Claude Code – GLM-4.7-Flash (thinking on) vs. Codex – gpt-oss-120b (high): geospatial agent comparison
Comparison between Claude Code – GLM-4.7-Flash (thinking on) and Codex – gpt-oss-120b (high) 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 – GLM-4.7-Flash (thinking on) | Codex – gpt-oss-120b (high) |
|---|---|---|
| Geospatial Agent Index | 27* (partial: attempt 1 on 52 of 565 tasks) | 47* (partial: attempt 1 on 49 of 565 tasks) |
| Earth-Bench V2 score | 20 | 62 |
| GeoBenchX score | 33 | 31 |
| GS-QA2 score | No data | No data |
| GeoAgentBench score (guided GIS workflow; outside the index) | No data | No data |
| GeoAnalystBench score (guided GIS workflow; outside the index) | No data | No data |
| Raster and remote sensing score | 15 | 64 |
| Vector and overlay score | No data | No data |
| Networks and routing score | No data | No data |
| Climate and time series score | 0 | 0 |
| Spatial statistics and interpolation score | 20 | 0 |
| Mapping and cartography score | 64 | 44 |
| Recognising infeasible requests score | 26 | 25 |
| Cost per task | $0.089 | $0.236 |
| Time per task | 13.6 min | 12.4 min |
| Tokens per attempt | 1.37M | 642k |
| Turns per attempt | 35.5 | 29.7 |
| Attempts decided | 49 | 44 |
| Input price per 1M tokens | $0.06 | $0.35 |
| Output price per 1M tokens | $0.40 | $0.75 |
| Context window | 131,072 | 128,000 |
| Image input | No | No |
| Developer | Z.ai | OpenAI |
Charts
Partial coverage. Claude Code – GLM-4.7-Flash (thinking on): 10 of 147 Earth-Bench V2 tasks; 42 of 173 GeoBenchX tasks; GS-QA2 incomplete; Codex – gpt-oss-120b (high): 8 of 147 Earth-Bench V2 tasks; 41 of 173 GeoBenchX tasks; GS-QA2 incomplete. 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) |
|---|
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. Claude Code – GLM-4.7-Flash (thinking on), Codex – gpt-oss-120b (high).
Data table: Cost per task
| Model | Creator | Cost per task | Coverage (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. Claude Code – GLM-4.7-Flash (thinking on), Codex – gpt-oss-120b (high).
Data table: 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-4.7-Flash (thinking on) or Codex – gpt-oss-120b (high)?
Which is cheaper per task, Claude Code – GLM-4.7-Flash (thinking on) or Codex – gpt-oss-120b (high)?
Which writes fewer output tokens per task, Claude Code – GLM-4.7-Flash (thinking on) or Codex – gpt-oss-120b (high)?
Which has the larger context window, Claude Code – GLM-4.7-Flash (thinking on) or Codex – gpt-oss-120b (high)?
More: Claude Code – GLM-4.7-Flash (thinking on) · Codex – gpt-oss-120b (high) · all comparisons