Kimi K2.7 Code 5M Tokens Monthly Pricing

Kimi K2.7 Code is no longer available

This model is deprecated. It may still respond for existing integrations, but it is no longer recommended for new work, and the figures below are retained as a historical record.

See current model pricing →

Complete Analysis: 5,001,000 tokens for Kimi K2.7 Code
⚡ 60% Cached

Complete analysis of pricing, performance, and use cases for Moonshot AI's Kimi K2.7 Code model with 60% Cached.

⚡ Caching Optimized (up to 83% savings)
$2.388500 (rounded ~ $2.39) Total Cost
5,001,000 Total Tokens
7 hours, 39 minutes, 33.26 seconds Processing Time
181 Effective Tokens/Sec

Click Recalculate to update after making changes

ℹ️ Bulk Calculation: Total volume exceeds single-request limit of 262,144 tokens. Budgeting mode active.

Select AI Model

Kimi K2.7 Code

Moonshot AIMax Context: 262,144 tokens
$0.95 / $4 per 1M tokens
60%
Provider-specific multipliers applied after all calculations
Enable for cache discounts
Select platform to enforce context limits
Number of requests (max 1M). Summary view auto-enabled >10k.

Calculate Token Costs

$1.900000 Input Cost
$0.004000 Output Cost
$0.000000 Unit Cost
$0.000000 Search Cost
$0.000000 Request Fee
$0.000000 Tool Fee
$0.000000 Code Execution
5,001,000Total Tokens
$0.000478Cost per 1K
2,093,783Tokens per $

📊 Advanced Cost Breakdown

Processing Speed

459m 33s Processing Time
185 Tokens/Second
235ms Time to First Token
181 Effective Speed

Model Comparison

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Model Information

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🔄 Advanced Options

⚡ Optimization

Flat fee per session (e.g., $0.03 for Code Interpreter)
Hourly storage fee for cached data
First 50 hours free, $0.05/hour after

🧠 Reasoning & Thinking

Manual thinking tokens (billed at output rate)

🔧 Special Modes


📚 Research & Citations

Fee per source cited

🎤 Realtime Audio & Video

Session length for billing

Kimi K2.7 Code Moonshot AI

$2.388500 (rounded ~ $2.39)
Total Cost
⚠️ Bulk Calculation: Total volume exceeds single-request limit of 262,144 tokens. Budgeting mode active.
⚡ 60% Cached 📊 Batch API 🔧 Tools
👁️
Vision/Images
✓ Available
🎧
Audio Processing
✗ Not Available
🎥
Video Analysis
✗ Not Available
🔧
Tool Usage
✓ Available
📄
OCR Support
✗ Not Available
📊
Batch API
✗ Not Available
⚡
Caching
✓ Available
83% savings

💰 Total Cost Calculation (from Plugin)

Base Cost (No Optimizations) $4.754000 (rounded ~ $4.75) Input: $4.750000
Output: $0.004000
Optimized Cost $2.388500 (rounded ~ $2.39) Input: $4.750000
Output: $0.004000
Unit: $0.000000
Fees: $0.000000
Total Savings $2.365500 (rounded ~ $2.37) 49.8% discount

Detailed Cost Analysis (from Plugin)

For 5,000,000 input tokens and 1,000 output tokens:

  • Input Cost: $4.750000
  • Output Cost: $0.004000
  • Total Cost: $2.388500 (rounded ~ $2.39)
  • Cost per 1K tokens: $0.000478
  • Tokens per dollar: 2,093,783 tokens
  • Context Window: 262144 tokens

Speed & Performance Analysis

With a processing speed of 185 tokens per second and 235ms time to first token:

  • Processing Time: 7 hours, 39 minutes, 33.26 seconds
  • Latency: 235 milliseconds to first token
  • Base Throughput: 185 tokens/second
  • Effective Throughput: 181 tokens/second (temperature-adjusted)

Best Use Cases

Ideal for terminal-native coding agents and IDE inline-completion tasks where high-frequencylow-latency code generation is required.

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✨ Market Recommendations AI Model Registry

← Back to Kimi K2.7 Code
📋 Active Input Parameters
Input Tokens: 5,000,000
Output Tokens: 1,000
Batch API: Enabled (50% discount)
Cached Tokens: 60%
Tools: Enabled
🔍
No Alternatives Found
No other models in the registry support all your current input parameters. Try adjusting some parameters to see more options.
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✨ How recommendations work (v8.6.0): We scan all active models in the registry and only include those that support ALL your current inputs. For token-based models, we check if they can handle your token counts. For special pricing models (OCR, video, audio), we verify they have the correct pricing structure. Features marked requested were in your inputs but not supported by that model. Now using official provider pricing without reseller markups.

Optimizing Code Generation for Mid-Market Workflows

Kimi K2.7 Code has emerged as a high-efficiency option for engineering teams managing significant token volumes in their IDEs. As a coding-first agentic model, it is built to handle the complex, context-heavy requirements of modern development environments where developers often trigger autocomplete and refactoring tasks across large repositories.

For SaaS teams handling roughly 5M tokens per month, Kimi K2.7 Code offers a balance of reasoning depth and token efficiency. Unlike general-purpose models that often struggle with the noise of multi-file projects, this model is explicitly tuned to maintain focus on the relevant codebase segments, reducing the need for redundant context passing. This makes it particularly effective for inline code suggestions where low latency and high accuracy are paramount for maintaining developer flow.

The model’s agentic capabilities allow it to go beyond simple autocomplete. When integrated into an IDE or terminal-native workflow, it can effectively navigate project structures and assist with routine refactoring tasks. Teams should consider this model if their development workflow requires a reliable balance between reasoning speed and cost management, especially when the engineering team is scaling rapidly and needs an AI partner that can handle high-frequency code completion requests without hitting latency bottlenecks.

Because it is designed with coding-heavy benchmarks in mind, it often feels more responsive in programming environments than larger, more generalized frontier models. For teams already deep in their development phase, this model provides a streamlined path for scaling AI-assisted coding without over-investing in generalized reasoning power that isn’t strictly necessary for daily IDE interactions.

Frequently Asked Questions

How accurate are these AI model cost calculations?
Our calculations are based on official pricing from each provider (Google, OpenAI, Anthropic, Meta, xAI, Perplexity, DeepSeek, Mistral) and are updated regularly. We account for all factors including multimodal inputs, caching discounts, batch API pricing, tool usage multipliers, OCR processing, audio minutes, silence fees, and research mode pricing. Note: Reseller markups and dedicated instance multipliers have been removed to reflect official provider pricing.
How does prompt caching work?
Caching discounts vary by provider: Google and OpenAI offer 90% discounts on cached input tokens. Anthropic uses write (1.25x) and read (0.10x) multipliers. Savings are applied to the token portion only, not unit-based fees.
How do Market Recommendations work (v8.6.0)?
Our recommendation engine scans the entire model registry and only includes models that support ALL your current input parameters (tokens, images, video, audio, OCR, tools, batch API, etc.). It calculates exact costs with your settings and sorts by price, showing you the best value options that can handle your complete workflow. Special pricing models (OCR, video, audio, image generation) are properly handled and only appear when their specific input types are requested. v8.6.0 removes reseller markups (20% buffer) and dedicated instance multipliers to reflect official provider pricing.
What is the YemHub AI Calculator Tool?
The YemHub AI Calculator is the most comprehensive tool for estimating costs and comparing performance metrics across 50+ AI models. It calculates token-based pricing, analyzes multimodal processing, accounts for state-dependent pricing (context cliffs, tiered tunnels), provides optimization recommendations, and now offers intelligent market matching to find the best alternatives for your specific needs.