Kimi K2.7 Code — Spezifikationen
| Entwickler | Moonshot AI |
|---|---|
| Typ | LLM (Programmierung, MoE) |
| Modality | Text → Text |
| Parameter | 1 Bio. insgesamt / 32 Mrd. aktiv (MoE) |
| Kontextfenster | 256 K |
| Maximale Ausgabe | — |
| Lizenz | Geänderte MIT-Lizenz (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2026-06 |
| Eingabepreis | 0,60 $/1 Mio. |
| Ausgabepreis | 2,50 $/1 Mio. |
| API-Anbieter | Moonshot, OpenRouter |
Lokal ausführen
| VRAM (4-Bit) | ~500 GB |
|---|---|
| Mindest-GPU | Multi-GPU-Server |
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
| Eingabe (pro 1 Mio. Token) | $0.600 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $2.50 |
| Output/input ratio | 4.2× |
| Blended (4:1 in:out) | $0.980 per 1M tokens |
What Kimi K2.7 Code costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Workload | Tokens/Monat | Cost / month |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $1.23 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $25 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $245 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to Kimi K2.7 Code
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| DeepSeek V4-Pro offenere | $0.522 | 47% cheaper |
| DeepSeek V4-Flash offenere | $0.168 | 83% cheaper |
Self-host or pay the 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 Selbsthosting-vs.-API-Rechner works out the break-even point for your token volume.
Häufig gestellte Fragen
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.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the Datenbank für KI-Modelle oder das LLM-Leaderboard.
