Claude Code – Claude Haiku 5.5 (high) vs. Codex – GPT-6 Luna (low): geospatial agent comparison
Comparison between Claude Code – Claude Haiku 5.5 (high) and Codex – GPT-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
| Metric | Claude Code – Claude Haiku 5.5 (high) | Codex – GPT-6 Luna (low) |
|---|---|---|
| Geospatial Agent Index | 76* | 51* (partial: attempt 1 on 449 of 488 tasks) |
| Earth-Bench V2 score | 72 | 44 |
| GeoBenchX score | 72 | 50 |
| GS-QA2 score | 83 | 59 |
| GeoAgentBench score (guided GIS workflow; outside the index) | 98 | 92 |
| GeoAnalystBench score (guided GIS workflow; outside the index) | 84 | 79 |
| Raster and remote sensing score | 79 | 55 |
| Vector and overlay score | 82 | 57 |
| Climate and time series score | 76 | 49 |
| Spatial statistics and interpolation score | 31 | 38 |
| Mapping and cartography score | 81 | 71 |
| Recognising infeasible requests score | 73 | 30 |
| Cost per task | $0.0085 | $0.0031 |
| Time per task | 0.9 min | 1.4 min |
| Tokens per attempt | 271k | 114k |
| Turns per attempt | 7.1 | 7.8 |
| Attempts decided | 488 | 449 |
| Input price per 1M tokens | $0.10 | $0.10 |
| Output price per 1M tokens | $0.50 | $0.50 |
| Context window | No data | No data |
| Image input | No data | No data |
| Developer | Anthropic | OpenAI |
Charts
Partial coverage. Codex – GPT-6 Luna (low): 111 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
First eight selected entries in metric order. Full results in table view.
Data table: Geospatial Agent Index
| Model | Creator | Geospatial Agent Index | Range while attempts are pending | Benchmarks covered | Coverage (attempts) |
|---|---|---|---|---|---|
| Claude Code – Claude Haiku 5.5 (high) | Anthropic | 76* | No pending judgements; coverage incomplete | 3 of 3 | 488 of 722 scheduled attempts |
Cost per task
Average cost per task (USD) · Lower is better
First eight selected entries in metric order. Full results in table view.
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
| Model | Creator | Cost per task | Coverage (attempts) |
|---|---|---|---|
| Claude Code – Claude Haiku 5.5 (high) | Anthropic | $0.0085 | 488 of 722 scheduled attempts |
Output tokens per task
Average output tokens per attempt, reasoning included · Lower is better
First eight selected entries in metric order. Full results in table view.
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
| Model | Creator | Output tokens per task | Coverage (attempts) |
|---|---|---|---|
| Claude Code – Claude Haiku 5.5 (high) | Anthropic | 6k | 488 of 722 scheduled attempts |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6 Luna (low)?
Which is cheaper per task, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6 Luna (low)?
Which writes fewer output tokens per task, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6 Luna (low)?
Which has the larger context window, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6 Luna (low)?
More: Claude Code – Claude Haiku 5.5 (high) · Codex – GPT-6 Luna (low) · all comparisons