Claude Code – GLM-4.7-Flash (thinking on) vs. Codex – GPT-6.1 Sol (medium): geospatial agent comparison
Comparison between Claude Code – GLM-4.7-Flash (thinking on) and Codex – GPT-6.1 Sol (medium) 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-6.1 Sol (medium) |
|---|---|---|
| Geospatial Agent Index | 27* (partial: attempt 1 on 52 of 565 tasks) | 72* |
| Earth-Bench V2 score | 20 | 61 |
| GeoBenchX score | 33 | 69 |
| GS-QA2 score | No data | 85 |
| GeoAgentBench score (guided GIS workflow; outside the index) | No data | 100 |
| GeoAnalystBench score (guided GIS workflow; outside the index) | No data | 100 |
| Raster and remote sensing score | 15 | 74 |
| Vector and overlay score | No data | 82 |
| Networks and routing score | No data | No data |
| Climate and time series score | 0 | 68 |
| Spatial statistics and interpolation score | 20 | 29 |
| Mapping and cartography score | 64 | 78 |
| Recognising infeasible requests score | 26 | 70 |
| Cost per task | $0.089 | $0.049 |
| Time per task | 13.6 min | 1.2 min |
| Tokens per attempt | 1.37M | 93k |
| Turns per attempt | 35.5 | 5.9 |
| Attempts decided | 49 | 565 |
| Input price per 1M tokens | $0.06 | $2.00 |
| Output price per 1M tokens | $0.40 | $10.00 |
| Context window | 131,072 | No data |
| Image input | No | No data |
| 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. 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) |
|---|---|---|---|---|---|
| Codex – GPT-6.1 Sol (medium) | OpenAI | 72* | No pending judgements; coverage incomplete; 208 results awaiting review | 3 of 3 | 565 of 859 planned 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).
Data table: Cost per task
| Model | Creator | Cost per task | Coverage (attempts) |
|---|---|---|---|
| Codex – GPT-6.1 Sol (medium) | OpenAI | $0.049 | 565 of 859 planned 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).
Data table: Output tokens per task
| Model | Creator | Output tokens per task | Coverage (attempts) |
|---|---|---|---|
| Codex – GPT-6.1 Sol (medium) | OpenAI | 1k | 565 of 859 planned attempts |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Claude Code – GLM-4.7-Flash (thinking on) or Codex – GPT-6.1 Sol (medium)?
Which is cheaper per task, Claude Code – GLM-4.7-Flash (thinking on) or Codex – GPT-6.1 Sol (medium)?
Which writes fewer output tokens per task, Claude Code – GLM-4.7-Flash (thinking on) or Codex – GPT-6.1 Sol (medium)?
Which has the larger context window, Claude Code – GLM-4.7-Flash (thinking on) or Codex – GPT-6.1 Sol (medium)?
More: Claude Code – GLM-4.7-Flash (thinking on) · Codex – GPT-6.1 Sol (medium) · all comparisons