Codex vs. Muse Code

Vergleich von Codex und Muse Code im Artificial Analysis Coding Agent Index, einschließlich Benchmark-Werten, Kosten, Ausführungszeit und Tokenverbrauch.

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Wichtigste Ergebnisse

Coding Agent Index

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

Zeit pro Aufgabe

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

Kosten pro Aufgabe

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

Vergleich

Direkter Vergleich von Codex und Muse Code.

Programmieragenten-Vergleich

Metrik
Codex
GPT-5.6 Sol (max)
Muse Code
Muse Spark 1.3 (max)
Analyse
Agent-Harness
Codex
Muse Code
Repräsentatives Modell
GPT-5.6 Sol (max)
Muse Spark 1.3 (max)
Coding Agent Index
65
68
Muse Code hat einen höheren Coding Agent Index als Codex
DeepSWE
69%
68%
Codex hat einen höheren DeepSWE-Wert als Muse Code
Terminal-Bench v2.1
83%
84%
Muse Code hat einen höheren Terminal-Bench v2.1-Wert als Codex
SWE-Atlas-QnA
43%
52%
Muse Code hat einen höheren SWE-Atlas-QnA-Wert als Codex
Kosten pro Aufgabe
$5.00
$0.00
Vergleich nicht verfügbar
Zeit pro Aufgabe
10.2m
24.6m
Codex hat eine geringere Zeit pro Aufgabe als Muse Code
Runden pro Aufgabe
112.3
128.5
Codex hat weniger Runden pro Aufgabe als Muse Code
Token-Nutzung pro Aufgabe
13.2M
14.2M
Codex hat eine geringere Token-Nutzung pro Aufgabe als Muse Code
Cache-Trefferquote
90%
95%
Muse Code hat eine höhere Cache-Trefferquote als Codex

Modellvarianten

Bewertete Modellvarianten für Codex und Muse Code.

Modellvarianten

Codex
GPT-5.6 Sol (max)
65
69%
83%
43%
$5.00
10.2m
13.2M
Codex
GPT-5.6 Sol (high)
64
65%
82%
45%
$3.00
6.2m
8M
Codex
GPT-5.6 Sol (xhigh)
63
67%
80%
43%
$3.74
7.3m
9.9M
Codex
GPT-5.6 Sol (medium)
62
64%
81%
40%
$2.19
5.0m
5.8M
Codex
GPT-5.5 (xhigh)
61
64%
83%
36%
$4.75
10.2m
12.2M
Codex
GPT-5.6 Terra (max)
60
67%
78%
36%
$1.93
8.2m
9.6M
Codex
GPT-5.6 Luna (max)
57
63%
75%
33%
$0.29
8.0m
16M
Codex
GPT-5.6 Terra (xhigh)
56
58%
77%
33%
$1.36
6.7m
6.6M
Codex
GPT-5.5 (medium)
55
57%
79%
31%
$2.65
6.4m
6.9M
Codex
GPT-5.6 Sol (low)
55
53%
78%
34%
$1.29
3.5m
3.2M
Codex
GPT-5.6 Terra (high)
55
60%
72%
31%
$1.14
6.0m
5.6M
Codex
GPT-5.6 Luna (xhigh)
53
57%
71%
31%
$0.24
6.9m
12.8M
Codex
GPT-5.6 Luna (high)
52
53%
73%
29%
$0.18
5.7m
9.6M
Codex
DeepSeek V4 Flash 0731 (max)
50
43%
68%
39%
$0.06
14.5m
20.8M
Codex
GPT-5.6 Terra (medium)
48
46%
70%
29%
$0.67
4.0m
3.2M
Codex
GPT-5.6 Sol (none)
43
35%
60%
34%
$1.09
3.3m
3.4M
Codex
DeepSeek V4 Pro 0813 (max)
43
16%
63%
49%
$0.09
12.4m
12.7M
Codex
GPT-5.6 Luna (medium)
42
37%
62%
27%
$0.09
3.2m
4.3M
Codex
GPT-5.6 Terra (low)
39
30%
63%
23%
$0.39
2.6m
1.7M
Codex
GPT-5.6 Luna (low)
25
10%
50%
15%
$0.04
1.7m
1.4M
Codex
GPT-5.6 Terra (none)
23
13%
37%
19%
$0.29
1.5m
1.1M
Codex
GPT-5.6 Luna (none)
19
6%
33%
17%
$0.07
2.2m
3.5M
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

Leistung

Leistung im 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.

Token-Nutzung

Token-Verbrauch im Artificial Analysis Coding Agent Index.

Token-Nutzung pro Aufgabe

Durchschnittliche Eingabe-, Cache- und Ausgabe-Tokens pro Aufgabe
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. Gesamt-Tokens

Artificial Analysis Coding Agent Index vs. durchschnittliche Gesamt-Tokens pro Aufgabe
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.

Kosten

Pay-per-Token-API-Kosten im Artificial Analysis Coding Agent Index, basierend auf den aktuellen Preisen pro Token.

Kosten pro Aufgabe

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. Kosten pro Aufgabe

Artificial Analysis Coding Agent Index vs. durchschnittliche Pay-per-Token-API-Kosten pro Aufgabe (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.

Ausführungszeit

Aktive Agent-Laufzeit im Artificial Analysis Coding Agent Index.

Zeit pro Aufgabe

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. Ausführungszeit

Artificial Analysis Coding Agent Index vs. durchschnittliche Agent-Laufzeit pro Aufgabe
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.