AIREITER
DOC APIPREZZI
TEMPLATE
MoonshotText Chat

Kimi K3 Chat Playground e API AI

Prova Kimi K3 online per codebase con contesto esteso, raccolte di ricerca, revisione di documenti e memoria dell'agente tramite una Chat Completions API compatibile con OpenAI.

InputUfficiale $3.00 per 1 M di tokenAIReiter $1.50 per 1 M di tokenOutputUfficiale $15.00 per 1 M di tokenAIReiter $7.50 per 1 M di tokenLettura cacheUfficiale $0.30 per 1 M di tokenAIReiter $0.15 per 1 M di token
Esegui con API
PlaygroundReadmeAPI

INPUT

1
2
3
4
5
6
7
8
9
10

Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

npm install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.AIREITER_API_KEY,
  baseURL: "https://aireiter.com/api/v1",
});

Run kimi-k3:

const response = await client.chat.completions.create({
    "model": "kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Send a message"
      }
    ],
    "max_tokens": 4096
  });

console.log(response);

Stream the response instead:

const stream = await client.chat.completions.create({
  ...{
    "model": "kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Send a message"
      }
    ],
    "max_tokens": 4096
  },
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

Install the official OpenAI client — AIReiter speaks the same protocol, so only the base URL changes:

pip install openai

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Point the client at AIReiter:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIREITER_API_KEY"],
    base_url="https://aireiter.com/api/v1",
)

Run kimi-k3:

response = client.chat.completions.create(
      model = "kimi-k3",
      messages = [
        {
          role = "user",
          content = "Send a message"
        }
      ],
      max_tokens = 4096
)

print(response)

Stream the response instead:

stream = client.chat.completions.create(
      model = "kimi-k3",
      messages = [
        {
          role = "user",
          content = "Send a message"
        }
      ],
      max_tokens = 4096,
    stream=True,
)

for event in stream:
    print(event)

Set the AIREITER_API_KEY environment variable:

export AIREITER_API_KEY=<paste-your-key-here>

Run kimi-k3 against AIReiter's API:

curl -s -X POST \
  -H "Authorization: Bearer $AIREITER_API_KEY" \
  -H "Content-Type: application/json" \
  "https://aireiter.com/api/v1/chat/completions" \
  -d '{
  "model": "kimi-k3",
  "messages": [
    {
      "role": "user",
      "content": "Send a message"
    }
  ],
  "max_tokens": 4096
}'

Add "stream": true to the body to receive the response as server-sent events.

OUTPUT

Example

A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that "production-ready" is a property you can demonstrate, not a hope.

Here are the first five steps, with the evidence you collect at each.

Step 1 — Get it building and the baseline green (half day) Read the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker. Evidence: The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. "this touches cron jobs and payments").

Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day) Don't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written. Evidence: An annotated trace — file → function → what it does — for that slice. A written list of "conventions I must follow" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.

Step 3 — Spike the core path (day 2) Build the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know. Evidence: A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.

Step 4 — Write the contract before the code (half of day 2 / day 3) Once the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification. Evidence: A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.

Step 5 — Implement in small, verified increments (days 3–4) Replace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed). Evidence: A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.

Steps 6+ would be the things that actually make it "shipped": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say "this will work in production" with evidence behind it, instead of a guess.

{
  "model": "kimi-k3",
  "input": {
    "model": "kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Send a message"
      }
    ],
    "max_tokens": 4096
  },
  "output": "A codebase you don't know is a risk, not a hurdle. The plan should burn down that risk in order: understand, run, trace, spike, then make the change small enough that \"production-ready\" is a property you can demonstrate, not a hope.\n\nHere are the first five steps, with the evidence you collect at each.\n\n**Step 1 — Get it building and the baseline green (half day)**\nRead the README, the manifest(s), the CI config, and any architecture docs. Boot the app locally, run the full test suite, run the linter/typechecker.\n*Evidence:* The exact commands that reproduce a clean build and a passing test suite from a fresh checkout (recorded output, not memory of it). A one-page map you wrote yourself: where the entry points are, the layers, the build/test toolchain, and the main risks to your feature (e.g. \"this touches cron jobs and payments\").\n\n**Step 2 — Trace the smallest end-to-end slice that resembles your feature (half day)**\nDon't start with your feature. Pick a neighboring one that already works and follow it from entry to persistence. Note the conventions along the way: how errors surface, how config is injected, how logging is done, how tests are written.\n*Evidence:* An annotated trace — file → function → what it does — for that slice. A written list of \"conventions I must follow\" (not intuitions; things you observed in real code). A named shortlist of the 3–5 files you will actually touch. If you can't produce this trace, you're not ready to write code.\n\n**Step 3 — Spike the core path (day 2)**\nBuild the ugly version: hardcoded values, no error handling, no tests. The purpose is to confirm the path you traced in step 2 is real and to surface what you didn't know you didn't know.\n*Evidence:* A working spike demonstrating the feature's central data path, alongside a list of every assumption the spike broke and what you corrected. That correction list is the most valuable document in this whole plan.\n\n**Step 4 — Write the contract before the code (half of day 2 / day 3)**\nOnce the spike proves the path, pin down what production needs: the inputs/outputs, the error cases, where it sits in the conventions from step 2. Then write the tests — they'll be red, but they're the specification.\n*Evidence:* A one-to-two-page design doc, an agreed interface/API shape (with the team if there is one), and a red test suite that encodes intended behavior. If you can't write the contract without consulting the code, you haven't finished step 2.\n\n**Step 5 — Implement in small, verified increments (days 3–4)**\nReplace the spike with the real thing in small commits, each one keeping the suite green, leaning on the existing patterns. Run lint/tests/typecheck per commit — CI, not just locally — and exercise the actual path against a real instance (staging or a local environment that isn't stubbed).\n*Evidence:* A branch with progressive commits, each green in CI; coverage on the new code; something that proves it works against reality (a test result, a log trace, a screenshot); and a review by at least one person who knows the codebase. The review counts as evidence — an unfamiliar codebase has tribal knowledge you cannot extract from the files alone.\n\nSteps 6+ would be the things that actually make it \"shipped\": a migration plan and its rollback, feature flagging, observability, the release and post-release verification. But the first five get you to a reviewed, green, working slice in staging — which is the point at which you can say \"this will work in production\" with evidence behind it, instead of a guess.",
  "metrics": {
    "input_tokens": 134,
    "output_tokens": 2354,
    "generated_in_seconds": 42.7
  },
  "example": true
}
Generated in
42.7 seconds
Token input
134
Token output
2354
Tokens per second
55.13 tokens / second
Time to first token
-

Dettagli del modello

Usa la stessa chiave del modello nel Playground, nelle richieste API e nei flussi di lavoro interni.

ID modello
kimi-k3
Provider
Moonshot
Protocollo
OpenAI Chat Completions
Finestra di contesto
1,048,576 token
Output massimo
131,072 token
Token input
150 crediti / 1 M token
Token output
750 crediti / 1 M token
Lettura cache
15 crediti / 1 M token
Scrittura cache
-

Cosa puoi fare con Kimi K3

Scegli Kimi K3 quando il contesto è il collo di bottiglia e una singola richiesta deve includere un ampio insieme di codice, documenti, evidenze o cronologia dell'agente.

Revisione di contesto esteso

Mantieni insieme in un unico contesto operativo grandi codebase, raccolte di documenti o evidenze di ricerca.

Analisi del repository

Traccia le relazioni tra i file e discuti le modifiche con una maggiore quantità di stato del progetto disponibile.

Sintesi della ricerca

Confronta le affermazioni tra molti appunti e fonti prima di produrre una conclusione strutturata.

Valutazione della memoria dell'agente

Esamina lunghe tracce degli strumenti e decisioni precedenti per individuare dove un flusso di lavoro automatizzato è andato storto.

Casi d'uso di Kimi K3

Ideale per workflow in cui conservare più evidenze nel prompt può evitare un chunking prematuro, il retrieval o la perdita dello stato del progetto.
01

Revisione della codebase

Analizza più contesto del repository in una singola richiesta.

02

Lunghe raccolte di documenti

Esamina contratti, policy, report o raccolte di ricerca.

03

Analisi delle tracce dell'agente

Ispeziona lunghe cronologie degli strumenti e lo stato conservato.

04

Prototipi ad alto contesto

Verifica se più contesto migliora i risultati prima di costruire il retrieval.

Come usare Kimi K3

Prova il modello in tre semplici passaggi.

01

Scegli le impostazioni

Imposta i controlli di risposta e le opzioni di caricamento supportate dal modello.

02

Invia un prompt

Descrivi l'attività, aggiungi il contesto rilevante e rivedi la risposta in streaming e l'uso dei token.

03

Collega l'API

Usa l'endpoint documentato e la tua API key per integrare lo stesso modello nel tuo prodotto.

Crea con la API di Kimi K3

Passa da un test interattivo a un'integrazione in produzione con controlli prevedibili e reportistica sull'utilizzo.

Protocolli familiari

Usa il protocollo API configurato per questo modello, inclusa lo streaming dove disponibile.

Visibilità sull'utilizzo

Tieni traccia dei token di input, dei token di output e dei crediti consumati dopo ogni risposta.

Controlli specifici del modello

Passa i parametri di generazione supportati invece di affidarti a valori predefiniti generici.

Un solo account e saldo

Testa e utilizza i modelli di testo supportati tramite lo stesso account AIReiter e lo stesso sistema di fatturazione.

FAQ su Kimi K3

Domande frequenti sul playground online, sui prezzi e sull'accesso API.

/ 01

Per cosa è migliore Kimi K3?

Usalo quando una richiesta necessita di un ampio insieme di codice, documenti, evidenze di ricerca o cronologia dell'agente.

/ 02

Quale finestra di contesto è disponibile per Kimi K3?

AIReiter indica Kimi K3 con una finestra di contesto da 1.048.576 token; verifica i limiti del client e i timeout prima di inviare richieste molto grandi.

/ 03

Posso chiamare Kimi K3 con un client in stile OpenAI?

Sì. AIReiter lo espone tramite un endpoint Chat Completions compatibile con OpenAI.

/ 04

Come viene prezzato Kimi K3?

Le tariffe attuali per input, cache-read e token di output sono mostrate da AIReiter; confermale prima dell'uso in produzione.

/ 05

Quando dovrei scegliere invece un modello più piccolo?

Usa un modello più leggero per richieste brevi e senza stato che non traggono vantaggio dalla capacità di lungo contesto di Kimi K3.

AIREITER

Domande? Contattaci a
[email protected]

新速率有限公司NEWRATE LIMITED香港九龍花園街 2-16 號好景商業中心 2304 室Room 2304, Haojing Commercial Center, 2-16 Garden Street, Kowloon, Hong Kong

LLM

GPT-6 AstraGemini 3.8 FlashClaude Fable 5.1GLM-5.3 FlashGemini 3.6 Flash

Video IA

Gemini Omni 1.1 Flash ExtMiniMax H3Kling 3.0 Motion ControlKling 3.0 TurboKling 3.0

Immagine IA

GPT-Image 2.5Grok Imagine Image 2.0Midjourney V8.1Midjourney V7Z-Image Turbo

Blog

Vedi Tutto →

Azienda

Informativa sulla privacyTermini di servizioPolitica di rimborso

© 2026 AIReiter. Tutti i diritti riservati.