Kimi Chat (by Moonshot AI) processes massive context at a price point that undercuts US giants, charging just $0.60 per million input tokens for its latest K2.5 model. While DeepSeek grabbed headlines for coding, Kimi staked its claim on memory, popularizing the "2 million character" context window long before Google's Gemini made 1M+ tokens standard. It is effectively the "Claude of China"—a research-heavy LLM specialized in reading extensive documents, legal contracts, and novels without losing the plot.
For developers, the math is compelling. Processing 10,000 financial reports (avg. 3,000 tokens each) totals 30 million input tokens. On GPT-4o ($2.50/1M), that's $75. On Kimi K2.5 ($0.60/1M), it's $18. If you use Kimi's automatic context caching for repeated prompts, the price drops to $0.15/1M—matching DeepSeek's floor pricing. The API is fully OpenAI-compatible, meaning migration is often just a base URL change.
The technical backbone is impressive. The K2.5 model uses a Mixture-of-Experts (MoE) architecture with 1 trillion parameters (32B active), achieving parity with GPT-4 on many internal benchmarks. It supports a 256k token API limit (the "2 million characters" is marketing shorthand for the consumer web interface, though the API's 256k tokens roughly equates to 400k-500k words). Performance on RAG (Retrieval Augmented Generation) tasks is excellent; it resists the "lost in the middle" phenomenon better than many open-weights models.
However, the trade-offs are geopolitical. Your data resides in Beijing, subject to Chinese regulations. While Kimi supports English well, its safety filters are tuned for Chinese compliance, which can trigger unexpected refusals on sensitive topics. Additionally, while it supports function calling and "thinking" modes (like K2 Thinking), it trails DeepSeek V3 in pure coding raw performance.
Skip Kimi if you require GDPR compliance or strict US data residency. Use it if you need to summarize 500-page PDFs or analyze massive unstructured datasets on a budget and can legally process that data in China.
Pricing
Moonshot operates a simple pay-per-token model with no recurring subscription for the API. The headline rate is $0.60/1M input and ~$2.50/1M output for the K2.5 model. The real differentiator is automatic context caching: if you reuse the same file or prompt prefix, input costs drop by 75% to $0.15/1M, making it viable for heavy RAG applications.
Free Tier: The consumer chat (web/app) is generous and largely free for testing long-context capabilities, though rate-limited during peak China hours (evening GMT+8). The API offers a small initial credit (usually ~15 CNY) but no permanent free tier volume.
Cost Cliff: Watch out for the output tokens ($2.50/1M). If you ask Kimi to rewrite huge documents rather than just summarize them, the bill climbs 4x faster.
Technical Verdict
The API is a drop-in replacement for OpenAI's ChatCompletion endpoint (base_url="https://api.moonshot.cn/v1"). Reliability is generally high, though latency from Western Europe/US can be variable due to the Great Firewall. The SDK ecosystem is mature since it reuses OpenAI's libraries. Note that while the consumer app boasts "2 million characters," the API currently enforces a hard cap of 256k tokens per request. Documentation is clean but primarily in Chinese, with English translations sometimes lagging.
Quick Start
from openai import OpenAI
client = OpenAI(
api_key="MOONSHOT_API_KEY",
base_url="https://api.moonshot.cn/v1"
)
response = client.chat.completions.create(
model="kimi-k2.5",
messages=[{"role": "user", "content": "Summarize this 10k word file..."}]
)
print(response.choices[0].message.content)Watch Out
- Data Residency: All API traffic is processed and stored in mainland China.
- Marketing mismatch: "2 Million Characters" applies to the chat app; API is capped at 256k tokens.
- Compliance filters: Prompts touching on politically sensitive topics in China may be rejected or filtered silently.
- Rate Limits: Free API credits expire quickly and have low concurrency limits until you top up.