Kimi K2 Free API Complete Guide: 1T MoE Flagship + 128K Context + Agentic Tool Calling at $0
TL;DR: Kimi K2 is Moonshot's 1T-parameter MoE flagship (32B active) with 128K context and native agentic tool-calling. Access it at $0 via Moonshot + OpenRouter :free + Apishare. This guide benchmarks latency/rate-limits and gives ready-to-run curl/Python/Node snippets.
Why Kimi K2?
- 1T MoE, 32B active: 1T total, only 32B activated per token — strong capability at low inference cost
- 128K context: Native 128K, tested stable at 100K+ for long docs and repo-level coding
- Agentic-native: Trained for agents — accurate tool-calling and multi-step planning, parallel tool calls supported
- Code & reasoning: LiveCodeBench / SWE-bench / MATH on par with Claude 4 Sonnet and DeepSeek-V3, strong in Chinese long-form
- Free in 3 ways: Moonshot trial credit + OpenRouter
moonshotai/kimi-k2:free+ Apishare aggregation — all $0 to start
Model Lineup
| Model | Type | Context | Active Params | Highlight | Free Access |
|---|---|---|---|---|---|
| Kimi K2 | MoE | 128K | 32B / 1T | Agentic / Tool Calling / Long Context | Moonshot / OpenRouter :free / Apishare |
| Kimi K2 Thinking | MoE+Thinking | 128K | 32B / 1T | Deep reasoning toggle | OpenRouter :free |
| Kimi K1.5 | MoE | 128K | - | Multimodal reasoning | Moonshot |
| DeepSeek-V3.1 | MoE | 128K | 37B / 671B | General flagship | OpenRouter :free |
| Qwen3 235B-A22B | MoE | 128K | 22B / 235B | Thinking mode | DashScope / OpenRouter :free |
Free Channel Comparison (tested 2026-09-02)
| Channel | Model ID | Price | Rate Limit | Context | Best For |
| Moonshot Official | moonshot-v1-128k / kimi-k2 | ~¥15 trial, then $0.6/1M input | 60 RPM | 128K | Most stable, agentic |
| OpenRouter :free | moonshotai/kimi-k2:free | $0 / $0 | 20 RPM / 50 req/day | 128K | Zero-cost trial |
| Apishare Aggregation | kimi-k2 | $0 (aggregated) | 30 RPM | 128K | Direct CN link, unified auth |
Get Started in 5 Minutes
Option 1: Moonshot Official
curl https://api.moonshot.cn/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MOONSHOT_API_KEY" \
-d '{
"model": "moonshot-v1-128k",
"messages": [{"role": "user", "content": "Write a quicksort in Python and explain complexity"}],
"temperature": 0.7
}'
Option 2: OpenRouter Free ($0)
curl https://openrouter.ai/api/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "HTTP-Referer: https://apishare.cc" \
-H "X-Title: Apishare" \
-d '{
"model": "moonshotai/kimi-k2:free",
"messages": [{"role": "user", "content": "Summarize a 100K-word report"}],
"temperature": 0.7
}'
Option 3: Python (OpenAI SDK)
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="sk-or-v1-xxx",
default_headers={"HTTP-Referer": "https://apishare.cc", "X-Title": "Apishare"}
)
resp = client.chat.completions.create(
model="moonshotai/kimi-k2:free",
messages=[{"role": "user", "content": "Plan a 3-step agent task: search-summarize-write"}],
tools=[{"type": "function", "function": {"name": "web_search", "description": "Search web", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}}],
tool_choice="auto"
)
print(resp.choices[0].message.content)
Option 4: Node.js
import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://openrouter.ai/api/v1", apiKey: process.env.OPENROUTER_API_KEY, defaultHeaders: { "HTTP-Referer": "https://apishare.cc", "X-Title": "Apishare" }});
const res = await client.chat.completions.create({ model: "moonshotai/kimi-k2:free", messages: [{ role: "user", content: "Explain Kimi K2 MoE routing" }] });
console.log(res.choices[0].message);
Benchmarks (2026-09-02)
| Channel | TTFB | 512 tokens | Rate-limited | Notes |
|---|---|---|---|---|
| Moonshot Official | 320ms | 2.1s | No | Most stable, no truncation at 128K |
| OpenRouter :free | 580ms | 3.4s | 20 RPM | 50 req/day, use Referer header |
| Apishare | 410ms | 2.6s | No | Good for batch |
Long-context test: 98K report + 3 follow-ups, K2 kept coherence without forgetting; 3 parallel tool calls succeeded 100%.
Pitfalls
- 128K != infinite: Reserve
max_tokensfor output, avoid truncation (100K in + 4K out) - Free rate limits: OpenRouter :free is 20 RPM / 50 req/day — add
sleep(3)or switch to Moonshot/Apishare for batch - Thinking mode: K2 Thinking needs explicit model ID; base K2 won't auto-think
- Tool calling: Must use OpenAI-compatible
toolsfield withtool_choice: "auto"for parallel calls - Temperature: 0.6-0.7 recommended for long Chinese output
Summary
- Zero-cost 1T MoE + Agent:
moonshotai/kimi-k2:freeone-line curl - Production: Moonshot
moonshot-v1-128k+ Apishare as backup - Long-context/Agent: K2 is one of the most stable free 128K + tool-calling options
🚀 Get Started: One-Click Free API Access
Want to call all the free models above with a single API key, no need to sign up for each provider? Apishare.cc provides a unified API Key — one key, 100+ models, free models at zero cost.
👉 Register on Apishare.cc → Get your unified API Key
📊 Want to see more free model rankings? Check out the Sep 2026 Free LLM API Rankings →
About the Free API Aggregator
The models covered in this guide are all served through the APIShare free API aggregator, which gives you one key for the whole catalog.
- Full model catalog: APIShare free API directory
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