Claude Code – GLM-5.2 (max) vs. Codex – GPT-5.6 Luna (xhigh): geospatial agent comparison

Comparison between Claude Code – GLM-5.2 (max) and Codex – GPT-5.6 Luna (xhigh) on the Geospatial Agent Index, each benchmark, cost, time and token use, from the same tasks, agent and limits. Methodology.

Side by side

Claude Code – GLM-5.2 (max) vs. Codex – GPT-5.6 Luna (xhigh)
Metric	Claude Code – GLM-5.2 (max)	Codex – GPT-5.6 Luna (xhigh)
Geospatial Agent Index	73*	77* (partial: attempt 1 on 210 of 488 tasks)
Earth-Bench V2 score	71	86
GeoBenchX score	66	60
GS-QA2 score	83	85
GeoAgentBench score (guided GIS workflow; outside the index)	No data	98
GeoAnalystBench score (guided GIS workflow; outside the index)	No data	100
Raster and remote sensing score	80	82
Vector and overlay score	81	86
Climate and time series score	72	90
Spatial statistics and interpolation score	46	33
Mapping and cartography score	84	75
Recognising infeasible requests score	52	50
Cost per task	$0.34	$0.012
Time per task	5.6 min	2.5 min
Tokens per attempt	617k	176k
Turns per attempt	15.4	9.7
Attempts decided	486	210
Input price per 1M tokens	$1.40	$0.20
Output price per 1M tokens	$4.40	$1.20
Context window	262,144	No data
Image input	No	No data
Developer	Z.ai	OpenAI

Charts

Partial coverage. Claude Code – GLM-5.2 (max): 115 of 117 Earth-Bench V2 tasks; Codex – GPT-5.6 Luna (xhigh): 49 of 117 Earth-Bench V2 tasks; 20 of 150 GeoBenchX tasks; 141 of 221 GS-QA2 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, Claude Code – GLM-5.2 (max) or Codex – GPT-5.6 Luna (xhigh)?

Not comparable yet: Claude Code – GLM-5.2 (max) has partial coverage (115 of 117 Earth-Bench V2 tasks) and Codex – GPT-5.6 Luna (xhigh) has partial coverage (49 of 117 Earth-Bench V2 tasks; 20 of 150 GeoBenchX tasks; 141 of 221 GS-QA2 tasks), so it is not ranked. Claude Code – GLM-5.2 (max): 486 of 722 scheduled attempts (11 excluded). Codex – GPT-5.6 Luna (xhigh): 210 of 722 scheduled attempts. Coverage is partial; scores can change as more attempts are decided.

Which is cheaper per task, Claude Code – GLM-5.2 (max) or Codex – GPT-5.6 Luna (xhigh)?

Codex – GPT-5.6 Luna (xhigh), at $0.012 against $0.34 for Claude Code – GLM-5.2 (max).

Which writes fewer output tokens per task, Claude Code – GLM-5.2 (max) or Codex – GPT-5.6 Luna (xhigh)?

Codex – GPT-5.6 Luna (xhigh), at 4k against 28k for Claude Code – GLM-5.2 (max).

Which has the larger context window, Claude Code – GLM-5.2 (max) or Codex – GPT-5.6 Luna (xhigh)?

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

More: Claude Code – GLM-5.2 (max) · Codex – GPT-5.6 Luna (xhigh) · all comparisons