Llama 3.3 70B (Non-reasoning) vs Nemotron 3.5 Bolt E4B (Reasoning)
Results updated 9 November 2026 · Both in Terminus-2 · How we benchmark
Verdict
Llama 3.3 70B (Non-reasoning) beats Nemotron 3.5 Bolt E4B (Reasoning) by 4 points, and is faster.
- Cost per task
- $0.023 vs No data
- Time per task
- 2.1 min vs 5.2 min
- Tokens per attempt
- 62k vs 341k
- Image input
- No vs No
Llama 3.3 70B (Non-reasoning), Terminus-2 scores higher than Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2 on all 3 evaluations scored for both.
Where they differ most
By kind of work, share of tasks passed, 0 to 100 · Higher is better
| Kind of work | Llama 3.3 70B (Non-reasoning) | Nemotron 3.5 Bolt E4B (Reasoning) |
|---|---|---|
| GIS analysis | 12 | 3 |
| Remote sensing and raster analysis | 7 | 1 |
| Climate and time series | 12 | 6 |
| Change detection | 4 | 8 |
Where the tasks are set: each kind of work links to its tasks and scoresCharts for each: Llama 3.3 70B (Non-reasoning), Terminus-2 · Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2.
Side by side
| Metric | Llama 3.3 70B (Non-reasoning), Terminus-2 | Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2 |
|---|---|---|
| Geospatial Agent Index: Text only | 8 | 4 |
| Geospatial Agent Index: Text + vision | No image input | No data |
| Earth-Bench V2 score | 9 | 5 |
| GeoBenchX score | 6 | 3 |
| GS-QA2 score | 10 | 3 |
| Vision agents score | No image input | No data |
| GeoAgentBench score (Guided workflows; not in the Agent Index) | 8 | 4 |
| GeoAnalystBench score (Guided workflows; not in the Agent Index) | 0 | 5 |
| OGD4All score (standalone; in no index) | No data | 21 |
| GeoNatureAgent score (standalone; in no index) | No data | 0 |
| MultiGlobeQA score (standalone; in no index) | No data | 16 |
| GeoAgentEval score (standalone; in no index) | No data | 40 |
| ImpactMesh score (standalone; in no index) | No data | No data |
| Remote sensing and raster analysis score | 7 | 1 |
| Change detection score | 4 | 8 |
| GIS analysis score | 12 | 3 |
| Climate and time series score | 12 | 6 |
| Making maps score | 2 | 2 |
| Cost per task | $0.023 | No data |
| Time per task (at 100 output tokens/s) | 2.1 min | 5.2 min |
| Tokens per attempt | 62k | 341k |
| Turns per attempt | 14.4 | 24.4 |
| Input price per 1M tokens | $0.37 | No data |
| Output price per 1M tokens | $0.56 | No data |
| Context window | 24,000 | 65,536 |
| Image input | No | No |
| Developer | Meta | NVIDIA |
Frequently asked questions
Which scores higher on the Geospatial Agent Index, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2?
Llama 3.3 70B (Non-reasoning), Terminus-2, 8 to 4.
Which is cheaper per task, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2?
Not known yet: Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2 has no cost data.
Which writes fewer output tokens per task, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2?
Llama 3.3 70B (Non-reasoning), Terminus-2, 2k to 14k.
Which has the larger context window, Llama 3.3 70B (Non-reasoning), Terminus-2 or Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2?
Nemotron 3.5 Bolt E4B (Reasoning), Terminus-2, 65,536 tokens to 24,000 tokens.