Kimi K3
Up to 30%Call Kimi K3 through LLM.API when you want Moonshot-family text generation with unified auth, provider choice, and production-friendly defaults.
What is Kimi K3?
On LLM.API, Kimi K3 (kimi-k3) serves as a Moonshot conversational model for products that need 1M tokens context windows and predictable token pricing. It is positioned as a Moonshot offering for production API access.
Providers
LLM.API routes Kimi K3 to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $3000; out — per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $3000; out — per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $3000; out — per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, JSON, structured | |
| in $3000; out — per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $3000; out — per 1M tokens | 1M tokens | tools, streaming, reasoning, web search, JSON, structured | |
| in $3000; out — per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $2660; out — per 1M tokens | 1M tokens | vision, tools, streaming, reasoning, web search, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test Kimi K3 right here — free to start.
Suggestions for your first prompt
Code snippet
Call Kimi K3 through the OpenAI-compatible API — POST /v1/chat/completions.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="kimi-k3",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content){
"model": "kimi-k3",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Code assistance
Helps write, explain, refactor, and debug application code across common languages.
Multilingual drafting
Drafts and translates professional content across major business languages. Relevant for `kimi-k3` workloads on LLM.API.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Reflects Moonshot positioning for this endpoint.
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Tuned to how teams typically call Kimi K3.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Grounded in the model's chat role rather than generic chat claims.
6 Most Valuable Use Cases
- Sales and success email drafting with CRM context with Kimi K3
- Customer support copilots that draft accurate, on-brand replies
- Policy Q&A bots with careful refusal behavior with Kimi K3
- Internal knowledge assistants grounded with your retrieval layer
- Data extraction into JSON for downstream systems with Kimi K3
- Product analytics narration and anomaly explanations
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run Kimi K3 on LLM.API?
Unified AI Routing
Reach Kimi K3 and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale Kimi K3.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Practical: Trace prompts, tokens, and errors for Kimi K3 alongside the rest of your stack.
Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap Kimi K3 for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You need a general-purpose text model for assistants, agents, or content workflows (Kimi K3)
- You need provider failover options exposed for this model id (Kimi K3)
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Kimi K3)
- You want OpenAI-compatible chat completions through a single LLM.API key (Kimi K3)
Avoid if...
- You need pure embedding, OCR, or media generation instead of chat
- Your use case depends on unpublished proprietary benchmarks not listed here
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
- You require on-prem only deployment with no cloud inference
BENCHMARKS
Kimi K3 benchmark scores
Intelligence index
Scale: 0-100 index points
Output speed
Scale: 0-400 tokens per second
Reference price per 1M tokens
Bars compare input and output list prices for this model.
Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.
COMMUNITY
What developers say about Kimi models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Kimi K3.
- 30-day hands-on reviews describe Kimi K2 as a capable, affordable alternative for coding and agent work.
- Community round-ups praise the generous free tier and long context, with speed under load the most common complaint.
- Developers commonly use it as a second opinion model alongside a frontier assistant.
SOURCES
Frequently Asked Questions
Does Kimi K3 support streaming?
Yes—at least one listed provider advertises streaming.
What is Kimi K3?
It is positioned as a Moonshot offering for production API access. On LLM.API it is addressed as `kimi-k3`.
What is the context length for Kimi K3?
Reported context for Kimi K3 is 1M tokens. Always verify the active provider row if multiple providers are listed.
Which providers serve Kimi K3?
LLM.API currently lists: alibaba, bitdeer, canopywave, moonshot, nebius, novita, togetherai. Availability can vary by region and account.
Where is the canonical page for Kimi K3?
https://llmapi.ai/models/moonshotai-kimi-k3/
Is Kimi K3 a chat model?
Yes—Kimi K3 is exposed as a chat/completions-style endpoint on LLM.API.
When should I choose Kimi K3?
You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need Kimi K3.
What are limitations of Kimi K3?
Like other API models, Kimi K3 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Can I use tools or structured outputs with Kimi K3?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
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