Claude Code vs. Grok Build

Name: Coding Agent Index
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

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

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주요 내용

Updated

Coding Agent Index

Artificial Analysis Coding Agent Index v1.5 · 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 Grok Build의 나란히 비교.

코딩 에이전트 비교

지표	Claude Code Fable 5.1 (max) (with fallback)	Grok Build Grok 4.6 (xhigh)	분석
에이전트 하네스	Claude Code	Grok Build
대표 모델	Fable 5.1 (max) (with fallback)	Grok 4.6 (xhigh)
Coding Agent Index	62	47	Claude Code의 Coding Agent Index가 Grok Build보다 높습니다
DeepSWE v1.1	64%	65%	Grok Build의 DeepSWE v1.1 점수가 Claude Code보다 높습니다
Terminal-Bench 4.0	58%	18%	Claude Code의 Terminal-Bench 4.0 점수가 Grok Build보다 높습니다
SWE-Atlas-QnA	65%	58%	Claude Code의 SWE-Atlas-QnA 점수가 Grok Build보다 높습니다
작업당 비용	$12.39	$3.57	Grok Build의 작업당 비용이 Claude Code보다 낮습니다
작업당 시간	34.8m	19.5m	Grok Build의 작업당 시간이 Claude Code보다 짧습니다
작업당 턴	36.7	112.1	Claude Code의 작업당 턴이 Grok Build보다 적습니다
작업당 토큰 사용량	5.7M	5.5M	Grok Build의 작업당 토큰 사용량이 Claude Code보다 적습니다
캐시 적중률	92%	91%	Claude Code의 캐시 적중률이 Grok Build보다 높습니다

모델 변형

Claude Code 및 Grok Build의 평가된 모델 변형.

모델 변형


Claude Code	Fable 5.1 (max) (with fallback)	62	64%	58%	65%	$12.39	34.8m	5.7M
Claude Code	Opus 5 (max)	60	63%	55%	62%	$10.79	41.9m	11.4M
Claude Code	Qwen3.8 Max	43	51%	17%	62%	$3.49	1.1h	8.7M
Grok Build	Grok 4.6 (xhigh)	47	65%	18%	58%	$3.57	19.5m	5.5M

성능

Artificial Analysis Coding Agent Index 전반의 성능.

Artificial Analysis Coding Agent Index

Artificial Analysis Coding Agent Index v1.5 incorporates 3 benchmarks: DeepSWE v1.1, Terminal-Bench 4.0, and SWE-Atlas-QnA · Higher is better

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The Artificial Analysis Coding Agent Index is a composite score built from DeepSWE v1.1, Terminal-Bench 4.0, 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.

Artificial Analysis Coding Agent Index vs. 작업당 비용

Artificial Analysis Coding Agent Index vs. 작업당 평균 토큰당 종량제 API 비용 (USD)

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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.

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

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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

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This chart shows the average pay-per-token API cost per task across the Artificial Analysis Coding Agent Index, spanning DeepSWE v1.1, Terminal-Bench 4.0, 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.

실행 시간

Artificial Analysis Coding Agent Index 전반의 활성 에이전트 실행 시간.

작업당 시간

Average agent wall time per task · Lower is better

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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

Color by

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.

Claude Code vs. Grok Build

Coding Agent Index

작업당 시간

작업당 비용

비교

코딩 에이전트 비교

모델 변형

모델 변형

성능

Artificial Analysis Coding Agent Index

What This Metric Means

Artificial Analysis Coding Agent Index vs. 작업당 비용

How to Read This Chart

토큰 사용량

작업당 토큰 사용량

Input Tokens

Artificial Analysis Coding Agent Index vs. 총 토큰

How to Read This Chart

비용

작업당 비용

What Cost Is Measuring

실행 시간

작업당 시간

What Execution Time Is Measuring

Artificial Analysis Coding Agent Index vs. 실행 시간

How to Read This Chart