Cut your Kimi K2 API costs by 40–85% using token masking, prompt compression, automatic cache exploitation, and smart output truncation — all in one drop-in Python class.
Most developers send raw, unoptimized prompts to the Kimi API and pay full price every single call. This toolkit applies 4 optimization layers before and during every API call to dramatically reduce token usage and cost — with zero change to output quality.
| Layer | Technique | Saving |
|---|---|---|
| 1 | Prompt Compression (alias substitution) | ~20% input tokens |
| 2 | Logit Bias / Token Masking | ~25% output tokens |
| 3 | Automatic Cache Exploitation | 75% off cached input |
| 4 | Stop Sequences + max_tokens cap |
~25% output tokens |
Result: ~40% average cost reduction per call.
Based on Kimi K2 API pricing ($0.60/M input · $0.15/M cached · $2.50/M output):
| Monthly Volume | ❌ Unoptimized | ✅ Optimized | 💰 Monthly Saving |
|---|---|---|---|
| 10K calls | $19.00 | $11.46 | $7.54 |
| 100K calls | $190.00 | $114.60 | $75.40 |
| 500K calls | $950.00 | $573.00 | $377.00 |
| 1M calls | $1,900.00 | $1,146.00 | $754.00 / mo |
pip install openai tiktoken
export KIMI_API_KEY="sk-your-key-here"
python kimi_optimizer.pyfrom kimi_optimizer import KimiTokenOptimizer
optimizer = KimiTokenOptimizer(api_key="your-key")
result = optimizer.chat(
user_message = "Explain tokenization cost impact in LLMs.",
system_prompt = "You are a senior ML engineer. Reply in JSON only.",
max_tokens = 300,
verbose = True # prints live cost estimate before each call
)
print(result["response"])
print(f"Actual cost: ${result['actual_cost_usd']}")
print(f"Tokens saved: {result['tokens_saved']}")
optimizer.print_session_stats()Sample output:
┌─ PRE-CALL ESTIMATE ──────────────────────────────
│ Input tokens : 847
│ Tokens compressed: 62 saved via aliases
│ Estimated cost : $0.000412
│ Baseline (no opt): $0.000693
│ Est. savings : 40.5%
└──────────────────────────────────────────────────
Before sending any text to the API, long repeated phrases are replaced with short aliases client-side. The model never sees the verbose form.
"large language model" → "LLM" (4 tokens → 1 token, -75%)
"natural language processing" → "NLP" (3 tokens → 1 token, -67%)
"application programming interface" → "API" (4 tokens → 1 token, -75%)
Add your own domain-specific phrases to the compression dictionary.
Sets specific token IDs to -100 in the API's logit_bias parameter — a hard ban that prevents filler tokens like "Certainly!", "Of course!", "Sure!" from ever appearing in the output. These tokens are pure cost with zero informational value.
Kimi K2 automatically caches repeated input prefixes at $0.15/M instead of $0.60/M (75% cheaper). The optimizer structures your prompts so the long static system prompt is always first — maximizing cache hits on every repeated call.
Output tokens cost 4× more than input tokens. Terminating generation early with stop sequences (###, }, etc.) and a hard max_tokens cap is the highest-ROI single change you can make.
├── kimi_optimizer.py # Main optimizer class (Python)
├── kimi_token_optimizer.html # Interactive research report & documentation
└── README.md
Customize the compression dictionary and logit bias in __init__:
self.compressions = {
"your long repeated phrase": "SHORT",
# add as many as needed for your domain
}
self.logit_bias = self._build_logit_bias(
ban_words = ["Certainly", "Absolutely", "Sure"],
boost_words = ["JSON", "error"] # tokens you want more often
)- Python 3.10+
openai>= 1.0tiktoken- Kimi K2 API key from platform.moonshot.ai
MIT — free to use, modify, and deploy in production.
PRs welcome! Especially interested in:
- Additional compression dictionaries for specific domains (medical, legal, finance)
- Async batch call support
- Token usage dashboard / visualization
Pricing figures based on Kimi K2 API rates as of February 2026. Actual savings vary by workload and cache hit rate.