月20日·Open Weights·2.8T MoE
textimage1.0M ctx推理
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)
Coding (60)Mathematics (88)Hard Science (GPQA) (92)Agentic & Tool Use (74)Knowledge (MMLU) (38)Vision / Multimodal (90)

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."}]
  }'

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
查看提供商矩阵 →

各榜单得分

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
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
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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
Kimi K3.1 Highspeed LLMPodium Score
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