Kimi K2.5 API Sunset August 31, 2026: Migration Guide

What Is Being Discontinued?

Per Moonshot's official model documentation, the following model strings will return errors after August 31, 2026, 23:59 UTC:

Important: K2.5 open weights on Hugging Face are not affected. This sunset only applies to the Moonshot-hosted API endpoints.

Why Is Moonshot Removing K2.5?

Kimi K3 launched on July 16, 2026, and Moonshot is consolidating its API surface around the new architecture. K3 offers:

• 200K context window (vs 128K max for K2.5)
• Better reasoning and code generation benchmarks
• Improved Chinese text processing
• Unified pricing at $0.50/M input, $2.00/M output

From Moonshot's perspective, maintaining two parallel API families creates support overhead and fragments their developer experience. K3 is the future; K2.5 is legacy.

What to Replace It With

For most use cases, simply swap your model string to kimi-k3. However, Moonshot now offers two specialized variants:

• kimi-k3 — General-purpose model. Best for chat, Q&A, document analysis, and creative writing.
• kimi-for-coding — Code-optimized variant. Better at code completion, debugging, and technical reasoning. Use this if K2.5 was primarily powering your IDE integration or code review pipeline.

Code Migration: Before & After

Python (OpenAI SDK)

# BEFORE (breaks after August 31)
from openai import OpenAI
client = OpenAI(api_key="YOUR_KIMI_KEY", base_url="https://api.moonshot.cn/v1")

response = client.chat.completions.create(
    model="kimi-k2.5",  # ❌ Deprecated
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# AFTER
response = client.chat.completions.create(
    model="kimi-k3",  # ✅ Direct replacement
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# OR, for coding workloads:
response = client.chat.completions.create(
    model="kimi-for-coding",  # ✅ Code-optimized
    messages=[{"role": "user", "content": "Refactor this Python function"}]
)

JavaScript / TypeScript

// BEFORE
const response = await fetch('https://api.moonshot.cn/v1/chat/completions', {
  method: 'POST',
  headers: { 'Authorization': 'Bearer ' + apiKey, 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'kimi-k2.5', // ❌ Deprecated
    messages: [{ role: 'user', content: 'Hello' }]
  })
});

// AFTER
const response = await fetch('https://api.moonshot.cn/v1/chat/completions', {
  method: 'POST',
  headers: { 'Authorization': 'Bearer ' + apiKey, 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'kimi-k3', // ✅ Direct replacement
    messages: [{ role: 'user', content: 'Hello' }]
  })
});

cURL

# BEFORE
curl https://api.moonshot.cn/v1/chat/completions \
  -H "Authorization: Bearer $KIMI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "kimi-k2.5", "messages": [{"role": "user", "content": "Hello"}]}'

# AFTER
curl https://api.moonshot.cn/v1/chat/completions \
  -H "Authorization: Bearer $KIMI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "kimi-k3", "messages": [{"role": "user", "content": "Hello"}]}'

How to Find K2.5 in Your Codebase

Run these commands in your project root to find every reference:

# Search for all deprecated strings
grep -r "kimi-k2.5\|moonshot-v1" --include="*.py" --include="*.js" --include="*.ts" --include="*.json" --include="*.yaml" --include="*.yml" --include="*.env" .

# If you use environment variables for model names
grep -r "KIMI_MODEL\|MOONSHOT_MODEL\|MODEL_NAME" --include="*.env" --include="*.json" .

Don't forget to check:

• CI/CD pipelines that hardcode model strings
• Database records storing model preferences
• Environment variable files (.env, secrets managers)
• Third-party integrations (Zapier, Make, n8n) that call your API

Testing Your Migration

Before the August 31 deadline, run these validation steps:

1. Staging test: Deploy your updated code to a staging environment and run your full test suite.
2. Prompt parity: Send identical prompts to both kimi-k2.5 (before sunset) and kimi-k3 (new). Compare output quality. K3 should be equal or better, but verify edge cases in your domain.
3. Token count check: K3 uses a different tokenizer. Verify that your token budgeting still holds. A prompt that was 2,000 tokens under K2.5 might be 2,200 under K3.
4. Latency baseline: Measure response time for your typical prompts. K3 is generally faster, but confirm for your workload.

Pricing Comparison

Good news: K3 is priced identically to K2.5. Your API bill won't change.

Using TokenEase to Avoid Future Sunsets

Model sunsets are becoming routine. In 2026 alone, we've seen:

• OpenAI Assistants API → sunset August 26
• Kimi K2.5 / moonshot-v1 → sunset August 31
• DeepSeek legacy strings → sunset October 24

Chasing every migration announcement is unsustainable. TokenEase solves this with a provider-agnostic API gateway:

• One API key for DeepSeek, GLM-5.1, Kimi K3, Qwen-Plus, and Doubao Pro
• One endpoint — fully OpenAI-compatible, zero code changes beyond the model string
• Automatic fallback — if one provider sunsets a model, switch to another with a single parameter
• Stable pricing — volume aggregation keeps costs 40% below OpenRouter

# TokenEase: future-proof your AI stack
from openai import OpenAI
client = OpenAI(api_key="tk_...", base_url="https://tokenease.io/v1")

# Today: Kimi K3
response = client.chat.completions.create(model="kimi-k3", messages=[...])

# Tomorrow: if Kimi sunsets, swap to GLM-5.1 in one line
response = client.chat.completions.create(model="glm-5.1", messages=[...])

# No new API keys. No new accounts. No migration sprints.

Questions about your specific migration? The TokenEase API supports both kimi-k3 and kimi-for-coding with the same $0.50/$2.00 pricing. Start testing now and beat the August 31 deadline.