How much does Kimi K3 cost to use via API?

Moonshot has not published per-token API pricing for Kimi K3 in the quickstart docs yet — the page links out to a chat-k3 pricing table that lists flat pay-as-you-go rates with no context-length tiering. In the Kimi Code product, K3 is gated behind the Moderato plan for 256K context and the Allegretto+ plan for the full 1M-token context.

How is Kimi K3 different from Kimi K2.7?

Kimi K3 jumps from Kimi K2.7's 1T Mixture-of-Experts and 256K context to a 2.8T MoE with a 1M-token context window, and swaps in a new Delta Attention hybrid architecture with Attention Residuals. K3 also adds native visual understanding, whereas K2.7-Code was text-only, and it always runs in thinking mode instead of exposing an instant variant.

Are Kimi K3 weights open, like Kimi K2.6 was?

Moonshot has not published Kimi K3 weights or a Hugging Face model card as of launch. Prior generations — Kimi K2.6 and Kimi K2.7 — were open-weighted on Hugging Face under Modified MIT, but K3 today is only reachable through kimi.com, the Kimi app, Kimi Code, and the Moonshot API.

How does Kimi K3 compare to Claude Opus 4.8 and GPT-5.5?

Independent reviewers report that Kimi K3 outperforms Claude Opus 4.8 and GPT-5.5 on several coding benchmarks and lands between GPT-5.6 and Anthropic's Claude Fable 5 in overall quality. Moonshot has not published its own benchmark table on the K3 quickstart page, so treat the comparisons as third-party until an official model card lands.

What can Kimi K3 process in a single request?

Kimi K3 supports up to 1,048,576 input tokens and up to 1,048,576 completion tokens, with a default max completion of 131,072. Inputs can be text, base64-encoded images, and video files uploaded through Moonshot's files API, and outputs stream as separate reasoning and content deltas so clients can display the thinking trace live.

Moonshot AI · 2026-07-16 · seismic

Kimi K3 — Moonshot's 2.8T flagship with 1M context lands on web, app, and API

Moonshot AI launches Kimi K3, a 2.8-trillion-parameter Mixture-of-Experts model with a 1M-token context, a new Delta Attention hybrid, and native vision. K3 is live on web, app, Kimi Code, and the api.moonshot.ai OpenAI-compatible API.

Kimi K3 Quickstart on the Moonshot AI platform docs.

Moonshot AI ships Kimi K3 today, jumping to a 2.8T mixture-of-experts with a 1M-token context and native vision.

Key specs

Context window	1M tokens
Total parameters	2.8T
Max completion	1,048,576 tokens

Quick facts

Maker	Moonshot AI
Size	2.8T total parameters (Mixture-of-Experts)
Context window	1M tokens
Architecture	Delta Attention (hybrid linear) + Attention Residuals
Modalities in	Text, image, video (native visual understanding)
API model name	kimi-k3 at api.moonshot.ai/v1 (OpenAI-SDK compatible)
Availability	kimi.com, Kimi mobile app, Kimi Code (Moderato tier for 256K, Allegretto+ for 1M), API
Reasoning	Thinking mode always on; reasoning_effort="max" only setting currently exposed

What is it?

Kimi K3 is Moonshot AI's new flagship Mixture-of-Experts model, live as of July 16, 2026 on kimi.com, the Kimi mobile app, the Kimi Code coding-agent tier, and the api.moonshot.ai OpenAI-compatible API. K3 takes text, images, and video in, runs with thinking mode always on, and reasons across a 1-million-token context.

How does it work?

Under the hood the model uses a new Delta Attention hybrid — a linear attention path fused with Attention Residuals — that lets a 2.8T-parameter MoE keep the KV cache tractable at 1M tokens. The API exposes a single `kimi-k3` model id compatible with the OpenAI SDK, streams reasoning and content as separate deltas, supports strict-schema structured JSON, dynamic tool loading, and automatic context caching without a manual cache id.

Why does it matter?

Kimi K3 is the first Chinese frontier model to cross 2 trillion parameters and ship end-to-end product access on day one. Third-party testers report K3 beating Claude Opus 4.8 and GPT-5.5 on coding benchmarks and landing between GPT-5.6 and Claude Fable 5 overall, which puts Moonshot's price-per-quality within striking distance of Anthropic and OpenAI even before weights or a paper appear.

Who is it for?

Engineers who want a 1M-context coding and knowledge model with an OpenAI-compatible API, and teams evaluating Chinese frontier models against Claude and GPT-5.

Frequently asked questions

How much does Kimi K3 cost to use via API?: Moonshot has not published per-token API pricing for Kimi K3 in the quickstart docs yet — the page links out to a chat-k3 pricing table that lists flat pay-as-you-go rates with no context-length tiering. In the Kimi Code product, K3 is gated behind the Moderato plan for 256K context and the Allegretto+ plan for the full 1M-token context.
How is Kimi K3 different from Kimi K2.7?: Kimi K3 jumps from Kimi K2.7's 1T Mixture-of-Experts and 256K context to a 2.8T MoE with a 1M-token context window, and swaps in a new Delta Attention hybrid architecture with Attention Residuals. K3 also adds native visual understanding, whereas K2.7-Code was text-only, and it always runs in thinking mode instead of exposing an instant variant.
Are Kimi K3 weights open, like Kimi K2.6 was?: Moonshot has not published Kimi K3 weights or a Hugging Face model card as of launch. Prior generations — Kimi K2.6 and Kimi K2.7 — were open-weighted on Hugging Face under Modified MIT, but K3 today is only reachable through kimi.com, the Kimi app, Kimi Code, and the Moonshot API.
How does Kimi K3 compare to Claude Opus 4.8 and GPT-5.5?: Independent reviewers report that Kimi K3 outperforms Claude Opus 4.8 and GPT-5.5 on several coding benchmarks and lands between GPT-5.6 and Anthropic's Claude Fable 5 in overall quality. Moonshot has not published its own benchmark table on the K3 quickstart page, so treat the comparisons as third-party until an official model card lands.
What can Kimi K3 process in a single request?: Kimi K3 supports up to 1,048,576 input tokens and up to 1,048,576 completion tokens, with a default max completion of 131,072. Inputs can be text, base64-encoded images, and video files uploaded through Moonshot's files API, and outputs stream as separate reasoning and content deltas so clients can display the thinking trace live.

Try it

curl https://api.moonshot.ai/v1/chat/completions -d '{"model":"kimi-k3","messages":[…]}'