Qwen 3.8 27B (xhigh), Qwen Code vs. GPT-6 Luna (medium), Codex
Qwen 3.8 27B (xhigh), Qwen Code against GPT-6 Luna (medium), Codex: Geospatial Agent Index, each evaluation, cost, time and tokens, on the same tasks and limits. The harnesses differ, so part of any gap can come from the harness. Methodology.
Side by side
| Metric | Qwen 3.8 27B (xhigh), Qwen Code | GPT-6 Luna (medium), Codex |
|---|---|---|
| Geospatial Agent Index: Text only | 68 | 57 |
| Geospatial Agent Index: Text + vision | Not run: Vision agents | 59 |
| Earth-Bench V2 score | 68 | 50 |
| GeoBenchX score | 62 | 57 |
| GS-QA2 score | 75 | 65 |
| Vision agents score | No data | 64 |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 94 | 94 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 79 | 89 |
| Raster and remote sensing score | 76 | 63 |
| Vector and overlay score | 75 | 65 |
| Climate and time series score | 61 | 47 |
| Spatial statistics and interpolation score | 54 | 23 |
| Mapping and cartography score | 63 | 75 |
| Recognising infeasible requests score | 60 | 43 |
| Cost per task | $0.22 | $0.0026 |
| Time per task (at 100 output tokens/s) | 4.7 min (estimated) | 0.4 min |
| Tokens per attempt | 840k | 86k |
| Turns per attempt | 17.6 | 6.4 |
| Attempts scored | 488 | 488 |
| Input price per 1M tokens | $0.45 | $0.10 |
| Output price per 1M tokens | $3.20 | $0.50 |
| Context window | 262,144 | 1,050,000 |
| Image input | Yes | Yes |
| Developer | Alibaba (Qwen) | OpenAI |
Charts
Geospatial Agent Index
Equal average of Earth-Bench V2, GeoBenchX and GS-QA2, 0 to 100 · Higher is better
Top eight. Every model is in the table.
Data table: Geospatial Agent Index
| Model | Developer | Geospatial Agent Index | Status | Evaluations scored | Tasks attempted |
|---|---|---|---|---|---|
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 68 | Complete | 3 of 3 | 488 of 488 tasks |
| GPT-6 Luna (medium), Codex | OpenAI | 57 | Complete | 3 of 3 | 488 of 488 tasks |
Cost per task
Average cost per task (USD) · Lower is better
Top eight. Every model is in the table.
Data table: Cost per task
| Model | Developer | Cost per task | Tasks attempted |
|---|---|---|---|
| GPT-6 Luna (medium), Codex | OpenAI | $0.0026 | 488 of 488 tasks |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | $0.22 | 488 of 488 tasks |
Output tokens per task
Average output tokens per attempt, reasoning included · Lower is better
Top eight. Every model is in the table.
Data table: Output tokens per task
| Model | Developer | Output tokens per task | Tasks attempted |
|---|---|---|---|
| GPT-6 Luna (medium), Codex | OpenAI | 1k | 488 of 488 tasks |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 28k | 488 of 488 tasks |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Qwen 3.8 27B (xhigh), Qwen Code or GPT-6 Luna (medium), Codex?
Qwen 3.8 27B (xhigh), Qwen Code, 68 to 57. Both have attempted all 488 tasks.
Which is cheaper per task, Qwen 3.8 27B (xhigh), Qwen Code or GPT-6 Luna (medium), Codex?
GPT-6 Luna (medium), Codex, $0.0026 to $0.22.
Which writes fewer output tokens per task, Qwen 3.8 27B (xhigh), Qwen Code or GPT-6 Luna (medium), Codex?
GPT-6 Luna (medium), Codex, 1k to 28k.
Which has the larger context window, Qwen 3.8 27B (xhigh), Qwen Code or GPT-6 Luna (medium), Codex?
GPT-6 Luna (medium), Codex, 1,050,000 tokens to 262,144 tokens.
More: Qwen 3.8 27B (xhigh), Qwen Code · GPT-6 Luna (medium), Codex · All comparisons