Does Kimi K3 support streaming?

Yes—at least one listed provider advertises streaming.

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.

Which endpoint do I call for Kimi K3?

POST https://api.llmapi.ai/v1/chat/completions with model "kimi-k3" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Kimi K3 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Kimi K3 support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format. Reasoning effort accepts max (default max).

Kimi K3: Self-Hosting & Deployment Guide

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Call Kimi K3 through LLM.API when you want Moonshot-family text generation with unified auth, provider choice, and production-friendly defaults.

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Start Using API

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.

How to run Kimi K3 yourself

Open weights (other), 2779.9B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Kimi K3 VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	5559.9 GB	Estimate	2779.9B params × 2 bytes
FP8	2819.9 GB	File size	RedHatAI/Kimi-K3-FP8-BLOCK

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy Kimi K3 on your hardware

Datacenter GPUsCloud instances

Hardware	BF16 (full precision)	FP8
NVIDIA H200141 GB	—	—
NVIDIA H10080 GB	—	—
NVIDIA A100 80GB80 GB	—	—
NVIDIA L40S48 GB	—	—

GPUs needed per variant.

Hardware	BF16 (full precision)	FP8
AWS p5.48xlarge8× H100 · 640 GB	—	—
AWS p4de.24xlarge8× A100 80GB · 640 GB	—	—
AWS g6e.12xlarge4× L40S · 192 GB	—	—
Google Cloud a3-highgpu-8g8× H100 · 640 GB	—	—
Azure ND H100 v58× H100 · 640 GB	—	—

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLang

pip install -U vllm
vllm serve moonshotai/Kimi-K3

pip install 'sglang[all]'
python -m sglang.launch_server --model-path moonshotai/Kimi-K3

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 10% below list price.

Get API key See API code ↓

Providers

LLM.API routes Kimi K3 to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

moonshot$3 in

$15 out

canopywave$3 in

$15 out

novita$3 in

$15 out

togetherai$3 in

$15 out

nebius$3 in

$15 out

alibaba$3 in

$15 out

melious$3.25 in

$16 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
moonshot	in $3; out $15 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, web search, JSON, structured
canopywave	in $3; out $15 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, web search, JSON, structured
novita10% off	in $3; out $15 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, JSON, structured
togetherai	in $3; out $15 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, web search, JSON, structured
nebius	in $3; out $15 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, web search, JSON, structured
alibaba5% off	in $3; out $15 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, web search, JSON, structured
melious	in $3.25; out $16 per 1M tokens	1M tokens	vision, tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test Kimi K3 right here — free to start.

Kimi K3

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Kimi K3 through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to kimi-k3 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "max"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_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."},
    ],
    reasoning_effort="max",
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "kimi-k3",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "kimi-k3",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "max"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID kimi-k3 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Supported	`image_url content parts`
Web search	Supported	`web_search`
Reasoning effort	max	`reasoning_effort: "max"`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Kimi K3 in production

Best workloadsLLM.API features

What the model is good at.

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.

What you get when you call Kimi K3 through LLM.API.

Unified AI Routing
Reach Kimi K3 and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale Kimi K3.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Practical: Trace prompts, tokens, and errors for Kimi K3 alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Swap Kimi K3 for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

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

LLM API rating for Kimi K3

★ 3.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

3.5Production adoptionActive open-weight community usage

4.2Benchmark qualityArtificial Analysis Intelligence Index 43.6 · LMArena text rank #23, Elo 1476

3.3Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.7Coding 3.7Tool & function calling 3.9Long context 3.9

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

BENCHMARKS

Kimi K3 benchmark scores

Intelligence index

This model44.

Tracked median18.

Scale: 0-100 index points

Output speed

This model39 t/s

Scale: 0-400 tokens per second

Reference price per 1M tokens

Input$3.00

Output$15.00

Bars compare input and output list prices for this model.

Intelligence index+144% vs median

Kimi K3: Self-Hosting & Deployment Guide44

Median tracked model18

Higher is better. The median covers every model tracked by Artificial Analysis.

Output speed

39tok/s

Measured throughput; the full dial is 300 tokens per second.

Reference price / 1M tokens

Input$3.00

Output$15.00

Provider list prices, before the LLM.API discount.

Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.

COMMUNITY

What developers say about Kimi models

LMArena#23 · Elo 1476Arena coding#15 · Elo 1509Speed39 tokens/sSources: LMArena, Artificial Analysis

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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Kimi K3: Self-Hosting & Deployment Guide

What is Kimi K3?

How to run Kimi K3 yourself

Kimi K3 VRAM requirements

GPU requirements: deploy Kimi K3 on your hardware

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Kimi K3 in production

Code assistance

Multilingual drafting

Analytical writing

Structured outputs

Safety-aware replies

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth

When to Use — When NOT to Use

Use it if...

Avoid if...

LLM API rating for Kimi K3

Kimi K3 benchmark scores

Intelligence index

Output speed

Reference price per 1M tokens

What developers say about Kimi models

Frequently Asked Questions

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