Can Kimi K3 handle million-token documents?

Yes. Kimi K3 supports context windows up to 1,000,000 tokens.

When should I choose Kimi K3 instead of Kimi K2.7 Code?

Use Kimi K3 for frontier reasoning, AI agents, and 1M-token workloads.

Is Kimi K3 suitable for AI coding agents?

Yes. It is optimized for long-horizon software engineering tasks.

Does Kimi K3 support configurable reasoning?

Yes. The model supports configurable reasoning effort settings.

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

Independent reporting indicates competitive performance on coding and AI-agent benchmarks.

Which API should developers use for Kimi K3?

The official Kimi API Platform.

What workloads is Kimi K3 best suited for?

Coding, AI agents, enterprise knowledge work, and long-context reasoning.

API Kimi K3 Conveniente | text-to-text

Playground per Kimi K3

Esplora il Playground di Kimi K3 — un ambiente interattivo per testare modelli ed eseguire query in tempo reale. Prova prompt, regola parametri e itera istantaneamente per accelerare lo sviluppo e convalidare i casi d'uso.

Technical Specifications of Kimi K3

Specification	Kimi K3
Developer	Moonshot AI
Model ID	kimi-k3
Model type	Frontier Mixture-of-Experts LLM
Total parameters	2.8 trillion
Experts	896 experts, 16 activated per token
Context window	Up to 1,000,000 tokens
Native multimodality	Visual understanding; Kimi API docs include image and video input examples
Core architecture	Kimi Delta Attention, Attention Residuals, Stable LatentMoE
Main use cases	Coding, software engineering, long-context reasoning, agent tasks, knowledge work, visual reasoning
Hosted API access	Kimi API Platform, CometAPI
Open weights	Scheduled by July 27, 2026

What is Kimi K3?

Kimi K3 is Moonshot AI's flagship large language model and currently the company's most capable foundation model. It is designed for professional software engineering, long-context reasoning, autonomous agents, and complex knowledge-intensive tasks. Unlike previous Kimi models, K3 expands the context window to 1 million tokens, enabling repository-scale coding, long document analysis, and multi-step agent workflows.

The model is built on a large-scale Mixture-of-Experts architecture and represents Moonshot's latest frontier model competing with leading proprietary systems from OpenAI and Anthropic.

Main Features of Kimi K3

1M-token context window enables reasoning across massive codebases, books, research papers, and enterprise documentation without aggressive chunking.
Optimized for software engineering with strong performance on repository-level coding, debugging, UI implementation, and agentic development workflows.
Designed for AI agents capable of long-horizon planning and multi-step task execution instead of isolated prompt completion.
Reasoning-aware inference supports configurable reasoning effort for complex problem solving.
K3 introduces two major architectural innovations: Kimi Delta Attention (KDA) for efficient long-sequence processing; Attention Residuals (AttnRes) to improve information retrieval across model depth
Kimi K3 approximately 2.5× higher scaling efficiency compared with Kimi K2.
Open-weight release strategy, allowing broader research and deployment compared with closed proprietary frontier models.

Benchmark Performance of Kimi K3

Moonshot released one of the most comprehensive benchmark reports among recent frontier models. Public results indicate Kimi K3 is competitive with leading proprietary systems across software engineering, reasoning, and agent tasks.

Benchmark	Kimi K3	GPT-5.6 Sol	Claude Fable 5	Claude Opus 4.8
TerminalBench 2.1	88.3	88.8	84.6	84.6
FrontierSWE	81.2	71.3	86.6	66.7
ProgramBench	77.8	77.6	76.8	71.9
SWE Marathon	42.0	39.0	35.0	40.0
BrowseComp	91.2	90.4	88.0	84.3
DeepSearchQA	95.0	—	94.2	93.1

Key observations

Kimi K3 exceeds GPT-5.6 Sol on FrontierSWE and BrowseComp.
It ranks among the strongest publicly reported coding models.
Long-context software engineering is one of its biggest strengths.
Agent benchmarks place it close to leading proprietary frontier systems, though Moonshot notes the strongest closed models still maintain an overall experience advantage.

Kimi K3

Limitations

Low- and high-reasoning modes are planned but were not available at launch.
Switching into K3 midway through an existing conversation may reduce answer quality because the model relies on complete reasoning history.
The model can occasionally be overly proactive when user intent is ambiguous.
Despite its strong benchmark results, Moonshot explicitly states that K3 does not surpass every proprietary frontier model across all tasks.

Why Use Kimi K3 on CometAPI?

For developers building with multiple foundation models, accessing Kimi K3 through CometAPI offers several operational advantages:

Unified API endpoint for Moonshot, OpenAI, Anthropic, Google, DeepSeek, and other providers.
Faster model switching without rewriting application logic.
Consistent authentication and request formats across providers.
Simplified billing and centralized usage management.
Easier evaluation of Kimi K3 against other frontier models within the same infrastructure.

Kimi K3 is especially valuable for organizations that require long-context reasoning, enterprise document intelligence, and advanced coding agents while retaining the flexibility of an open-weight model.

High-Value Kimi K3 API Use Cases on CometAPI

Coding agents and repository-scale debugging

Kimi K3's strongest public positioning is long-horizon coding. A CometAPI-powered coding agent can send the model repository maps, failing test output, error logs, relevant files, screenshots, and tool results. K3 can then propose a plan, call tools, inspect feedback, and iterate. Use streaming for interactive progress and preserve complete assistant messages between tool turns.

Enterprise document intelligence

The 1M context window is attractive for contracts, policies, filings, support logs, and technical manuals. A practical pattern is to use retrieval for everyday questions, then escalate to K3 when the user asks for cross-document synthesis, contradiction detection, audit prep, or a board-ready memo with citations.

Multimodal product QA

K3's visual understanding makes it useful for reviewing dashboards, landing pages, chart exports, design comps, bug screenshots, and rendered PDFs. Ask for specific outputs: accessibility issues, missing states, likely data-quality problems, mismatched copy, or prioritized bug reports.

Tool-using internal assistants

For internal operations, K3 can combine reasoning with tools such as CRM lookup, billing systems, documentation search, ticket retrieval, deployment status, and analytics queries. Use tool_choice to force retrieval when facts must come from systems of record, then switch back to auto once the model has the right tool context.

FAQ

Prezzi per Kimi K3

Esplora i prezzi competitivi per Kimi K3, progettato per adattarsi a vari budget e necessità di utilizzo. I nostri piani flessibili garantiscono che paghi solo per quello che usi, rendendo facile scalare man mano che i tuoi requisiti crescono. Scopri come Kimi K3 può migliorare i tuoi progetti mantenendo i costi gestibili.

Model	Comet Price (USD / M Tokens)	Official Price (USD / M Tokens)	Discount
kimi-k3	Ingresso:$2.4/M Uscita:$12/M	Ingresso:$3/M Uscita:$15/M	-20%

Codice di esempio e API per Kimi K3

Accedi a codice di esempio completo e risorse API per Kimi K3 per semplificare il tuo processo di integrazione. La nostra documentazione dettagliata fornisce una guida passo dopo passo, aiutandoti a sfruttare appieno il potenziale di Kimi K3 nei tuoi progetti.

POST

/v1/chat/completions

#!/bin/bash

curl --retry 0 "https://api.cometapi.com/v1/chat/completions" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {
        "role": "system",
        "content": "You are Kimi, an AI assistant provided by Moonshot AI. You are proficient in Chinese and English conversations. You provide users with safe, helpful, and accurate answers. You will reject any questions involving terrorism, racism, or explicit content. Moonshot AI is a proper noun and should not be translated."
      },
      {
        "role": "user",
        "content": "Hello, my name is Li Lei. What is 1+1?"
      }
    ],
    "max_completion_tokens": 1024
  }'

cURL Code Example

#!/bin/bash

curl --retry 0 "https://api.cometapi.com/v1/chat/completions" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {
        "role": "system",
        "content": "You are Kimi, an AI assistant provided by Moonshot AI. You are proficient in Chinese and English conversations. You provide users with safe, helpful, and accurate answers. You will reject any questions involving terrorism, racism, or explicit content. Moonshot AI is a proper noun and should not be translated."
      },
      {
        "role": "user",
        "content": "Hello, my name is Li Lei. What is 1+1?"
      }
    ],
    "max_completion_tokens": 1024
  }'

Python Code Example

import os

from openai import OpenAI


client = OpenAI(
    api_key=os.environ["COMETAPI_KEY"],
    base_url="https://api.cometapi.com/v1",
    max_retries=0,
)

completion = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {
            "role": "system",
            "content": (
                "You are Kimi, an AI assistant provided by Moonshot AI. "
                "You are proficient in Chinese and English conversations. "
                "You provide users with safe, helpful, and accurate answers. "
                "You will reject any questions involving terrorism, racism, "
                "or explicit content. Moonshot AI is a proper noun and should "
                "not be translated."
            ),
        },
        {
            "role": "user",
            "content": "Hello, my name is Li Lei. What is 1+1?",
        },
    ],
    max_completion_tokens=1024,
)

print(f"Completion ID: {completion.id}")
print(completion.choices[0].message.content)

JavaScript Code Example

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.COMETAPI_KEY,
  baseURL: "https://api.cometapi.com/v1",
  maxRetries: 0,
});

const completion = await client.chat.completions.create({
  model: "kimi-k3",
  messages: [
    {
      role: "system",
      content:
        "You are Kimi, an AI assistant provided by Moonshot AI. " +
        "You are proficient in Chinese and English conversations. " +
        "You provide users with safe, helpful, and accurate answers. " +
        "You will reject any questions involving terrorism, racism, or explicit content. " +
        "Moonshot AI is a proper noun and should not be translated.",
    },
    { role: "user", content: "Hello, my name is Li Lei. What is 1+1?" },
  ],
  max_completion_tokens: 1024,
});

console.log(`Completion ID: ${completion.id}`);
console.log(completion.choices[0].message.content);

Uptime

Tasso di successo delle richieste negli ultimi 30 giorni, che riflette l'affidabilità di ogni provider di modelli. CometAPI monitora tutti i provider connessi in tempo reale, 24 ore su 24, 7 giorni su 7.

RespondLIVE

13171msAvg. Response

UptimeLIVE

100.0%Avg. Uptime

Versioni di Kimi K3

Il motivo per cui Kimi K3 dispone di più snapshot può includere fattori potenziali come variazioni nell'output dopo aggiornamenti che richiedono snapshot precedenti per coerenza, offrire agli sviluppatori un periodo di transizione per l'adattamento e la migrazione, e diversi snapshot corrispondenti a endpoint globali o regionali per ottimizzare l'esperienza utente. Per le differenze dettagliate tra le versioni, si prega di fare riferimento alla documentazione ufficiale.

Version
kimi-k3

Kimi K3

Playground per Kimi K3

Technical Specifications of Kimi K3

What is Kimi K3?

Main Features of Kimi K3

Benchmark Performance of Kimi K3

Key observations

Limitations

Why Use Kimi K3 on CometAPI?

High-Value Kimi K3 API Use Cases on CometAPI

Coding agents and repository-scale debugging

Enterprise document intelligence

Multimodal product QA

Tool-using internal assistants

FAQ

Can Kimi K3 handle million-token documents?

When should I choose Kimi K3 instead of Kimi K2.7 Code?

Is Kimi K3 suitable for AI coding agents?

Does Kimi K3 support configurable reasoning?

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

What is Kimi K3's maximum context window?

Which API should developers use for Kimi K3?

What workloads is Kimi K3 best suited for?

Prezzi per Kimi K3

Codice di esempio e API per Kimi K3

cURL Code Example

Python Code Example

JavaScript Code Example

Uptime

Versioni di Kimi K3

Kimi K3

Playground per Kimi K3

Technical Specifications of Kimi K3

What is Kimi K3?

Main Features of Kimi K3

Benchmark Performance of Kimi K3

Key observations

Limitations

Why Use Kimi K3 on CometAPI?

High-Value Kimi K3 API Use Cases on CometAPI

Coding agents and repository-scale debugging

Enterprise document intelligence

Multimodal product QA

Tool-using internal assistants

FAQ

Can Kimi K3 handle million-token documents?

When should I choose Kimi K3 instead of Kimi K2.7 Code?

Is Kimi K3 suitable for AI coding agents?

Does Kimi K3 support configurable reasoning?

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

What is Kimi K3's maximum context window?

Which API should developers use for Kimi K3?

What workloads is Kimi K3 best suited for?

Prezzi per Kimi K3

Codice di esempio e API per Kimi K3

cURL Code Example

Python Code Example

JavaScript Code Example

Uptime

Versioni di Kimi K3