Devin Fusion CLI vs. Muse Code

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 Devin Fusion CLI 및 Muse Code를 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

Devin Fusion CLI 및 Muse Code의 나란히 비교.

코딩 에이전트 비교

지표	Devin Fusion CLI Claude Fable 5.1 XHigh + SWE-2 Medium	Muse Code Muse Spark 1.3 (max)	분석
에이전트 하네스	Devin Fusion CLI	Muse Code
대표 모델	Claude Fable 5.1 XHigh + SWE-2 Medium	Muse Spark 1.3 (max)
Coding Agent Index	62	54	Devin Fusion CLI의 Coding Agent Index가 Muse Code보다 높습니다
DeepSWE v1.1	63%	72%	Muse Code의 DeepSWE v1.1 점수가 Devin Fusion CLI보다 높습니다
Terminal-Bench 4.0	56%	32%	Devin Fusion CLI의 Terminal-Bench 4.0 점수가 Muse Code보다 높습니다
SWE-Atlas-QnA	66%	59%	Devin Fusion CLI의 SWE-Atlas-QnA 점수가 Muse Code보다 높습니다
작업당 비용	$7.90	$3.98	Muse Code의 작업당 비용이 Devin Fusion CLI보다 낮습니다
작업당 시간	35.8m	18.4m	Muse Code의 작업당 시간이 Devin Fusion CLI보다 짧습니다
작업당 턴	99.5	185.2	Devin Fusion CLI의 작업당 턴이 Muse Code보다 적습니다
작업당 토큰 사용량	9.7M	16.7M	Devin Fusion CLI의 작업당 토큰 사용량이 Muse Code보다 적습니다
캐시 적중률	96%	97%	Muse Code의 캐시 적중률이 Devin Fusion CLI보다 높습니다

모델 변형

Devin Fusion CLI 및 Muse Code의 평가된 모델 변형.

모델 변형


Devin Fusion CLI	Claude Fable 5.1 XHigh + SWE-2 Medium	62	63%	56%	66%	$7.90	35.8m	9.7M
Devin Fusion CLI	GPT-6 Astra XHigh + SWE-2 Medium	59	67%	50%	59%	$4.54	24.7m	6.1M
Muse Code	Muse Spark 1.3 (max)	54	72%	32%	59%	$3.98	18.4m	16.7M
Muse Code	Muse Spark 1.3 (xhigh)	48	73%	17%	55%	$3.45	18.3m	16.2M

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

Color by

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.

Devin Fusion CLI vs. Muse Code

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