Claude Code vs. Kimi Code CLI

Artificial Analysis Coding Agent Index의 벤치마크 점수, 비용, 실행 시간, 토큰 사용량을 기준으로 Claude Code 및 Kimi Code CLI를 비교합니다.

방법론에 관한 자세한 내용은 방법론 페이지를 참조하세요.

다른 비교 살펴보기
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주요 내용

Coding Agent Index

Artificial Analysis Coding Agent Index v1.4 · Higher is better

작업당 시간

Average agent wall time per task · Lower is better

작업당 비용

Average API cost per task (USD) · Lower is better

비교

Claude Code 및 Kimi Code CLI의 나란히 비교.

코딩 에이전트 비교

지표
Claude Code
Opus 5 (xhigh)
Kimi Code CLI
Kimi K3
분석
에이전트 하네스
Claude Code
Kimi Code CLI
대표 모델
Opus 5 (xhigh)
Kimi K3
Coding Agent Index
68
63
Claude Code의 Coding Agent Index가 Kimi Code CLI보다 높습니다
DeepSWE
60%
64%
Kimi Code CLI의 DeepSWE 점수가 Claude Code보다 높습니다
Terminal-Bench v2.1
89%
88%
Claude Code의 Terminal-Bench v2.1 점수가 Kimi Code CLI보다 높습니다
SWE-Atlas-QnA
55%
37%
Claude Code의 SWE-Atlas-QnA 점수가 Kimi Code CLI보다 높습니다
작업당 비용
$8.17
$3.08
Kimi Code CLI의 작업당 비용이 Claude Code보다 낮습니다
작업당 시간
23.7m
24.1m
Claude Code의 작업당 시간이 Kimi Code CLI보다 짧습니다
작업당 턴
152.1
122.9
Kimi Code CLI의 작업당 턴이 Claude Code보다 적습니다
작업당 토큰 사용량
21.6M
10.4M
Kimi Code CLI의 작업당 토큰 사용량이 Claude Code보다 적습니다
캐시 적중률
97%
95%
Claude Code의 캐시 적중률이 Kimi Code CLI보다 높습니다

모델 변형

Claude Code 및 Kimi Code CLI의 평가된 모델 변형.

모델 변형

Claude Code
Opus 5 (xhigh)
68
60%
89%
55%
$8.17
23.7m
21.6M
Claude Code
Fable 5 (max) (with fallback)
67
66%
87%
49%
$11.69
23.5m
13.9M
Claude Code
Opus 5 (max)
67
63%
89%
49%
$8.94
24.2m
23.7M
Claude Code
Opus 5 (high)
66
61%
87%
49%
$3.92
14.0m
9.9M
Claude Code
Opus 5 (medium)
64
63%
85%
44%
$3.17
12.2m
8M
Claude Code
Opus 4.8 (max)
62
56%
84%
47%
$7.72
23.1m
17.9M
Claude Code
Qwen3.8 Max
61
52%
84%
48%
$3.23
29.9m
12.5M
Claude Code
Opus 5 (low)
59
57%
82%
39%
$2.30
10.0m
5.3M
Claude Code
Opus 4.8 (xhigh)
59
51%
83%
43%
$5.67
17.8m
13.6M
Claude Code
Opus 4.8 (high)
58
52%
82%
39%
$3.78
12.7m
9.2M
Claude Code
Opus 4.8 (medium)
56
49%
82%
36%
$3.30
12.0m
7.8M
Claude Code
Opus 4.7 (max)
52
40%
78%
37%
$5.92
15.8m
16.1M
Claude Code
Opus 4.8 (low)
49
41%
78%
28%
$2.18
8.6m
5.2M
Claude Code
GLM-5.2
43
29%
72%
29%
$6.66
25.1m
6.6M
Claude Code
Opus 4.7 (medium)
42
27%
77%
23%
$1.80
6.7m
4.6M
Claude Code
Sonnet 4.6 (medium)
39
29%
67%
20%
$2.04
13.8m
8.4M
Claude Code
Qwen3.7 Plus (thinking)
38
19%
72%
24%
$6.30
10.7m
8.8M
Claude Code
GLM-5.1
37
19%
67%
25%
$4.28
19.0m
25.8M
Claude Code
Kimi K2.6
34
17%
69%
16%
$1.22
40.7m
11.7M
Claude Code
DeepSeek V4 Pro (high)
33
9%
70%
20%
$0.25
17.9m
9.9M
Kimi Code CLI
Kimi K3
63
64%
88%
37%
$3.08
24.1m
10.4M

성능

Artificial Analysis Coding Agent Index 전반의 성능.

Artificial Analysis Coding Agent Index

Artificial Analysis Coding Agent Index v1.4 incorporates 3 benchmarks: DeepSWE, Terminal-Bench v2.1, and SWE-Atlas-QnA · Higher is better
Since benchmarking, we have observed a higher rate of content safety filtering on this endpoint.

The Artificial Analysis Coding Agent Index is a composite score built from DeepSWE, Terminal-Bench v2.1, and SWE-Atlas-QnA.

It is useful for quick comparison, but it should be read alongside the per-eval breakdowns. Two agents with similar index values can still have different strengths across repository tasks, terminal workflows, and rubric-based evaluations.

토큰 사용량

Artificial Analysis Coding Agent Index 전반의 토큰 소비량.

작업당 토큰 사용량

작업당 평균 입력·캐시·출력 토큰
Prompt cache hit rates can vary significantly by provider routing, which can materially change effective cost.

Non-cached input tokens sent to the model, including prompts, instructions, tool context, and task context that were not served from prompt cache.

Artificial Analysis Coding Agent Index vs. 총 토큰

Artificial Analysis Coding Agent Index vs. 작업당 평균 총 토큰
Most attractive quadrant

Each point represents a coding-agent variant. Farther left means lower average total token usage per task, while higher on the chart means higher benchmark performance. Agents toward the upper-left achieve stronger results with fewer tokens.

비용

현재 토큰당 가격을 기준으로 한 Artificial Analysis Coding Agent Index의 토큰당 종량제 API 비용.

작업당 비용

Average pay-per-token API cost per task (USD) · Lower is better

This chart shows the average pay-per-token API cost per task across the Artificial Analysis Coding Agent Index, spanning DeepSWE, Terminal-Bench v2.1, and SWE-Atlas-QnA.

Where applicable, that cost model includes standard input pricing, discounted cached-input pricing, separate cache-write charges, and output pricing rather than treating all prompt tokens as if they were billed at the same uncached input rate.

It is intended to show pay-per-token API cost, not consumer plan pricing or the full operational cost of deploying the system in production. Infrastructure, engineering, and supervision costs are not the focus of this metric.

Artificial Analysis Coding Agent Index vs. 작업당 비용

Artificial Analysis Coding Agent Index vs. 작업당 평균 토큰당 종량제 API 비용 (USD)
Most attractive quadrant
Pareto line

Each point represents a coding-agent variant. Farther left means lower average cost per task, while higher on the chart means higher benchmark performance. The most efficient agents sit toward the upper-left: stronger results at lower cost.

실행 시간

Artificial Analysis Coding Agent Index 전반의 활성 에이전트 실행 시간.

작업당 시간

Average agent wall time per task · Lower is better

This chart uses agent wall time: how long the agent process was actively running on each task.

It does not include environment startup, verifier or judge time, or other harness overhead, so it is a cleaner comparison of how long the agent itself was working.

Artificial Analysis Coding Agent Index vs. 실행 시간

Artificial Analysis Coding Agent Index vs. 작업당 평균 에이전트 실행 시간
Most attractive quadrant

Each point represents a coding-agent variant. Farther left means shorter average agent runtime per task, while higher on the chart means higher benchmark performance. Agents toward the upper-left deliver stronger results in less active agent time.