Gemini CLIとOpencode

Gemini CLIとOpencodeをArtificial Analysis Coding Agent Indexのベンチマークスコア、コスト、実行時間、トークン使用量で比較します。

詳しい方法論は、方法論ページをご覧ください。

ほかの比較を見る
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ハイライト

Coding Agent Index

Artificial Analysis Coding Agent Index v1.4 · Higher is better

タスクあたりの時間

Average agent wall time per task · Lower is better

タスクあたりのコスト

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

比較

Gemini CLI と Opencode の並列比較。

コーディングエージェント比較

指標
Gemini CLI
Gemini 3.1 Pro (high)
Opencode
Gemini 3.7 Flash (high)
分析
エージェントハーネス
Gemini CLI
Opencode
代表モデル
Gemini 3.1 Pro (high)
Gemini 3.7 Flash (high)
Coding Agent Index
33
60
Opencode は Gemini CLI よりCoding Agent Indexが高いです
DeepSWE
14%
57%
Opencode は Gemini CLI より DeepSWE のスコアが高いです
Terminal-Bench v2.1
76%
91%
Opencode は Gemini CLI より Terminal-Bench v2.1 のスコアが高いです
SWE-Atlas-QnA
9%
31%
Opencode は Gemini CLI より SWE-Atlas-QnA のスコアが高いです
タスクあたりのコスト
$1.04
$1.27
Gemini CLI は Opencode よりタスクあたりのコストが低いです
タスクあたりの時間
11.1m
8.8m
Opencode は Gemini CLI よりタスクあたりの時間が短いです
タスクあたりのターン数
31
83.4
Gemini CLI は Opencode よりタスクあたりのターン数が少ないです
タスクあたりのトークン使用量
4.7M
18.5M
Gemini CLI は Opencode よりタスクあたりのトークン使用量が少ないです
キャッシュヒット率
87%
86%
Gemini CLI は Opencode よりキャッシュヒット率が高いです

モデルバリアント

Gemini CLI と Opencode の評価済みモデルバリアント。

モデルバリアント

Gemini CLI
Gemini 3.1 Pro (high)
33
14%
76%
9%
$1.04
11.1m
4.7M
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

パフォーマンス

Artificial Analysis Coding Agent Indexにおけるパフォーマンス。

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.

トークン使用量

Artificial Analysis Coding Agent Indexにおけるトークン消費量。

タスクあたりのトークン使用量

タスクあたりの入力・キャッシュ・出力トークンの平均
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. 合計トークン

Artificial Analysis Coding Agent Index vs. タスクあたりの平均合計トークン
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.

コスト

現在のトークン単位の価格に基づく、Artificial Analysis Coding Agent Indexのトークン従量課金APIコスト。

タスクあたりのコスト

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. タスクあたりのコスト

Artificial Analysis Coding Agent Index vs. タスクあたりの平均トークン従量課金APIコスト（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.

実行時間

Artificial Analysis Coding Agent Indexにおけるエージェントの実稼働時間。

タスクあたりの時間

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. 実行時間

Artificial Analysis Coding Agent Index vs. タスクあたりの平均エージェント稼働時間
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.