Moonshot AI
Kimi K3.1 HighspeedAbierto
Latency-optimized serving tier of Kimi K3.1 — 1M context at flash-class throughput.
Inteligencia
91.5 #13
AAQI 60.0
Velocidad de Salida
130 t/s
ITL 7.7 ms/tok
Precio combinado (3:1)
$3.13 /1M
Entrada $1.5/M · Salida $8/M
Contexto
1.0M
≈ 2,500 páginas A4 · salida máx. 65,536
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Kimi K3.1 Highspeed
Claude Mythos Preview (#1)
Average (68)
Los ejes del radar se aproximan a partir de las puntuaciones de categoría cuando faltan mediciones directas — ver Metodología.
Fortalezas y compromisos
Donde gana
- 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
Puntos a vigilar
- 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
Casos de uso clave
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.
Inicio rápido para desarrolladores y API
Define primero
MOONSHOT_API_KEY — obtener una clave APIcurl 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);Recursos y enlaces oficiales
Despliegue de API
Comparar hosts y latencia
Throughput, TTFT y precios de caché en Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock y Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Puntuaciones por Fuente
Puntuaciones por Categoría
Resultados de Benchmarks
Duelos directos en vivo
Especificaciones en vivo y arquitectura
Bajo el capó
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
Fecha de Lanzamiento
20 oct 2026
Arquitectura
Kimi K3.1 Highspeed — 2.8T MoE, speculative decode
Parámetros
2.8T MoE
Licencia
Open Weights
Ventana de Contexto
1,000,000 tokens
Salida máx.
65,536 tokens
Modalidades
text + image
Precios de caché
lectura $0.15 · escritura $1.88 /1M
Razonamiento
Niveles de esfuerzo
Precio de Entrada
$1.5/M
Precio de Salida
$8/M
Velocidad de Salida
130 t/s
Tiempo al Primer Token
—
Datos actualizados
22 oct 2026
Hardware e inferencia local
Datos: Convly AI (CC BY 4.0) ↗GPU mín.
Multi-node cluster (e.g. 2× 8-GPU H200 141GB nodes)
Factura mensual estimada
Gasto mensual estimado
$0.00
Calculando ahorro vs GPT-4o…
Citar e insertar
Badge de Markdown para GitHub
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}
}Insertar insignia de puntuación
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