Kimi K3 API on KeyoAPI — 256K Context, Pricing & Benchmarks

Kimi K3 by Moonshot AI delivers 256K context window reasoning and coding performance on an OpenAI-compatible endpoint at ~$0.41 / $2.04 per 1M tokens.

kimi-k3 is a llm model on KeyoAPI, a prepaid AI API gateway: call it at POST /v1/chat/completions, billed at ~$0.41 / $2.04 per 1M in/out from the same balance that covers chat, vision, OCR and video models.

Listed price: ~$0.41 / $2.04 per 1M in/out · Live catalog: Model Square · Static list: pricing list.

Overview

Looking for an OpenAI-compatible Kimi K3 API key with production reliability and an accessible prototyping route? On KeyoAPI, kimi-k3 provides metered access to Moonshot AI’s frontier 256K context reasoning model at indicative sell rates of approximately $0.41 per 1M input tokens and $2.04 per 1M output tokens (verify real-time rates on the full pricing list).

Unlike consumer chat applications with interactive browser interfaces, KeyoAPI provides a raw developer relay endpoint at https://www.keyoapi.xyz/v1/chat/completions using standard Bearer token authentication. This enables enterprise teams, autonomous agents, and SaaS developers to drop Kimi K3 directly into existing OpenAI SDK codebases simply by updating baseURL and model.

With a 256,000-token context window, Kimi K3 is specifically designed for high-density information retrieval, full repository code reviews, complex multi-agent scratchpads, and iterative document extraction. If you need cheaper triage or high-volume routing on the same account wallet, you can seamlessly branch calls to deepseek-v4.1-flash or glm-5.3 without generating additional credentials or reconfiguring client libraries.

Live Gateway Performance & Telemetry (Field Tested)

The metrics above reflect real gateway telemetry measured on 2026-10-19 via automated streaming probes against https://www.keyoapi.xyz/v1/chat/completions using model kimi-k3. Rather than relying on synthetic vendor marketing figures, KeyoAPI captures real-world network and inference behavior directly from active relay channels.

Key observations from our live sampling: Time to First Token (TTFT) has a median of 9,779 ms (p50) and a 95th percentile of 19,962 ms (p95). Because Kimi K3 engages in thorough internal reasoning and context ingestion across its 256K window before emitting its first chunk, interactive applications should always enable streaming mode and display an active generation state to prevent user drop-off.

Under heavy concurrent upstream load, a 15-request spot check saw intermittent upstream timeouts — fifteen probes are not a statistically meaningful uptime metric, so this page publishes latency figures only. In production, implement exponential backoff retries (minimum 2s initial delay) or fallback routing to deepseek-v4.1-flash or glm-5.3 for latency-critical paths.

Standardized AI Benchmarks & Capability Scores

Moonshot AI designed Kimi K3 as a frontier reasoning model with an emphasis on autonomous code synthesis, complex mathematics, and multi-turn problem resolution. Independent benchmarking institutions have rigorously evaluated Kimi K3 across standardized evaluation suites.

The 79.0% score on SWE-bench Verified places Kimi K3 among elite models globally for developer tooling and autonomous programming agents. Combined with a 91.6% score on LiveCodeBench, the model proves adept at parsing large-scale AST trees, understanding legacy repository conventions, and writing bug-free unit test suites across multi-file pull requests.

Multi-Model Cost & Architecture Comparison

KeyoAPI provides unified billing and a consistent OpenAI-compatible endpoint across all major model families. The table below compares kimi-k3 with top alternative reasoning and chat models available in our catalog to help engineering teams choose the right tier for each stage of their pipelines.

Notice that kimi-k3 provides 256K tokens of context — larger than claude-sonnet-5 (200K) or gpt-6-luna (128K). Against Anthropic’s official list price ($3.00 / $15.00) the 1,000-conversation workload runs about 7.4× cheaper on kimi-k3; against Keyo’s own claude-sonnet-5 (~$0.44 / $2.21) the gap narrows to single digits, and deepseek-v4.1-flash-1017 (~$0.02 / $0.06) undercuts both. Kimi’s case is the 256K window, not the unit price.

Production Workload Cost Simulation (1,000 Support Conversations)

To ground token prices in actual budgeting numbers, consider an automated tier-2 customer support workload consisting of 1,000 multi-turn customer conversations.

In a typical enterprise support session, the conversation history, retrieved internal documentation, and ticket context consume approximately 1,800 input tokens. The model's synthesized diagnosis and actionable step-by-step resolution require approximately 400 output tokens.

For a batch of 1,000 conversations, total token consumption equals: 1,000 × 1,800 = 1,800,000 input tokens (1.8M), and 1,000 × 400 = 400,000 output tokens (0.4M).

Applying KeyoAPI’s kimi-k3 sell rates: Input cost = 1.8M × $0.41 / 1M = $0.738. Output cost = 0.4M × $2.04 / 1M = $0.816. Total cost for 1,000 resolved tickets ≈ $1.55.

That is about $0.00155 per customer interaction. For comparison, the same batch at Anthropic’s official list price for claude-sonnet-5 ($3.00 / $15.00) would cost $11.40 (~7.4× more), while Keyo’s own claude-sonnet-5 (~$0.44 / $2.21) lands at $1.68 — within ~8% of kimi-k3. Choose kimi-k3 for the 256K context window, not for the cheapest per-token rate.

• Listed price: ~$0.41 / $2.04 per 1M in/out
• Billing base: $1 of wallet balance = 500,000 quota units
• Gift credit: new users get $10, invites add $3 (spend-first on :free IDs)
• Endpoint: POST /v1/chat/completions with Bearer auth on https://www.keyoapi.xyz/v1

How to call it

curl https://www.keyoapi.xyz/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "system", "content": "You are an expert programming assistant."},
      {"role": "user", "content": "Explain how to handle backpressure in streaming LLM pipelines."}
    ],
    "stream": true,
    "max_tokens": 1024
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("KEYO_API_KEY", "YOUR_API_KEY"),
    base_url="https://www.keyoapi.xyz/v1",
)

response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {"role": "system", "content": "You are a code analysis agent."},
        {"role": "user", "content": "Analyze repository architecture and identify bottlenecks."},
    ],
    temperature=0.6,
    max_tokens=1500,
    stream=True,
)

for chunk in response:
    content = chunk.choices[0].delta.content or ""
    print(content, end="", flush=True)

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.KEYO_API_KEY || "YOUR_API_KEY",
  baseURL: "https://www.keyoapi.xyz/v1",
});

async function main() {
  const stream = await openai.chat.completions.create({
    model: "kimi-k3",
    messages: [
      { role: "system", content: "You are an automated support classifier." },
      { role: "user", content: "Categorize ticket #8492 and draft an initial resolution response." },
    ],
    stream: true,
  });

  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || "");
  }
}

main().catch(console.error);

FAQ

Related models

Guide for kimi-k3. Catalog: the pricing page · Rates: the full pricing list.