Llama 3.3 70B (Non-reasoning), Terminus-2 vs. Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2
Llama 3.3 70B (Non-reasoning), Terminus-2 against Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: Geospatial Agent Index, each evaluation, cost, time and tokens, on the same tasks and limits. Methodology.
Side by side
| Metric | Llama 3.3 70B (Non-reasoning), Terminus-2 | Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 |
|---|---|---|
| Geospatial Agent Index: Text only | 8 | 52 |
| Geospatial Agent Index: Text + vision | No image input | No data |
| Earth-Bench V2 score | 9 | 46 |
| GeoBenchX score | 6 | 53 |
| GS-QA2 score | 10 | 59 |
| Vision agents score | No image input | No data |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 8 | 68 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 0 | 37 |
| OGD4All score (standalone; in no index) | No data | No data |
| GeoNatureAgent score (standalone; in no index) | No data | No data |
| MultiGlobeQA score (standalone; in no index) | No data | No data |
| GeoAgentEval score (standalone; in no index) | No data | No data |
| Remote sensing and raster analysis score | 7 | 61 |
| GIS analysis score | 12 | 51 |
| Climate and time series score | 10 | 46 |
| Spatial statistics score | 0 | 31 |
| Making maps score | 2 | 67 |
| Recognising infeasible requests score | 13 | 43 |
| Cost per task | $0.023 | $0.0096 |
| Time per task (at 100 output tokens/s) | 2.1 min | 3.8 min |
| Tokens per attempt | 62k | 98k |
| Turns per attempt | 14.4 | 10.1 |
| Input price per 1M tokens | $0.29 | $0.15 |
| Output price per 1M tokens | $2.25 | $0.50 |
| Context window | 24,000 | 262,144 |
| Image input | No | Yes |
| Developer | Meta | NVIDIA |
Charts
Geospatial Agent Index
Equal average of Earth-Bench V2, GeoBenchX and GS-QA2, 0 to 100 · Higher is better
Data table: Geospatial Agent Index
| Model | Developer | Geospatial Agent Index |
|---|---|---|
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 52 |
| Llama 3.3 70B (Non-reasoning), Terminus-2 | Meta | 8 |
Cost per task
Average cost per task (USD) · Lower is better
Data table: Cost per task
| Model | Developer | Cost per task |
|---|---|---|
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | $0.0096 |
| Llama 3.3 70B (Non-reasoning), Terminus-2 | Meta | $0.023 |
Output tokens per task
Average output tokens per attempt, reasoning included · Lower is better
Data table: Output tokens per task
| Model | Developer | Output tokens per task |
|---|---|---|
| Llama 3.3 70B (Non-reasoning), Terminus-2 | Meta | 2k |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 7k |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2?
Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2, 52 to 8.
Which is cheaper per task, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2?
Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2, $0.0096 to $0.023.
Which writes fewer output tokens per task, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2?
Llama 3.3 70B (Non-reasoning), Terminus-2, 2k to 7k.
Which has the larger context window, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2?
Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2, 262,144 tokens to 24,000 tokens.
More: Llama 3.3 70B (Non-reasoning), Terminus-2 · Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 · All comparisons