Muse Code vs. Opencode

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

Comparação entre Muse Code e Opencode no Artificial Analysis Coding Agent Index, incluindo pontuações de benchmarks, custo, tempo de execução e uso de tokens.

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Destaques

Updated

Coding Agent Index

Artificial Analysis Coding Agent Index v1.5 · Higher is better

Tempo por tarefa

Average agent wall time per task · Lower is better

Custo por tarefa

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

Comparação

Comparação lado a lado de Muse Code e Opencode.

Comparação de agentes de programação

Métrica	Muse Code Muse Spark 1.3 (max)	Opencode GLM-5.3	Análise
Harness do agente	Muse Code	Opencode
Modelo representativo	Muse Spark 1.3 (max)	GLM-5.3
Coding Agent Index	54	54	Muse Code tem um Coding Agent Index mais alto que Opencode
DeepSWE v1.1	72%	61%	Muse Code tem uma pontuação mais alta em DeepSWE v1.1 que Opencode
Terminal-Bench 4.0	32%	40%	Opencode tem uma pontuação mais alta em Terminal-Bench 4.0 que Muse Code
SWE-Atlas-QnA	59%	59%	Ambos os agentes têm a mesma pontuação em SWE-Atlas-QnA
Custo por tarefa	$3.98	$4.24	Muse Code tem um custo por tarefa mais baixo que Opencode
Tempo por tarefa	18.4m	48.1m	Muse Code tem um tempo por tarefa mais baixo que Opencode
Turnos por tarefa	185.2	107	Opencode tem menos turnos por tarefa que Muse Code
Uso de tokens por tarefa	16.7M	14.3M	Opencode tem um uso de tokens por tarefa mais baixo que Muse Code
Taxa de acertos de cache	97%	97%	Opencode tem uma taxa de acertos de cache mais alta que Muse Code

Variantes de modelo

Variantes de modelo avaliadas para Muse Code e Opencode.

Variantes de modelo


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%	$3.47	18.3m	16.2M
Opencode	GLM-5.3	54	61%	40%	59%	$4.24	48.1m	14.3M

Desempenho

Desempenho no Artificial Analysis Coding Agent Index.

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. custo por tarefa

Artificial Analysis Coding Agent Index vs. média do custo da API de pagamento por token por tarefa (USD)

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Most attractive quadrant

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.

Uso de tokens

Consumo de tokens no Artificial Analysis Coding Agent Index.

Uso de tokens por tarefa

Média de tokens de entrada, cache e saída por tarefa

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. tokens totais

Artificial Analysis Coding Agent Index vs. média de tokens totais por tarefa

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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.

Custo

Custo da API de pagamento por token no Artificial Analysis Coding Agent Index, com base nos preços atuais por token.

Custo por tarefa

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.

Tempo de execução

Tempo de execução ativo do agente no Artificial Analysis Coding Agent Index.

Tempo por tarefa

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. tempo de execução

Artificial Analysis Coding Agent Index vs. média do tempo de execução do agente por tarefa

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.

Muse Code vs. Opencode

Coding Agent Index

Tempo por tarefa

Custo por tarefa

Comparação

Comparação de agentes de programação

Variantes de modelo

Variantes de modelo

Desempenho

Artificial Analysis Coding Agent Index

What This Metric Means

Artificial Analysis Coding Agent Index vs. custo por tarefa

How to Read This Chart

Uso de tokens

Uso de tokens por tarefa

Input Tokens

Artificial Analysis Coding Agent Index vs. tokens totais

How to Read This Chart

Custo

Custo por tarefa

What Cost Is Measuring

Tempo de execução

Tempo por tarefa

What Execution Time Is Measuring

Artificial Analysis Coding Agent Index vs. tempo de execução

How to Read This Chart