Nemotron 3.5 Super 120B (Reasoning) vs gpt-oss-20b (high)
Results updated 9 November 2026 · In Terminus-2 and Codex, so part of any gap can come from the harness · How we benchmark
Verdict
Nemotron 3.5 Super 120B (Reasoning) clearly beats gpt-oss-20b (high) by 47 points, and is cheaper; gpt-oss-20b (high) is faster.
- Cost per task
- $0.011 vs $0.094
- Time per task
- 13.8 min vs 4.8 min
- Tokens per attempt
- 105k vs 457k
- Image input
- No vs No
Nemotron 3.5 Super 120B (Reasoning), Terminus-2 scores higher than gpt-oss-20b (high), Codex on all 3 evaluations scored for both. Nemotron 3.5 Super 120B (Reasoning), Terminus-2 is cheaper ($0.011 against $0.094 per task), but gpt-oss-20b (high), Codex is faster (4.8 min against 13.8 min).
Where they differ most
By kind of work, share of tasks passed, 0 to 100 · Higher is better
| Kind of work | Nemotron 3.5 Super 120B (Reasoning) | gpt-oss-20b (high) |
|---|---|---|
| Making maps | 70 | 4 |
| GIS analysis | 70 | 12 |
| Climate and time series | 50 | 4 |
| Change detection | 56 | 12 |
Where the tasks are set: each kind of work links to its tasks and scoresCharts for each: Nemotron 3.5 Super 120B (Reasoning), Terminus-2 · gpt-oss-20b (high), Codex.
Side by side
| Metric | Nemotron 3.5 Super 120B (Reasoning), Terminus-2 | gpt-oss-20b (high), Codex |
|---|---|---|
| Geospatial Agent Index: Text only | 56 | 9 |
| Geospatial Agent Index: Text + vision | No data | No image input |
| Earth-Bench V2 score | 49 | 6 |
| GeoBenchX score | 56 | 10 |
| GS-QA2 score | 63 | 12 |
| Vision agents score | No data | No image input |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 83 | 10 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 79 | 11 |
| OGD4All score (standalone; in no index) | 85 | No data |
| GeoNatureAgent score (standalone; in no index) | 100 | No data |
| MultiGlobeQA score (standalone; in no index) | 70 | No data |
| GeoAgentEval score (standalone; in no index) | 84 | No data |
| ImpactMesh score (standalone; in no index) | No data | No data |
| Remote sensing and raster analysis score | 54 | 11 |
| Change detection score | 56 | 12 |
| GIS analysis score | 70 | 12 |
| Climate and time series score | 50 | 4 |
| Making maps score | 70 | 4 |
| Cost per task | $0.011 | $0.094 |
| Time per task (at 100 output tokens/s) | 13.8 min | 4.8 min |
| Tokens per attempt | 105k | 457k |
| Turns per attempt | 10.5 | 24.2 |
| Input price per 1M tokens | $0.20 | $0.070 |
| Output price per 1M tokens | $0.50 | $0.30 |
| Context window | 262,144 | 128,000 |
| Image input | No | No |
| Developer | NVIDIA | OpenAI |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Nemotron 3.5 Super 120B (Reasoning), Terminus-2 or gpt-oss-20b (high), Codex?
Nemotron 3.5 Super 120B (Reasoning), Terminus-2, 56 to 9.
Which is cheaper per task, Nemotron 3.5 Super 120B (Reasoning), Terminus-2 or gpt-oss-20b (high), Codex?
Nemotron 3.5 Super 120B (Reasoning), Terminus-2, $0.011 to $0.094.
Which writes fewer output tokens per task, Nemotron 3.5 Super 120B (Reasoning), Terminus-2 or gpt-oss-20b (high), Codex?
Nemotron 3.5 Super 120B (Reasoning), Terminus-2, 8k to 23k.
Which has the larger context window, Nemotron 3.5 Super 120B (Reasoning), Terminus-2 or gpt-oss-20b (high), Codex?
Nemotron 3.5 Super 120B (Reasoning), Terminus-2, 262,144 tokens to 128,000 tokens.