Kimi K2.7 Code — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Moonshot AI |
|---|---|
| Tipo | LLM (para programación, MoE) |
| Modalidad | Texto → Texto |
| Parámetros | 1 billón total / 32 mil millones activos (MoE) |
| Ventana de contexto | 256K |
| Licencia | MIT modificada (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2026-06 |
| Precio de entrada | $0.6 /1M |
| Precio de salida | $2.5 /1M |
| Proveedores de API | Moonshot, OpenRouter |
Ejecútelo localmente
| VRAM (4 bits) | ~500 GB |
|---|---|
| GPU mínima | Servidor multi-GPU |
What is Kimi K2.7 Code?
Kimi K2.7 Code is Moonshot AI’s open coding model — a 1-trillion-parameter
mixture-of-experts with 32B active parameters, 384 experts across 61 layers, tuned
specifically for agentic software engineering. It has a 256K context, ships under a
modified-MIT licence, and uses roughly 30% fewer reasoning tokens than its predecessor.
That last figure is the one that pays. In an agentic coding loop the model reasons on
every step, and reasoning tokens are billed as output — so a 30% reduction in tokens spent
thinking is close to a 30% cut in the running cost of the agent, independent of the headline
price. At $0.60 in / $2.50 out per million tokens, K2.7 Code is already an order of magnitude
below frontier coding models; the token efficiency compounds that. The 256K context is
narrower than the 1M offered by several rivals, which matters for whole-repository reasoning
but rarely for the file-and-dependency scope most coding agents actually operate on.
Self-hosting needs around 500 GB at 4-bit, so this is an API model in practice for all but
the largest deployments.
Kimi K2.7 Code pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.600 |
|---|---|
| Salida (por cada millón de tokens) | $2.50 |
| Relación salida/entrada | 4.2× |
| Combinada (4:1 entrada:salida) | $0.980 por 1 millón de tokens |
What Kimi K2.7 Code costs per month
Gasto mensual real con una mezcla entrada:salida de 4:1, es decir, la proporción que realmente genera una carga de trabajo típica de chat o RAG.
| Carga de trabajo | Tokens/mes | Coste mensual |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $1.23 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $25 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $245 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to Kimi K2.7 Code
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| DeepSeek V4-Pro abierta | $0.522 | 47% cheaper |
| DeepSeek V4-Flash abierta | $0.168 | 83% cheaper |
¿Autoalojarlo o pagar por la API?
Kimi K2.7 Code is open-weight, so you can run it yourself. It needs ~500 GB of VRAM at 4-bit (Multi-GPU server). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calculadora de autohospedaje frente a API calcula el punto de equilibrio para su volumen de tokens.
Preguntas frecuentes
How much does Kimi K2.7 Code cost per 1M tokens?
Kimi K2.7 Code costs $0.600 per 1M input tokens and $2.50 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.980 per 1M tokens.
How much does Kimi K2.7 Code cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $25 on Kimi K2.7 Code. A side project (1M in / 0.25M out) costs roughly $1.23.
What is a cheaper alternative to Kimi K2.7 Code?
DeepSeek V4-Pro is the strongest cheaper option in our database at $0.522 per 1M blended — about 47% less than Kimi K2.7 Code. It is also open-weight, so self-hosting is an option.
Can I run Kimi K2.7 Code locally?
Yes. Kimi K2.7 Code is open-weight and needs about ~500 GB of VRAM at 4-bit quantisation (Multi-GPU server).
Why does Kimi K2.7 Code charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Kimi K2.7 Code charges 4.2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Los precios corresponden a las tarifas oficiales publicadas para la API principal del modelo y se revisan periódicamente conforme los proveedores los actualicen. No incluyen descuentos por volumen, procesamiento por lotes ni entradas en caché. Compare todos los modelos uno al lado del otro en la Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).