Codex vs. Opencode

Artificial Analysis Coding Agent Index의 벤치마크 점수, 비용, 실행 시간, 토큰 사용량을 기준으로 Codex 및 Opencode를 비교합니다.

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주요 내용

Coding Agent Index

Artificial Analysis Coding Agent Index v1.4 · 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

Codex 및 Opencode의 나란히 비교.

코딩 에이전트 비교

지표
Codex
GPT-5.6 Sol (max)
Opencode
Gemini 3.7 Flash (high)
분석
에이전트 하네스
Codex
Opencode
대표 모델
GPT-5.6 Sol (max)
Gemini 3.7 Flash (high)
Coding Agent Index
65
60
Codex의 Coding Agent Index가 Opencode보다 높습니다
DeepSWE
69%
57%
Codex의 DeepSWE 점수가 Opencode보다 높습니다
Terminal-Bench v2.1
83%
91%
Opencode의 Terminal-Bench v2.1 점수가 Codex보다 높습니다
SWE-Atlas-QnA
43%
31%
Codex의 SWE-Atlas-QnA 점수가 Opencode보다 높습니다
작업당 비용
$6.42
$1.27
Opencode의 작업당 비용이 Codex보다 낮습니다
작업당 시간
10.2m
8.8m
Opencode의 작업당 시간이 Codex보다 짧습니다
작업당 턴
112.3
83.4
Opencode의 작업당 턴이 Codex보다 적습니다
작업당 토큰 사용량
13.2M
18.5M
Codex의 작업당 토큰 사용량이 Opencode보다 적습니다
캐시 적중률
90%
86%
Codex의 캐시 적중률이 Opencode보다 높습니다

모델 변형

Codex 및 Opencode의 평가된 모델 변형.

모델 변형

Codex
GPT-5.6 Sol (max)
65
69%
83%
43%
$6.42
10.2m
13.2M
Codex
GPT-5.6 Sol (high)
64
65%
82%
45%
$3.85
6.2m
8M
Codex
GPT-5.6 Sol (xhigh)
63
67%
80%
43%
$4.80
7.3m
9.9M
Codex
GPT-5.6 Sol (medium)
62
64%
81%
40%
$2.81
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%
$2.70
8.2m
9.6M
Codex
GPT-5.6 Luna (max)
57
63%
75%
33%
$1.51
8.0m
16M
Codex
GPT-5.6 Terra (xhigh)
56
58%
77%
33%
$1.88
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%
$1.66
3.5m
3.2M
Codex
GPT-5.6 Terra (high)
55
60%
72%
31%
$1.56
6.0m
5.6M
Codex
GPT-5.6 Luna (xhigh)
53
57%
71%
31%
$1.21
6.9m
12.8M
Codex
GPT-5.6 Luna (high)
52
53%
73%
29%
$0.92
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%
$0.88
4.0m
3.2M
Codex
GPT-5.6 Sol (none)
43
35%
60%
34%
$1.40
3.3m
3.4M
Codex
GPT-5.6 Luna (medium)
42
37%
62%
27%
$0.44
3.2m
4.3M
Codex
GPT-5.6 Terra (low)
39
30%
63%
23%
$0.49
2.6m
1.7M
Codex
GPT-5.6 Luna (low)
25
10%
50%
15%
$0.19
1.7m
1.4M
Codex
GPT-5.6 Terra (none)
23
13%
37%
19%
$0.36
1.5m
1.1M
Codex
GPT-5.6 Luna (none)
19
6%
33%
17%
$0.33
2.2m
3.5M
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

성능

Artificial Analysis Coding Agent Index 전반의 성능.

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
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.

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
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

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. 작업당 비용

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

실행 시간

Artificial Analysis Coding Agent Index 전반의 활성 에이전트 실행 시간.

작업당 시간

Average agent wall time per task · Lower is better

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
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.