Codex – gpt-oss-120b (high) vs. Codex – GPT-5.6 Luna (low): geospatial agent comparison

Comparison between Codex – gpt-oss-120b (high) and Codex – GPT-5.6 Luna (low) 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-oss-120b (high) vs. Codex – GPT-5.6 Luna (low)
Metric	Codex – gpt-oss-120b (high)	Codex – GPT-5.6 Luna (low)
Geospatial Agent Index	43*	60* (partial: attempt 1 on 397 of 488 tasks)
Earth-Bench V2 score	49	44
GeoBenchX score	55	61
GS-QA2 score	25	76
GeoAgentBench score (guided GIS workflow; outside the index)	No data	86
GeoAnalystBench score (guided GIS workflow; outside the index)	No data	95
Raster and remote sensing score	40	70
Vector and overlay score	23	72
Climate and time series score	44	42
Spatial statistics and interpolation score	0	0
Mapping and cartography score	61	83
Recognising infeasible requests score	62	50
Cost per task	$0.26	$0.0057
Time per task	11.7 min	1.6 min
Tokens per attempt	700k	84k
Turns per attempt	29.8	7.1
Attempts decided	488	397
Input price per 1M tokens	$0.35	$0.20
Output price per 1M tokens	$0.75	$1.20
Context window	128,000	No data
Image input	No	No data
Developer	OpenAI	OpenAI

Charts

Partial coverage. Codex – GPT-5.6 Luna (low): 59 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

Attempts still to run

Data table: Geospatial Agent Index

Geospatial Agent Index
Model	Creator	Geospatial Agent Index	Range while attempts are pending	Benchmarks covered	Coverage (attempts)
Codex – gpt-oss-120b (high)	OpenAI	43*	No pending judgements; coverage incomplete; 58 results awaiting review	3 of 3	488 of 722 scheduled attempts (7 excluded)

Cost per task

Average cost per task (USD) · Lower is better

Attempts still to run

How to read this chart

Not shown: partial coverage. 1 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)
Codex – gpt-oss-120b (high)	OpenAI	$0.26	488 of 722 scheduled attempts (7 excluded)

Output tokens per task

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

Attempts still to run

How to read this chart

Not shown: partial coverage. 1 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)
Codex – gpt-oss-120b (high)	OpenAI	27k	488 of 722 scheduled attempts (7 excluded)

Frequently asked questions

Which scores higher on the Geospatial Agent Index, Codex – gpt-oss-120b (high) or Codex – GPT-5.6 Luna (low)?

Not comparable yet: Codex – GPT-5.6 Luna (low) has partial coverage (59 of 150 GeoBenchX tasks), so it is not ranked. Codex – gpt-oss-120b (high): 488 of 722 scheduled attempts (7 excluded). Codex – GPT-5.6 Luna (low): 397 of 722 scheduled attempts. Coverage is partial; scores can change as more attempts are decided.

Which is cheaper per task, Codex – gpt-oss-120b (high) or Codex – GPT-5.6 Luna (low)?

Codex – GPT-5.6 Luna (low), at $0.0057 against $0.26 for Codex – gpt-oss-120b (high).

Which writes fewer output tokens per task, Codex – gpt-oss-120b (high) or Codex – GPT-5.6 Luna (low)?

Codex – GPT-5.6 Luna (low), at 1k against 27k for Codex – gpt-oss-120b (high).

Which has the larger context window, Codex – gpt-oss-120b (high) or Codex – GPT-5.6 Luna (low)?

Not known yet: Codex – GPT-5.6 Luna (low) has no context window data.

More: Codex – gpt-oss-120b (high) · Codex – GPT-5.6 Luna (low) · all comparisons