GLM-5.3-Flash (max) vs Qwen 3.6 35B-A3B (Reasoning)
Results updated 6 November 2026 · Both in Terminus-2 · How we benchmark
Verdict
GLM-5.3-Flash (max) beats Qwen 3.6 35B-A3B (Reasoning) by 3 points; Qwen 3.6 35B-A3B (Reasoning) is cheaper and faster.
- Cost per task
- $0.094 vs $0.068
- Time per task
- 8.5 min vs 6.2 min
- Tokens per attempt
- 817k vs 358k
- Image input
- Yes vs Yes
GLM-5.3-Flash (max), Terminus-2 scores higher on GeoBenchX and GS-QA2; Qwen 3.6 35B-A3B (Reasoning), Terminus-2 scores higher on Earth-Bench V2. Qwen 3.6 35B-A3B (Reasoning), Terminus-2 is cheaper and faster: $0.068 against $0.094 per task, and 6.2 min against 8.5 min.
Where they differ most
By kind of work, share of tasks passed, 0 to 100 · Higher is better
| Kind of work | GLM-5.3-Flash (max) | Qwen 3.6 35B-A3B (Reasoning) |
|---|---|---|
| GIS analysis | 86 | 68 |
| Climate and time series | 50 | 62 |
| Change detection | 52 | 60 |
| Making maps | 67 | 70 |
Where the tasks are set: each kind of work links to its tasks and scoresCharts for each: GLM-5.3-Flash (max), Terminus-2 · Qwen 3.6 35B-A3B (Reasoning), Terminus-2.
Side by side
| Metric | GLM-5.3-Flash (max), Terminus-2 | Qwen 3.6 35B-A3B (Reasoning), Terminus-2 |
|---|---|---|
| Geospatial Agent Index: Text only | 63 | 60 |
| Geospatial Agent Index: Text + vision | No data | No data |
| Earth-Bench V2 score | 51 | 62 |
| GeoBenchX score | 57 | 51 |
| GS-QA2 score | 80 | 69 |
| Vision agents score | No data | No data |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 84 | 65 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 84 | 58 |
| OGD4All score (standalone; in no index) | No data | 93 |
| GeoNatureAgent score (standalone; in no index) | No data | 93 |
| MultiGlobeQA score (standalone; in no index) | No data | 72 |
| GeoAgentEval score (standalone; in no index) | No data | 84 |
| ImpactMesh score (standalone; in no index) | No data | No data |
| Remote sensing and raster analysis score | 69 | 67 |
| Change detection score | 52 | 60 |
| GIS analysis score | 86 | 68 |
| Climate and time series score | 50 | 62 |
| Making maps score | 67 | 70 |
| Cost per task | $0.094 | $0.068 |
| Time per task (at 100 output tokens/s) | 8.5 min | 6.2 min |
| Tokens per attempt | 817k | 358k |
| Turns per attempt | 15.8 | 16.0 |
| Input price per 1M tokens | $0.15 | $0.25 |
| Output price per 1M tokens | $0.50 | $1.49 |
| Context window | 1,048,576 | 262,144 |
| Image input | Yes | Yes |
| Developer | Z.ai | Alibaba (Qwen) |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, GLM-5.3-Flash (max), Terminus-2 or Qwen 3.6 35B-A3B (Reasoning), Terminus-2?
GLM-5.3-Flash (max), Terminus-2, 63 to 60.
Which is cheaper per task, GLM-5.3-Flash (max), Terminus-2 or Qwen 3.6 35B-A3B (Reasoning), Terminus-2?
Qwen 3.6 35B-A3B (Reasoning), Terminus-2, $0.068 to $0.094.
Which writes fewer output tokens per task, GLM-5.3-Flash (max), Terminus-2 or Qwen 3.6 35B-A3B (Reasoning), Terminus-2?
Qwen 3.6 35B-A3B (Reasoning), Terminus-2, 16k to 30k.
Which has the larger context window, GLM-5.3-Flash (max), Terminus-2 or Qwen 3.6 35B-A3B (Reasoning), Terminus-2?
GLM-5.3-Flash (max), Terminus-2, 1,048,576 tokens to 262,144 tokens.