Qwen 3.6 35B-A3B (Reasoning) vs GPT-6 Luna (medium)
Results updated 5 November 2026 · In Qwen Code and Codex, so part of any gap can come from the harness · How we benchmark
Verdict
Qwen 3.6 35B-A3B (Reasoning) and GPT-6 Luna (medium) are level on the Geospatial Agent Index, at 57.
- Cost per task
- $0.00 vs $0.0026
- Time per task
- 2.9 min (estimated) vs 0.4 min
- Tokens per attempt
- 650k vs 86k
- Image input
- Yes vs Yes
Qwen 3.6 35B-A3B (Reasoning), Qwen Code scores higher on Earth-Bench V2; GPT-6 Luna (medium), Codex scores higher on GeoBenchX and GS-QA2. Qwen 3.6 35B-A3B (Reasoning), Qwen Code is cheaper ($0.00 against $0.0026 per task), but GPT-6 Luna (medium), Codex is faster (0.4 min against 2.9 min).
Where they differ most
By kind of work, share of tasks passed, 0 to 100 · Higher is better
| Kind of work | Qwen 3.6 35B-A3B (Reasoning) | GPT-6 Luna (medium) |
|---|---|---|
| Change detection | 68 | 48 |
| Making maps | 59 | 75 |
| Climate and time series | 64 | 48 |
| GIS analysis | 56 | 65 |
Where the tasks are set: each kind of work links to its tasks and scoresCharts for each: Qwen 3.6 35B-A3B (Reasoning), Qwen Code · GPT-6 Luna (medium), Codex.
Side by side
| Metric | Qwen 3.6 35B-A3B (Reasoning), Qwen Code | GPT-6 Luna (medium), Codex |
|---|---|---|
| Geospatial Agent Index: Text only | 57 | 57 |
| Geospatial Agent Index: Text + vision | No data | 59 |
| Earth-Bench V2 score | 60 | 50 |
| GeoBenchX score | 50 | 57 |
| GS-QA2 score | 62 | 65 |
| Vision agents score | No data | 64 |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 65 | 94 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 47 | 89 |
| OGD4All score (standalone; in no index) | 93 | No data |
| GeoNatureAgent score (standalone; in no index) | 79 | No data |
| MultiGlobeQA score (standalone; in no index) | 72 | No data |
| GeoAgentEval score (standalone; in no index) | 68 | No data |
| ImpactMesh score (standalone; in no index) | No data | No data |
| Remote sensing and raster analysis score | 65 | 64 |
| Change detection score | 68 | 48 |
| GIS analysis score | 56 | 65 |
| Climate and time series score | 64 | 48 |
| Making maps score | 59 | 75 |
| Cost per task | $0.00 | $0.0026 |
| Time per task (at 100 output tokens/s) | 2.9 min (estimated) | 0.4 min |
| Tokens per attempt | 650k | 86k |
| Turns per attempt | 16.9 | 6.4 |
| Input price per 1M tokens | $0.25 | $0.10 |
| Output price per 1M tokens | $1.49 | $0.50 |
| Context window | 262,144 | 1,050,000 |
| Image input | Yes | Yes |
| Developer | Alibaba (Qwen) | OpenAI |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Qwen 3.6 35B-A3B (Reasoning), Qwen Code or GPT-6 Luna (medium), Codex?
Qwen 3.6 35B-A3B (Reasoning), Qwen Code, 57 to 57.
Which is cheaper per task, Qwen 3.6 35B-A3B (Reasoning), Qwen Code or GPT-6 Luna (medium), Codex?
Qwen 3.6 35B-A3B (Reasoning), Qwen Code, $0.00 to $0.0026.
Which writes fewer output tokens per task, Qwen 3.6 35B-A3B (Reasoning), Qwen Code or GPT-6 Luna (medium), Codex?
GPT-6 Luna (medium), Codex, 1k to 18k.
Which has the larger context window, Qwen 3.6 35B-A3B (Reasoning), Qwen Code or GPT-6 Luna (medium), Codex?
GPT-6 Luna (medium), Codex, 1,050,000 tokens to 262,144 tokens.