NVIDIA: geospatial agent results
Helps you choose the best NVIDIA model for geospatial work: GIS and Earth observation agent tasks, vision and knowledge, with cost and time.
Overview
Most intelligent
- #1Nemotron 3 Ultra (high) Open weights74
- #2Nemotron 3.5 Super VL 120B (Reasoning) Open weights59
- #3Nemotron 3 Nano Omni 30B (Reasoning) Open weights32
- #4Nemotron 3.5 Lightning 30B (Reasoning) Open weights27
Geospatial Intelligence Index, Text only
Lowest cost
- #1Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2$0.0096
- #2Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2$0.013
- #3Nemotron 3 Ultra (high), Terminus-2$0.067
- #4Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2$0.13
Cost per agent task, at list prices
Fastest
- #1Nemotron 3 Ultra (high), Terminus-22.4 min
- #2Nemotron 3.5 Super VL 120B (Reasoning), Terminus-23.8 min
- #3Nemotron 3 Nano Omni 30B (Reasoning), Terminus-24.6 min
- #4Nemotron 3.5 Lightning 30B (Reasoning), Terminus-24.8 min
Time per agent task, at 100 output tokens/s
- For intelligence, the top NVIDIA models are Nemotron 3 Ultra (high) (74), Nemotron 3.5 Super VL 120B (Reasoning) (59) and Nemotron 3 Nano Omni 30B (Reasoning) (32) on the Geospatial Intelligence Index (Text only).
- For cost, the cheapest per agent task are Nemotron 3.5 Super VL 120B (Reasoning) ($0.0096), Nemotron 3.5 Lightning 30B (Reasoning) ($0.013) and Nemotron 3 Ultra (high) ($0.067).
- For speed, the fastest per agent task are Nemotron 3 Ultra (high) (2.4 min), Nemotron 3.5 Super VL 120B (Reasoning) (3.8 min) and Nemotron 3 Nano Omni 30B (Reasoning) (4.6 min).
- For context window, the largest are Nemotron 3 Ultra: 262K tokens, Nemotron 3.5 Lightning 30B: 262K tokens and Nemotron 3.5 Super VL 120B: 262K tokens.
Intelligence
Geospatial Intelligence Index, Text only · Higher is better
Data table: Intelligence
| Model | Developer | Intelligence |
|---|---|---|
| Nemotron 3 Ultra (high) | NVIDIA | 74 |
| Nemotron 3.5 Super VL 120B (Reasoning) | NVIDIA | 59 |
| Nemotron 3 Nano Omni 30B (Reasoning) | NVIDIA | 32 |
| Nemotron 3.5 Lightning 30B (Reasoning) | NVIDIA | 27 |
Cost per task
USD per agent task · 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 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | $0.013 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | $0.067 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | $0.13 |
Time per task
Minutes per agent task · Lower is better
Data table: Time per task
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 2.4 min |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 3.8 min |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 4.6 min |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 4.8 min |
Models
Nemotron 3 Ultra
Open weightsText only
262K token context · Released 4 June 2026
$0.50 input · $2.20 output per 1M tokens
Nemotron 3.5 Super VL 120B
Open weightsReads images
262K token context · Released 15 October 2026
$0.15 input · $0.50 output per 1M tokens
Nemotron 3 Nano Omni 30B
Open weightsReads images
256K token context · Released 28 April 2026
$0.20 input · $0.80 output per 1M tokens
Nemotron 3.5 Lightning 30B
Open weightsText only
262K token context · Released 11 August 2026
$0.070 input · $0.20 output per 1M tokens
Intelligence
Text only
Geospatial Intelligence Index: Text only
Weighted average of agent tasks, guided workflows and geospatial knowledge, 0 to 100 · Higher is better
How to read this chart
What this metric means. A weighted average of group scores, each 0 to 100: Agent tasks 4/6, Guided workflows 1/6, Geospatial knowledge 1/6.
Bars. Every NVIDIA model with a result, highlighted, and the strongest models from other labs, faded, for context. Provisional: not yet ranked.
Data table: Geospatial Intelligence Index: Text only
| Model | Developer | Geospatial Intelligence Index: Text only |
|---|---|---|
| Claude Opus 5.5 (xhigh) | Anthropic | 89 |
| GPT-6.1 Sol (medium) | OpenAI | 86 |
| DeepSeek V4 Pro (max) | DeepSeek | 79 |
| Nemotron 3 Ultra (high) | NVIDIA | 74 |
| Nemotron 3.5 Super VL 120B (Reasoning) | NVIDIA | 59 |
| Nemotron 3 Nano Omni 30B (Reasoning) | NVIDIA | 32 |
| Nemotron 3.5 Lightning 30B (Reasoning) | NVIDIA | 27 |
Text + vision
No NVIDIA model has a Text + vision score yet: that view also needs Vision agents and Vision results.
Agent tasks
Text only
Geospatial Agent Index: Text only
Equal average of Earth-Bench V2, GeoBenchX and GS-QA2, 0 to 100 · Higher is better
How to read this chart
Bars. Every NVIDIA model with a result, highlighted, and the strongest models from other labs, faded, for context. Provisional: not yet ranked.
Data table: Geospatial Agent Index: Text only
| Model | Developer | Geospatial Agent Index: Text only |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 85 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 82 |
| GLM-5.3 (max), Claude Code | Z.ai | 73 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 68 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 52 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 21 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 13 |
Text + vision
No NVIDIA model has a Text + vision score yet: that view also needs Vision agents and Vision results.
Earth-Bench V2
Earth observation data analysis · 0 to 100 · Higher is better
Data table: Earth-Bench V2
| Model | Developer | Earth-Bench V2 |
|---|---|---|
| Claude Opus 5.5 (high), Claude Code | Anthropic | 79 |
| GPT-6 Sol (high), Codex | OpenAI | 75 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 69 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 46 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 31 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 16 |
GeoBenchX
Maps, numbers and lists from country and city data; some tasks should be declined · 0 to 100 · Higher is better
Data table: GeoBenchX
| Model | Developer | GeoBenchX |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 83 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 79 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 65 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 53 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 23 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 20 |
GS-QA2
Spatial data reasoning over map and elevation data · 0 to 100 · Higher is better
Data table: GS-QA2
| Model | Developer | GS-QA2 |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 94 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 94 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 70 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 59 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 9 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 4 |
GeoAgentBench
Guided workflows: multi-step spatial execution · 0 to 100 · Higher is better
Data table: GeoAgentBench
| Model | Developer | GeoAgentBench |
|---|---|---|
| Claude Opus 5.5 (high), Claude Code | Anthropic | 100 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 100 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 96 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 68 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 28 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 10 |
GeoAnalystBench
Guided workflows: spatial workflow and code generation · 0 to 100 · Higher is better
Data table: GeoAnalystBench
| Model | Developer | GeoAnalystBench |
|---|---|---|
| DeepSeek V4 Pro (max), Terminus-2 | DeepSeek | 100 |
| GLM-5.2 (high), Terminus-2 | Z.ai | 100 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 63 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 37 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 32 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 16 |
OGD4All
Not in an index: questions on a city's open government data · 0 to 100 · Higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Claude Opus 4.8 (high), Claude Code | Anthropic | 97 |
| GLM-5.3 (max), Claude Code | Z.ai | 96 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 90 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 85 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 49 |
GeoNatureAgent
Not in an index: environmental raster analysis over named regions · 0 to 100 · Higher is better
Data table: GeoNatureAgent
| Model | Developer | GeoNatureAgent |
|---|---|---|
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 100 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 100 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 89 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 64 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 0 |
MultiGlobeQA
Not in an index: spatial reasoning over facts about places worldwide · 0 to 100 · Higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 83 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 82 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 79 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 72 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 68 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 49 |
GeoAgentEval
Not in an index: building, auditing and repairing a PostGIS database · 0 to 100 · Higher is better
Data table: GeoAgentEval
| Model | Developer | GeoAgentEval |
|---|---|---|
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 100 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 96 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 80 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 48 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 36 |
Knowledge
Geospatial knowledge
Questions on geospatial knowledge · 0 to 100 · Higher is better
Data table: Geospatial knowledge
| Model | Developer | Geospatial knowledge |
|---|---|---|
| Claude Sonnet 4.6 (high) | Anthropic | 98 |
| Gemini 3.8 Flash (low) | 94 | |
| Nemotron 3.5 Super VL 120B (Reasoning) | NVIDIA | 92 |
| Nemotron 3 Ultra (high) | NVIDIA | 91 |
| Nemotron 3.5 Lightning 30B (Reasoning) | NVIDIA | 90 |
| Nemotron 3 Nano Omni 30B (Reasoning) | NVIDIA | 85 |
Cost
Cost per task
Average cost per agent task (USD) · Lower is better
How to read this chart
What cost is measuring. What one attempt at a task would cost if paid token by token at the provider's published API prices, averaged over attempts: ordinary input tokens, input tokens served from the provider's cache at the lower cached price, tokens written to the cache where the provider charges for that, and output tokens (thinking included). Cost assumes each harness uses the model's pay-per-token API, priced at public list rates; sandboxes and our own engineering time are not included. Failed attempts count too.
How to read this chart. Lower is better. Costs are pay-per-token list prices, not any discount or credit a customer might have.
Bars. Every NVIDIA entry with a cost, highlighted, and the top-ranked entries from other labs on the Geospatial Agent Index, faded, for context. Provisional: not yet ranked.
Data table: Cost per task
| Model | Developer | Cost per task |
|---|---|---|
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | $0.0096 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | $0.013 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | $0.067 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | $0.085 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | $0.13 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | $0.25 |
Geospatial Agent Index vs. cost per task
Geospatial Agent Index, Text only; cost per task (USD) · Higher and further left is better
- Most attractive quadrant
- Pareto line
How to read this chart
Why a log scale. Costs and times differ by a factor of ten or more between models, so equal steps on the axis are equal ratios.
What cost is measuring. What one attempt at a task would cost if paid token by token at the provider's published API prices, averaged over attempts: ordinary input tokens, input tokens served from the provider's cache at the lower cached price, tokens written to the cache where the provider charges for that, and output tokens (thinking included). Cost assumes each harness uses the model's pay-per-token API, priced at public list rates; sandboxes and our own engineering time are not included. Failed attempts count too.
How to read this chart. Each point is one entry. Further left means a lower average cost per task (log scale); higher means a higher Geospatial Agent Index. Both axes fit the models shown, so the score axis does not start at zero. The shaded most attractive quadrant is the top-left quarter of the plot, split at the middle of each axis: a reading aid that moves with the axes when you change the models shown, not a fixed bar. The dotted Pareto line joins the shown models that no cheaper shown model beats on score.
Data table: Geospatial Agent Index vs. cost per task
| Model | Geospatial Agent Index | Cost per task (USD) | On the Pareto line |
|---|---|---|---|
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | 21 | $0.13 | No |
| Nemotron 3 Ultra (high), Terminus-2 | 68 | $0.067 | Yes |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | 13 | $0.013 | No |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | 52 | $0.0096 | Yes |
| Claude Opus 5.5 (xhigh), Claude Code | 85 | $0.25 | Yes |
| GPT-6.1 Sol (xhigh), Codex | 82 | $0.085 | Yes |
| GLM-5.3 (max), Claude Code | 73 | $0.48 | No |
Time
Time per task
Average minutes per agent task, at 100 output tokens/s · Lower is better
How to read this chart
What this metric means. Time as if every model wrote 100 output tokens per second: output tokens divided by 100, plus the time the agent's commands took. Serving speed differs between providers and is not ranked.
Bars. Every NVIDIA entry with a cost, highlighted, and the top-ranked entries from other labs on the Geospatial Agent Index, faded, for context. Provisional: not yet ranked.
Data table: Time per task
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 0.8 min |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 1.4 min |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 2.4 min |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 3.8 min |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 4.6 min |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 4.8 min |
Geospatial Agent Index vs. time per task
Geospatial Agent Index, Text only; time per task (minutes) · Higher and further left is better
- Most attractive quadrant
- Pareto line
How to read this chart
Why a log scale. Costs and times differ by a factor of ten or more between models, so equal steps on the axis are equal ratios.
What this metric means. Time as if every model wrote 100 output tokens per second: output tokens divided by 100, plus the time the agent's commands took. Serving speed differs between providers and is not ranked.
How to read this chart. Each point is one entry. Further left means a lower average time per task (log scale); higher means a higher Geospatial Agent Index. Both axes fit the models shown, so the score axis does not start at zero. The shaded most attractive quadrant is the top-left quarter of the plot, split at the middle of each axis: a reading aid that moves with the axes when you change the models shown, not a fixed bar. The dotted Pareto line joins the shown models that no faster shown model beats on score.
Data table: Geospatial Agent Index vs. time per task
| Model | Geospatial Agent Index | Time per task (minutes) | On the Pareto line |
|---|---|---|---|
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | 21 | 4.6 min | No |
| Nemotron 3 Ultra (high), Terminus-2 | 68 | 2.4 min | No |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | 13 | 4.8 min | No |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | 52 | 3.8 min | No |
| Claude Opus 5.5 (xhigh), Claude Code | 85 | 1.4 min | Yes |
| GPT-6.1 Sol (xhigh), Codex | 82 | 0.8 min | Yes |
| GLM-5.3 (max), Claude Code | 73 | 4.3 min | No |
Every benchmark
Score by benchmark
Share answered correctly, 0 to 100 · Darker is better; hatched: provisional, evaluation not finished
Data table: Score by benchmark
| Model | Status | Earth-Bench V2 | GeoBenchX | GS-QA2 | GeoAgentBench | GeoAnalystBench | OGD4All | GeoNatureAgent | MultiGlobeQA | GeoAgentEval | Geospatial knowledge |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nemotron 3 Ultra (high), Terminus-2 | Ranked | 69 | 65 | 70 | 96 | 63 | 92 | 92 | 72 | 92 | 91 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | Ranked | 46 | 53 | 59 | 68 | 37 | 85 | 89 | 82 | 80 | 92 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | Ranked | 31 | 23 | 9 | 28 | 16 | 49 | 0 | 49 | 48 | 85 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | Ranked | 16 | 20 | 4 | 10 | 32 | 90 | 64 | 68 | 36 | 90 |
