Kimi K3 - Kyma API

Overview

Kimi K3 is Moonshot’s 2.8T open-weight multimodal model, designed as a successor to K2.7. It accepts text and image inputs and outputs text, with native support for structured outputs, tool calling, and explicit reasoning steps. The model operates with a 1,048,576-token context window and a maximum output length of 32,768 tokens. On Kyma, the model runs through an OpenAI-compatible endpoint with automatic request failover and exact cost reporting in the usage.cost field. Prompt caching is supported, reducing repeated prefix costs to 10% of the standard input rate. The platform serves it at a medium speed tier, averaging around 12 tokens per second in production. The 32,768-token output cap means it is not suited for generating extremely long documents in a single pass. As a premium-tier model, it carries higher per-token costs than lighter alternatives, and its medium throughput requires planning for latency-sensitive applications.

Specs

Field	Value
Model ID	`kimi-k3`
Best for	Long-horizon agentic coding, reasoning over large repos, vision input
Context window	1.048576M
Max output tokens	33K
Input modalities	Text, Image
Output modalities	Text
Tool calling	Yes
Structured outputs	Yes
Prompt caching	Yes
Speed	Medium
Cost band	Premium
Release stage	Stable

Pricing

	Per 1M tokens
Input	$4.05
Output	$20.25

Use this when

Agentic Workflow Execution — Handles multi-step coding and tool execution across extended sessions without losing context.
Large Repository Analysis — Ingests entire codebases or documentation sets within its million-token window for cross-file reasoning.
Vision-Enabled Document Review — Processes image inputs alongside text to extract and reason over multimodal data.
Structured Data Extraction — Outputs strictly formatted JSON or XML for reliable downstream parsing and API integration.

Not ideal for

It is not the right choice for high-throughput, low-latency chat or generating outputs longer than 32,768 tokens.

Pick something else when

You need faster response times and lower costs for simple chat: use deepseek-v4-flash or qwen3.7-flash.
You require outputs exceeding 32,768 tokens for long-form drafting: use gpt-5.6-terra or gemini-3.6-flash.
You want a cheaper open-weight model for basic code completion: use qwen-3-32b or deepseek-v3.

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-...")

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "..."}],
    tools=[...],  # function calling supported
)

Agent query example

Ask the API which models fit, instead of hardcoding an id:

curl "https://kymaapi.com/v1/models?recommended_for=write-code&tools=true&quality_tier=frontier-open"

FAQ

Does Kyma support prompt caching for this model? Yes, repeated prompt prefixes are cached automatically and billed at 10% of the standard input rate. What happens if the serving path fails during a request? Kyma routes every request through automatic failover, so degraded paths are silently rerouted without dropping the call. Can I use this model for image analysis? Yes, Kimi K3 accepts both text and image inputs, though it only outputs text.

​Overview

​Specs

​Pricing

​Use this when

​Not ideal for

​Pick something else when

​Example

​Agent query example

​FAQ