Kimi is Moonshot AI's flagship model, and its calling card since launch has been context length — handling documents and conversations that other models truncate or fumble. By mid-2026, the k1.5 reasoning variant added strong chain-of-thought capability, making Kimi a serious option for both long-document work and hard analytical tasks.
What changed in 2026
- k1.5 launched as a reasoning model. Moonshot's chain-of-thought variant competes with o1-class models on math, coding, and logical deduction — a step up from the base Kimi 1.5 model.
- Context handling improved. Earlier Kimi models degraded noticeably on documents past ~100K tokens. The 2026 models show better coherence at 200K+ token inputs.
- API availability expanded globally. The Moonshot API now has response times under 2 seconds for most regions including Europe and North America — early versions had significant latency outside APAC.
- Multimodal beta launched. Image understanding was added in beta; text remains the primary strength but document images (PDFs with charts) are now processable.
Kimi model overview
| Model |
Context |
Strength |
| Kimi 1.5 |
200K tokens |
Long-document Q&A, summarization |
| Kimi k1.5 |
128K tokens |
Reasoning, math, hard analysis |
| Kimi Vision (beta) |
32K |
Image + document understanding |
How to access Kimi
Option 1 — Kimi web app (free tier available)
Visit kimi.moonshot.cn (or the international version at kimi.ai). Upload documents up to several MB. Free tier has message limits; paid plan removes most restrictions.
Option 2 — Mobile app
iOS and Android apps are available globally. Useful for on-the-go document review or voice input.
Option 3 — Moonshot API
from openai import OpenAI
client = OpenAI(
api_key="YOUR_MOONSHOT_KEY",
base_url="https://api.moonshot.cn/v1"
)
response = client.chat.completions.create(
model="moonshot-v1-128k", # or moonshot-v1-32k / moonshot-v1-8k
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarize this document: ..."}
]
)
The API is OpenAI-compatible. Key models: moonshot-v1-8k, moonshot-v1-32k, moonshot-v1-128k. Use the largest context tier only when the task genuinely needs it — larger context is priced higher.
Practical use cases for Kimi
Long document analysis. Upload a 200-page PDF, legal contract, or technical spec and ask detailed questions — no chunking required.
Codebase review. Paste an entire repository's worth of code into a 200K context and ask for architecture critique, bug finding, or refactoring suggestions.
Research synthesis. Feed multiple papers into a single conversation and ask Kimi to compare methods, identify contradictions, or synthesize findings.
Chinese-English bilingual work. Kimi handles code-switching and bilingual documents fluidly — useful for APAC teams working across both languages.
How to start
- Start with the web app. Upload a document you work with regularly and test summarization and Q&A quality before building API integrations.
- Choose the right context tier in the API. Use
moonshot-v1-8k for short tasks (cheaper), moonshot-v1-128k only when you genuinely need the space.
- For hard reasoning tasks, try k1.5. It takes longer but produces more accurate results on math, logic, and code debugging.
- Structure your document prompts. For long documents, add: "The following is a [document type]. Please [specific task]." before the content.
Common mistakes
Using long context when short would do. Long context is slower and costs more per call. For a 3-page document, use the 8K tier and save significantly.
Expecting real-time data. Kimi does not have live web search. For current news, prices, or recent events, use Perplexity or Grok.
Pasting unformatted content. Kimi handles markdown, code blocks, and structured text well — paste with formatting preserved for better output.
Ignoring the system prompt for document tasks. Tell Kimi the document type, your role, and what you want up front. It follows instructions well when given them.
What to skip
- k1.5 for casual conversation — the reasoning model is slower and uses more tokens; use Kimi 1.5 for chat and k1.5 only for hard analytical tasks.
- Kimi for image generation — it does not generate images; for multimodal output, use Midjourney, DALL-E, or Flux.
- The web app for automated workflows — use the API for anything you need to script or run at volume.
FAQ
Is Kimi better than ChatGPT for long documents?
For very long inputs (100K+ tokens), Kimi's context handling has historically been stronger. GPT-4o at 128K is comparable for most cases; evaluate both on your specific document type.
What language is Kimi strongest in?
Chinese and English. The training data is heavily weighted to both languages; other languages are supported but quality varies.
How does Kimi pricing compare?
The Moonshot API is priced per token; roughly competitive with Mistral and cheaper than GPT-4 for equivalent context sizes. Check platform.moonshot.cn for current rates.
Is there a free API tier?
New accounts receive a small credit amount to trial the API. Sustained use requires a paid top-up on the platform.
Where to go next
See How to use Qwen in 2026, ChatGPT vs Claude vs Gemini vs Grok in 2026, and Perplexity vs Google AI in 2026.