---
title: "MoonshotAI: Kimi K2.7 Code — price, capability and what beats it · Undominated.ai"
canonical: https://undominated.ai/models/moonshotai__kimi-k2.7-code/
description: "MoonshotAI: Kimi K2.7 Code: $0.670/M in, $3.40/M out. Independent capability scores, context-tier pricing, and the models that are both better and cheaper."
---

# MoonshotAI: Kimi K2.7 Code — price, capability and what beats it · Undominated.ai

> MoonshotAI: Kimi K2.7 Code: $0.670/M in, $3.40/M out. Independent capability scores, context-tier pricing, and the models that are both better and cheaper.

[Leaderboard](/) / Moonshot AI

# Kimi K2.7 Code

Moonshot AI · released 2026-06-12 · modified-mit

 $1.35 per million tokens, balanced Balanced Summarise Chat Code gen Agentic

## MiniMax M3 is both better and cheaper.

It scores **+2.4** higher and costs **61% less** ($0.525/M against $1.35/M) on this workload — and it does everything this model does.

[DeepSeek V4 Flash 0731](/models/deepseek__deepseek-v4-flash-0731) is cheaper still (92% less) but drops no image input.

## Our take

 editorial — not a measurement

A coding-specialised open-weight model at $0.67/$3.40 with a 262k window, and the sharpest price contrast in the dataset: it is roughly a quarter the price of GPT-5.3-Codex ($1.75/$14) on output, the dimension that dominates codegen cost. If you are paying for a coding model and have not benchmarked this one against your actual repo tasks, you are probably overspending. Open weights mean you can also self-host it behind your firewall, which matters for proprietary codebases.

### Strengths

 - $0.67/$3.40 — ~4x cheaper output than GPT-5.3-Codex
- 262k context and 262k max output
- Open weights — self-hostable for proprietary code
- Cache reads at $0.17

### Weaknesses

 - No independent benchmark score captured in this pass
- Licence not independently verified
- Smaller context than the 1M-token generalists

### Reach for it when

 - High-volume code generation
- On-prem coding assistants over proprietary repos
- Cost-reduction target for teams on Codex

### Avoid it if

 - You need verified benchmark evidence before adopting
- Your tasks need >262k of repo context

Sources: openrouter.ai

### Every price dimension

| Input | $0.670 /M |
| --- | --- |
| Output | $3.40 /M |
| Cached input | $0.170 /M |

### Independent scores

| Intelligence | 43 |
| --- | --- |
| Coding | 60.8 |
| Agentic | 30.3 |

Sources are listed separately rather than averaged. Across the 62 models both have scored they correlate at r = 0.806 — close agreement overall, but four models rank very differently between them, and a blended score would hide exactly those.

#### Best rankings by task

 - htmlslides #7 of 34 1215
- godotgamedev #12 of 43 1183
- asciiart #14 of 83 1243
- python-pptxslides #17 of 35 1168
- agenticgamedev #17 of 34 1138
- website #18 of 151 1292
- fullstack #19 of 59 1208
- webapps #21 of 56 1212

### Capability

| Context window | 262K |
| --- | --- |
| Max output | 262K |
| Input modes | text, image |
| Tool use | yes |
| Reasoning | always on |
| Open weights | yes |

### Provenance

| Price source | openrouter.ai |
| --- | --- |
| Fetched | 2026-08-24 |
| Quality data | verified |
| Cross-checked | vendor page |

MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...