Muse Code vs. Opencode

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

Coding Agent Index

Artificial Analysis Coding Agent Index v1.4 · Higher is better
Not publicly available

Tempo por tarefa

Average agent wall time per task · Lower is better
Not publicly available

Custo por tarefa

Average API cost per task (USD) · Lower is better
Not publicly available

Comparação

Comparação lado a lado de Muse Code e Opencode.

Comparação de agentes de programação

Métrica
Muse Code
Muse Spark 1.3 (max)
Opencode
Gemini 3.8 Flash (high)
Análise
Harness do agente
Muse Code
Opencode
Modelo representativo
Muse Spark 1.3 (max)
Gemini 3.8 Flash (high)
Coding Agent Index
68
61
Muse Code tem um Coding Agent Index mais alto que Opencode
DeepSWE
68%
62%
Muse Code tem uma pontuação mais alta em DeepSWE que Opencode
Terminal-Bench v2.1
84%
84%
Ambos os agentes têm a mesma pontuação em Terminal-Bench v2.1
SWE-Atlas-QnA
52%
38%
Muse Code tem uma pontuação mais alta em SWE-Atlas-QnA que Opencode
Custo por tarefa
$0.00
$2.04
Comparação não disponível
Tempo por tarefa
24.6m
11.9m
Opencode tem um tempo por tarefa mais baixo que Muse Code
Turnos por tarefa
128.5
119.5
Opencode tem menos turnos por tarefa que Muse Code
Uso de tokens por tarefa
14.2M
27.8M
Muse Code tem um uso de tokens por tarefa mais baixo que Opencode
Taxa de acertos de cache
95%
88%
Muse Code tem uma taxa de acertos de cache mais alta que Opencode

Variantes de modelo

Variantes de modelo avaliadas para Muse Code e Opencode.

Variantes de modelo

Muse Code
Muse Spark 1.3 (max)
68
68%
84%
52%
$0.00
24.6m
14.2M
Muse Code
Muse Spark 1.3 (xhigh)
64
67%
82%
44%
$1.72
12.8m
14.8M
Muse Code
Muse Spark 1.2 (xhigh)
62
58%
82%
45%
$2.07
40.8m
20M
Opencode
Gemini 3.8 Flash (high)
61
62%
84%
38%
$2.04
11.9m
27.8M
Opencode
Gemini 3.7 Flash (high)
60
57%
91%
31%
$1.27
8.8m
18.5M
Opencode
Muse Spark 1.2 (xhigh)
59
53%
80%
44%
$1.91
17.7m
16.4M
Opencode
Muse Spark 1.1 (xhigh)
55
54%
77%
33%
$1.44
12.7m
12.3M
Opencode
Opus 4.7 (medium)
51
40%
78%
36%
$2.94
12.5m
7.6M
Opencode
Gemini 3.6 Flash (high)
47
41%
79%
22%
$2.08
10.4m
13M

Desempenho

Desempenho no Artificial Analysis Coding Agent Index.

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
Not publicly available
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.

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
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

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. custo por tarefa

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

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
Not publicly available

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
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.