Cursor CLI vs. TBH

Comparación entre Cursor CLI y TBH en el Artificial Analysis Coding Agent Index, incluidos resultados de benchmarks, coste, tiempo de ejecución y uso de tokens.

Para más detalles sobre nuestra metodología, consulta nuestra página de metodología.

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Destacados

Coding Agent Index

Artificial Analysis Coding Agent Index v1.3 · Higher is better

Time per Task

Average agent wall time per task · Lower is better

Cost per Task

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

Comparison

Side-by-side comparison of Cursor CLI and TBH.

Coding Agent Comparison

Metric
Cursor CLI
GPT-5.5 (medium)
TBH
Muse Spark 1.2 (xhigh)
Analysis
Agent Harness
Cursor CLI
TBH
Representative Model
GPT-5.5 (medium)
Muse Spark 1.2 (xhigh)
Coding Agent Index
46
61
TBH has a higher Coding Agent Index than Cursor CLI
DeepSWE
37%
58%
TBH has a higher DeepSWE score than Cursor CLI
Terminal-Bench v2
73%
79%
TBH has a higher Terminal-Bench v2 score than Cursor CLI
SWE-Atlas-QnA
28%
45%
TBH has a higher SWE-Atlas-QnA score than Cursor CLI
Cost per Task
$2.01
$2.33
Cursor CLI has a lower cost per task than TBH
Time per Task
6.6m
42.0m
Cursor CLI has a lower time per task than TBH
Turns per Task
78
149.6
Cursor CLI has a lower turns per task than TBH
Token Usage per Task
4M
21.5M
Cursor CLI has a lower token usage per task than TBH
Cache Hit Rate
89%
95%
TBH has a higher cache hit rate than Cursor CLI

Model Variants

Evaluated model variants for Cursor CLI and TBH.

Model Variants

Cursor CLI
GPT-5.5 (medium)
46
37%
73%
28%
$2.01
6.6m
4M
Cursor CLI
Opus 4.7 (medium)
45
32%
71%
34%
$2.68
13.6m
5.7M
Cursor CLI
Composer 2.5
38
16%
67%
31%
$0.08
9.5m
3.6M
Cursor CLI
Composer 2.5 Fast
38
16%
67%
31%
$0.55
6.8m
4.2M
Cursor CLI
GPT-5.4 (medium)
37
17%
66%
28%
$1.55
8.1m
4M
Cursor CLI
Composer 2
27
0%
65%
18%
$0.04
8.6m
3M
TBH
Muse Spark 1.2 (xhigh)
61
58%
79%
45%
$2.33
42.0m
21.5M

Performance

Performance across the Artificial Analysis Coding Agent Index.

Artificial Analysis Coding Agent Index

Artificial Analysis Coding Agent Index v1.3 incorporates 3 benchmarks: DeepSWE, Terminal-Bench v2, 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, 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 Usage

Token consumption across the Artificial Analysis Coding Agent Index.

Token Usage per Task

Average input, cache, and output tokens per task
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. Total Tokens

Artificial Analysis Coding Agent Index vs. average total tokens per task
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.

Cost

Pay-per-token API cost across the Artificial Analysis Coding Agent Index, based on current per-token pricing.

Cost per Task

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, 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. Cost per Task

Artificial Analysis Coding Agent Index vs. average pay-per-token API cost per task (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.

Execution Time

Active agent runtime across the Artificial Analysis Coding Agent Index.

Time per Task

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. Execution Time

Artificial Analysis Coding Agent Index vs. average agent wall time per task
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.