GLM-5.2 (Non-reasoning) vs MiMo V2.6 Flash (Reasoning)
Results updated 1 November 2026 · In Terminus-2 and MiMo Code, so part of any gap can come from the harness · How we benchmark
Verdict
MiMo V2.6 Flash (Reasoning) edges GLM-5.2 (Non-reasoning) by 1 point, and is cheaper; GLM-5.2 (Non-reasoning) is faster.
- Cost per task
- $0.099 vs $0.047
- Time per task
- 1.5 min vs 5.3 min (estimated)
- Tokens per attempt
- 185k vs 2.16M
- Image input
- No vs Yes
GLM-5.2 (Non-reasoning), Terminus-2 scores higher on Earth-Bench V2 and GeoBenchX; MiMo V2.6 Flash (Reasoning), MiMo Code scores higher on GS-QA2. MiMo V2.6 Flash (Reasoning), MiMo Code is cheaper ($0.047 against $0.099 per task), but GLM-5.2 (Non-reasoning), Terminus-2 is faster (1.5 min against 5.3 min).
Where they differ most
By kind of work, share of tasks passed, 0 to 100 · Higher is better
| Kind of work | GLM-5.2 (Non-reasoning) | MiMo V2.6 Flash (Reasoning) |
|---|---|---|
| GIS analysis | 65 | 83 |
| Making maps | 79 | 67 |
| Climate and time series | 58 | 51 |
| Recognising infeasible requests | 45 | 48 |
Where the tasks are set: each kind of work links to its tasks and scoresCharts for each: GLM-5.2 (Non-reasoning), Terminus-2 · MiMo V2.6 Flash (Reasoning), MiMo Code.
Side by side
| Metric | GLM-5.2 (Non-reasoning), Terminus-2 | MiMo V2.6 Flash (Reasoning), MiMo Code |
|---|---|---|
| Geospatial Agent Index: Text only | 63 | 64 |
| Geospatial Agent Index: Text + vision | No image input | No data |
| Earth-Bench V2 score | 58 | 55 |
| GeoBenchX score | 60 | 57 |
| GS-QA2 score | 72 | 82 |
| Vision agents score | No image input | No data |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 92 | 90 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 74 | 79 |
| OGD4All score (standalone; in no index) | No data | 84 |
| GeoNatureAgent score (standalone; in no index) | No data | 92 |
| MultiGlobeQA score (standalone; in no index) | No data | 56 |
| GeoAgentEval score (standalone; in no index) | No data | 76 |
| Remote sensing and raster analysis score | 72 | 73 |
| GIS analysis score | 65 | 83 |
| Climate and time series score | 58 | 51 |
| Spatial statistics score | 31 | 31 |
| Making maps score | 79 | 67 |
| Recognising infeasible requests score | 45 | 48 |
| Cost per task | $0.099 | $0.047 |
| Time per task (at 100 output tokens/s) | 1.5 min | 5.3 min (estimated) |
| Tokens per attempt | 185k | 2.16M |
| Turns per attempt | 12.8 | 23.6 |
| Input price per 1M tokens | $1.40 | $0.14 |
| Output price per 1M tokens | $4.40 | $0.28 |
| Context window | 262,144 | 1,048,576 |
| Image input | No | Yes |
| Developer | Z.ai | Xiaomi |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, GLM-5.2 (Non-reasoning), Terminus-2 or MiMo V2.6 Flash (Reasoning), MiMo Code?
MiMo V2.6 Flash (Reasoning), MiMo Code, 64 to 63.
Which is cheaper per task, GLM-5.2 (Non-reasoning), Terminus-2 or MiMo V2.6 Flash (Reasoning), MiMo Code?
MiMo V2.6 Flash (Reasoning), MiMo Code, $0.047 to $0.099.
Which writes fewer output tokens per task, GLM-5.2 (Non-reasoning), Terminus-2 or MiMo V2.6 Flash (Reasoning), MiMo Code?
GLM-5.2 (Non-reasoning), Terminus-2, 6k to 32k.
Which has the larger context window, GLM-5.2 (Non-reasoning), Terminus-2 or MiMo V2.6 Flash (Reasoning), MiMo Code?
MiMo V2.6 Flash (Reasoning), MiMo Code, 1,048,576 tokens to 262,144 tokens.