Moonshot (Kimi) · Mid-tier · 256K context window
$0.006050 per call at 3,000 input / 800 output tokens.
Per call
$0.006050
10K calls/mo
$60.5
Cache savings
83% off input
Batch savings
40% off all
Adjust token counts, volume, and discount toggles to model your json extraction spend. Prices verified daily against provider pricing pages.
Call Kimi K2 with the OpenAI Python SDK pointed at api.moonshot.cn:
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.moonshot.cn/v1",
api_key=os.environ["MOONSHOT_API_KEY"],
)
response = client.chat.completions.create(
model="kimi-k2",
max_tokens=800,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.006050 per call at standard rates
# Input: $0.95/MTok
# Output: $4.00/MTokPer-call cost for all models scoring 50+ on json extraction benchmarks. Hover for details.
Cheapest viable option: Gemini 3 Flash at $0.003900/call (36% less, 86/100 quality).
Structured JSON output is compact and predictable, so cost is set almost entirely by how much source text you send. The schema itself is identical on every call and belongs in the cached prefix. This is also a task where a smaller model often matches a larger one: the output space is constrained by the schema, which limits how wrong a cheaper model can go.
For Kimi K2 specifically, that means output tokens cost 4.2x more than input, at $4.00 vs $0.95 per MTok. At this use case’s 3,000/800 token split, output accounts for 53% of the $0.006050 per-call cost.
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $0.95 | 3,000 |
| Output | $4.00 | 800 |
Caching drops input cost from $0.95 to $0.16/MTok. For repeated system prompts, that saves $0.002371 per call.
Submit requests asynchronously and receive 40% off all token costs. This works best for offline pipelines where latency requirements are flexible.
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.006050 | $60.5 | $36.79 | $23.71 |
| 50,000 calls | $0.006050 | $302.5 | $183.94 | $118.56 |
| 100,000 calls | $0.006050 | $605 | $367.88 | $237.12 |
| 500,000 calls | $0.006050 | $3,025 | $1,839.4 | $1,185.6 |
| 1,000,000 calls | $0.006050 | $6,050 | $3,678.8 | $2,371.2 |
Benchmark data for json extraction is pending for Kimi K2. Test against your own evaluation suite before moving to production.
Benchmark data is not yet available for Kimi K2. We recommend testing against your own evaluation criteria.
2 models score 80+ on json extraction benchmarks, sorted by input rate. Cheapest: Gemini 3 Flash (36% less, 86/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Kimi |
|---|---|---|---|---|---|---|
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 86/100 | $0.003900 | 36% cheaper |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 88/100 | $0.007000 | 16% more |
Provider
Moonshot (Kimi)
Tier
Mid-tier
Context
256K tokens
Released
Not published
Prices from platform.kimi.ai (K2.6, read 2026-10-11). Cache hit: $0.16/MTok input. Batch API at 60% of standard rate. Not yet benchmarked, so never recommended.
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| Kimi K3 | Flagship | $3.00 | $15.00 | 1M |
You can compare Kimi K2 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.
See how Kimi K2 costs change across different workload types.
Code Generation
$0.0179/call
Unit Test Generation
$0.0229/call
SQL Generation
$0.005425/call
API Generation
$0.0268/call
Code Refactoring
$0.0198/call
Code Review
$0.0128/call
PR Review
$0.0196/call
Security Review
$0.0157/call
Bug Detection
$0.0128/call
Document Summarization
$0.0135/call
PDF Summarization
$0.0202/call
Meeting Notes
$0.0156/call
Email Summarization
$0.004850/call
Chatbot
$0.005900/call
RAG Pipeline
$0.0117/call
Customer Support Bot
$0.006850/call
FAQ Bot
$0.004625/call
Data Extraction
$0.007800/call
Invoice Parsing
$0.004300/call
Resume Parsing
$0.005575/call
Chain of Thought
$0.0229/call
Legal Analysis
$0.0255/call
Math Reasoning
$0.0134/call
Financial Analysis
$0.0236/call
Content Moderation
$0.000875/call
Sentiment Analysis
$0.000875/call
Categorization
$0.001360/call
Intent Detection
$0.000685/call
Copywriting
$0.008475/call
Blog Writing
$0.0330/call
Marketing Copy
$0.0128/call
Product Descriptions
$0.006475/call
A typical json extraction call using Kimi K2 costs about $0.006050 per call. This estimate uses 3,000 input tokens and 800 output tokens at standard rates. Standard rates are $0.95 per million input tokens and $4.00 per million output tokens.
Our benchmark data does not yet include a json extraction ranking for Kimi K2. We recommend testing it against your own evaluation criteria before production usage.
The most affordable model for json extraction above 80/100 quality is Gemini 3 Flash. It comes from Gemini at $0.50 per million input tokens. Output costs $3.00 per million tokens with a quality score of 86/100. Switching from Kimi K2 to Gemini 3 Flash saves about 36% per API call.
Three strategies lower Kimi K2 costs for this task type effectively. Prompt caching gives a 83% discount on input tokens when reusing system prompts. The batch API gives 40% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.006050 to about $0.000610 per call.
Moonshot (Kimi) has 1 other models alongside Kimi K2 today: Kimi K3 at $3.00/$15.00 per million tokens (flagship). The right choice depends on whether your json extraction workload prioritizes quality, cost, or speed.
Kimi K2 supports a context window of 256K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For json extraction, typical calls use 3,000 input and 800 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
This calculator estimates costs based on published rates only. Connect your API usage to see where your money goes and which models drive your spend.