Codex – GPT-6 Sol (high) vs. Codex – GPT-6 Luna (high): geospatial agent comparison

Comparison between Codex – GPT-6 Sol (high) and Codex – GPT-6 Luna (high) on the Geospatial Agent Index, each benchmark, cost, time and token use, from the same tasks, agent and limits. Methodology.

Side by side

Codex – GPT-6 Sol (high) vs. Codex – GPT-6 Luna (high)
Metric	Codex – GPT-6 Sol (high)	Codex – GPT-6 Luna (high)
Geospatial Agent Index	80* (partial: attempt 1 on 475 of 488 tasks)	71* (partial: attempt 1 on 444 of 488 tasks)
Earth-Bench V2 score	76	66
GeoBenchX score	75	71
GS-QA2 score	89	77
GeoAgentBench score (guided GIS workflow; outside the index)	100	96
GeoAnalystBench score (guided GIS workflow; outside the index)	100	100
Raster and remote sensing score	84	73
Vector and overlay score	86	77
Climate and time series score	80	69
Spatial statistics and interpolation score	33	33
Mapping and cartography score	85	71
Recognising infeasible requests score	79	78
Cost per task	$0.080	$0.0039
Time per task	1.7 min	1.7 min
Tokens per attempt	144k	116k
Turns per attempt	8.6	7.3
Attempts decided	460	430
Input price per 1M tokens	$2.00	$0.10
Output price per 1M tokens	$10.00	$0.50
Context window	No data	No data
Image input	No data	No data
Developer	OpenAI	OpenAI

Charts

Partial coverage. Codex – GPT-6 Sol (high): 116 of 117 Earth-Bench V2 tasks; 138 of 150 GeoBenchX tasks; Codex – GPT-6 Luna (high): 106 of 150 GeoBenchX tasks. Grey placeholders identify these entries in the index, cost and output token charts; their height does not represent a value. Comparable bars appear when first-attempt coverage is complete. Their available results remain in the side-by-side table.

Geospatial Agent Index

Equal-weight average of the evaluation scores, 0 to 100 · Higher is better

Data table: Geospatial Agent Index

Geospatial Agent Index
Model	Creator	Geospatial Agent Index	Range while attempts are pending	Benchmarks covered	Coverage (attempts)

Cost per task

Average cost per task (USD) · Lower is better

How to read this chart

Not shown: partial coverage. 2 entries without a first attempt on every index task. What is still running is on the models page.

Data table: Cost per task

Cost per task
Model	Creator	Cost per task	Coverage (attempts)

Output tokens per task

Average output tokens per attempt, reasoning included · Lower is better

How to read this chart

Not shown: partial coverage. 2 entries without a first attempt on every index task. What is still running is on the models page.

Data table: Output tokens per task

Output tokens per task
Model	Creator	Output tokens per task	Coverage (attempts)

Frequently asked questions

Which scores higher on the Geospatial Agent Index, Codex – GPT-6 Sol (high) or Codex – GPT-6 Luna (high)?

Not comparable yet: Codex – GPT-6 Sol (high) has partial coverage (116 of 117 Earth-Bench V2 tasks; 138 of 150 GeoBenchX tasks) and Codex – GPT-6 Luna (high) has partial coverage (106 of 150 GeoBenchX tasks), so it is not ranked. Codex – GPT-6 Sol (high): 460 of 722 scheduled attempts. Codex – GPT-6 Luna (high): 430 of 722 scheduled attempts. Coverage is partial; scores can change as more attempts are decided.

Which is cheaper per task, Codex – GPT-6 Sol (high) or Codex – GPT-6 Luna (high)?

Codex – GPT-6 Luna (high), at $0.0039 against $0.080 for Codex – GPT-6 Sol (high).

Which writes fewer output tokens per task, Codex – GPT-6 Sol (high) or Codex – GPT-6 Luna (high)?

Codex – GPT-6 Luna (high), at 3k against 3k for Codex – GPT-6 Sol (high).

Which has the larger context window, Codex – GPT-6 Sol (high) or Codex – GPT-6 Luna (high)?

Not known yet: Codex – GPT-6 Sol (high) has no context window data and Codex – GPT-6 Luna (high) has no context window data.

More: Codex – GPT-6 Sol (high) · Codex – GPT-6 Luna (high) · all comparisons