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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

  • 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

IntelligenceScores 0 to 100: Nemotron 3 Ultra (high): 74; Nemotron 3.5 Super VL 120B (Reasoning): 59; Nemotron 3 Nano Omni 30B (Reasoning): 32; Nemotron 3.5 Lightning 30B (Reasoning): 270255075100Nemotron 3 Ultra (high)74Nemotron 3.5 Super VL 120B (Reasoning)59Nemotron 3 Nano Omni 30B (Reasoning)32Nemotron 3.5 Lightning 30B (Reasoning)27
Data table: Intelligence

Cost per task

USD per agent task · Lower is better

Cost per taskCost per task in US dollars: Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: $0.0096; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: $0.013; Nemotron 3 Ultra (high), Terminus-2: $0.067; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: $0.13$0$0.05$0.10$0.15$0.20Nemotron 3.5 Super VL 120B (Reasoning)Terminus-2$0.0096Nemotron 3.5 Lightning 30B (Reasoning)Terminus-2$0.013Nemotron 3 Ultra (high)Terminus-2$0.067Nemotron 3 Nano Omni 30B (Reasoning)Terminus-2$0.13
Data table: Cost per task

Time per task

Minutes per agent task · Lower is better

Time per taskTime per task: Nemotron 3 Ultra (high), Terminus-2: 2.4 min; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 3.8 min; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 4.6 min; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 4.8 min01.534.56Nemotron 3 Ultra (high)Terminus-22.4 minNemotron 3.5 Super VL 120B (Reasoning)Terminus-23.8 minNemotron 3 Nano Omni 30B (Reasoning)Terminus-24.6 minNemotron 3.5 Lightning 30B (Reasoning)Terminus-24.8 min
Data table: Time per task

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

Geospatial Intelligence Index: Text onlyScores 0 to 100: Claude Opus 5.5 (xhigh): 89; GPT-6.1 Sol (medium): 86; DeepSeek V4 Pro (max): 79; Nemotron 3 Ultra (high): 74; Nemotron 3.5 Super VL 120B (Reasoning): 59; Nemotron 3 Nano Omni 30B (Reasoning): 32; Nemotron 3.5 Lightning 30B (Reasoning): 270255075100Claude Opus 5.5 (xhigh)89GPT-6.1 Sol (medium)86DeepSeek V4 Pro (max)79Nemotron 3 Ultra (high)74Nemotron 3.5 Super VL 120B (Reasoning)59Nemotron 3 Nano Omni 30B (Reasoning)32Nemotron 3.5 Lightning 30B (Reasoning)27
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
Geospatial Intelligence Index: Text only
ModelDeveloperGeospatial Intelligence Index: Text only
Claude Opus 5.5 (xhigh)Anthropic89
GPT-6.1 Sol (medium)OpenAI86
DeepSeek V4 Pro (max)DeepSeek79
Nemotron 3 Ultra (high)NVIDIA74
Nemotron 3.5 Super VL 120B (Reasoning)NVIDIA59
Nemotron 3 Nano Omni 30B (Reasoning)NVIDIA32
Nemotron 3.5 Lightning 30B (Reasoning)NVIDIA27

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

Geospatial Agent Index: Text onlyScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 85; GPT-6.1 Sol (xhigh), Codex: 82; GLM-5.3 (max), Claude Code: 73; Nemotron 3 Ultra (high), Terminus-2: 68; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 52; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 21; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 130255075100Claude Opus 5.5 (xhigh)Claude Code85GPT-6.1 Sol (xhigh)Codex82GLM-5.3 (max)Claude Code73Nemotron 3 Ultra (high)Terminus-268Nemotron 3.5 Super VL 120B (Reasoning)Terminus-252Nemotron 3 Nano Omni 30B (Reasoning)Terminus-221Nemotron 3.5 Lightning 30B (Reasoning)Terminus-213
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

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

Earth-Bench V2Scores 0 to 100: Claude Opus 5.5 (high), Claude Code: 79; GPT-6 Sol (high), Codex: 75; Nemotron 3 Ultra (high), Terminus-2: 69; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 46; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 31; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 160255075100Claude Opus 5.5 (high)Claude Code79GPT-6 Sol (high)Codex75Nemotron 3 Ultra (high)Terminus-269Nemotron 3.5 Super VL 120B (Reasoning)Terminus-246Nemotron 3 Nano Omni 30B (Reasoning)Terminus-231Nemotron 3.5 Lightning 30B (Reasoning)Terminus-216
Data table: Earth-Bench V2

GeoBenchX

Maps, numbers and lists from country and city data; some tasks should be declined · 0 to 100 · Higher is better

GeoBenchXScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 83; GPT-6.1 Sol (medium), Codex: 79; Nemotron 3 Ultra (high), Terminus-2: 65; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 53; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 23; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 200255075100Claude Opus 5.5 (xhigh)Claude Code83GPT-6.1 Sol (medium)Codex79Nemotron 3 Ultra (high)Terminus-265Nemotron 3.5 Super VL 120B (Reasoning)Terminus-253Nemotron 3 Nano Omni 30B (Reasoning)Terminus-223Nemotron 3.5 Lightning 30B (Reasoning)Terminus-220
Data table: GeoBenchX

GS-QA2

Spatial data reasoning over map and elevation data · 0 to 100 · Higher is better

GS-QA2Scores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 94; GPT-6.1 Sol (xhigh), Codex: 94; Nemotron 3 Ultra (high), Terminus-2: 70; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 59; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 9; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 40255075100Claude Opus 5.5 (xhigh)Claude Code94GPT-6.1 Sol (xhigh)Codex94Nemotron 3 Ultra (high)Terminus-270Nemotron 3.5 Super VL 120B (Reasoning)Terminus-259Nemotron 3 Nano Omni 30B (Reasoning)Terminus-29Nemotron 3.5 Lightning 30B (Reasoning)Terminus-24
Data table: GS-QA2

GeoAgentBench

Guided workflows: multi-step spatial execution · 0 to 100 · Higher is better

GeoAgentBenchScores 0 to 100: Claude Opus 5.5 (high), Claude Code: 100; GPT-6.1 Sol (medium), Codex: 100; Nemotron 3 Ultra (high), Terminus-2: 96; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 68; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 28; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 100255075100Claude Opus 5.5 (high)Claude Code100GPT-6.1 Sol (medium)Codex100Nemotron 3 Ultra (high)Terminus-296Nemotron 3.5 Super VL 120B (Reasoning)Terminus-268Nemotron 3 Nano Omni 30B (Reasoning)Terminus-228Nemotron 3.5 Lightning 30B (Reasoning)Terminus-210
Data table: GeoAgentBench

GeoAnalystBench

Guided workflows: spatial workflow and code generation · 0 to 100 · Higher is better

GeoAnalystBenchScores 0 to 100: DeepSeek V4 Pro (max), Terminus-2: 100; GLM-5.2 (high), Terminus-2: 100; Nemotron 3 Ultra (high), Terminus-2: 63; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 37; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 32; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 160255075100DeepSeek V4 Pro (max)Terminus-2100GLM-5.2 (high)Terminus-2100Nemotron 3 Ultra (high)Terminus-263Nemotron 3.5 Super VL 120B (Reasoning)Terminus-237Nemotron 3.5 Lightning 30B (Reasoning)Terminus-232Nemotron 3 Nano Omni 30B (Reasoning)Terminus-216
Data table: GeoAnalystBench

OGD4All

Not in an index: questions on a city's open government data · 0 to 100 · Higher is better

OGD4AllScores 0 to 100: Claude Opus 4.8 (high), Claude Code: 97; GLM-5.3 (max), Claude Code: 96; Nemotron 3 Ultra (high), Terminus-2: 92; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 90; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 85; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 490255075100Claude Opus 4.8 (high)Claude Code97GLM-5.3 (max)Claude Code96Nemotron 3 Ultra (high)Terminus-292Nemotron 3.5 Lightning 30B (Reasoning)Terminus-290Nemotron 3.5 Super VL 120B (Reasoning)Terminus-285Nemotron 3 Nano Omni 30B (Reasoning)Terminus-249
Data table: OGD4All

GeoNatureAgent

Not in an index: environmental raster analysis over named regions · 0 to 100 · Higher is better

GeoNatureAgentScores 0 to 100: Qwen 3.8 27B (xhigh), Terminus-2: 100; Step 5 Preview (Reasoning), Terminus-2: 100; Nemotron 3 Ultra (high), Terminus-2: 92; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 89; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 64; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 00255075100Qwen 3.8 27B (xhigh)Terminus-2100Step 5 Preview (Reasoning)Terminus-2100Nemotron 3 Ultra (high)Terminus-292Nemotron 3.5 Super VL 120B (Reasoning)Terminus-289Nemotron 3.5 Lightning 30B (Reasoning)Terminus-264Nemotron 3 Nano Omni 30B (Reasoning)Terminus-20
Data table: GeoNatureAgent

MultiGlobeQA

Not in an index: spatial reasoning over facts about places worldwide · 0 to 100 · Higher is better

MultiGlobeQAScores 0 to 100: Step 5 Preview (Reasoning), Terminus-2: 83; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 82; Claude Sonnet 5.5 (xhigh), Claude Code: 79; Nemotron 3 Ultra (high), Terminus-2: 72; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 68; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 490255075100Step 5 Preview (Reasoning)Terminus-283Nemotron 3.5 Super VL 120B (Reasoning)Terminus-282Claude Sonnet 5.5 (xhigh)Claude Code79Nemotron 3 Ultra (high)Terminus-272Nemotron 3.5 Lightning 30B (Reasoning)Terminus-268Nemotron 3 Nano Omni 30B (Reasoning)Terminus-249
Data table: MultiGlobeQA

GeoAgentEval

Not in an index: building, auditing and repairing a PostGIS database · 0 to 100 · Higher is better

GeoAgentEvalScores 0 to 100: Claude Opus 5.5 (medium), Claude Code: 100; GLM-5.3-Flash (max), Claude Code: 96; Nemotron 3 Ultra (high), Terminus-2: 92; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 80; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 48; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 360255075100Claude Opus 5.5 (medium)Claude Code100GLM-5.3-Flash (max)Claude Code96Nemotron 3 Ultra (high)Terminus-292Nemotron 3.5 Super VL 120B (Reasoning)Terminus-280Nemotron 3 Nano Omni 30B (Reasoning)Terminus-248Nemotron 3.5 Lightning 30B (Reasoning)Terminus-236
Data table: GeoAgentEval

Knowledge

Geospatial knowledge

Questions on geospatial knowledge · 0 to 100 · Higher is better

Geospatial knowledgeScores 0 to 100: Claude Sonnet 4.6 (high): 98; Gemini 3.8 Flash (low): 94; Nemotron 3.5 Super VL 120B (Reasoning): 92; Nemotron 3 Ultra (high): 91; Nemotron 3.5 Lightning 30B (Reasoning): 90; Nemotron 3 Nano Omni 30B (Reasoning): 850255075100Claude Sonnet 4.6 (high)98Gemini 3.8 Flash (low)94Nemotron 3.5 Super VL 120B (Reasoning)92Nemotron 3 Ultra (high)91Nemotron 3.5 Lightning 30B (Reasoning)90Nemotron 3 Nano Omni 30B (Reasoning)85
Data table: Geospatial knowledge

Cost

Cost per task

Average cost per agent task (USD) · Lower is better

Cost per taskCost per task in US dollars: Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: $0.0096; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: $0.013; Nemotron 3 Ultra (high), Terminus-2: $0.067; GPT-6.1 Sol (xhigh), Codex: $0.085; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: $0.13; Claude Opus 5.5 (xhigh), Claude Code: $0.25$0$0.10$0.20$0.30$0.40Nemotron 3.5 Super VL 120B (Reasoning)Terminus-2$0.0096Nemotron 3.5 Lightning 30B (Reasoning)Terminus-2$0.013Nemotron 3 Ultra (high)Terminus-2$0.067GPT-6.1 Sol (xhigh)Codex$0.085Nemotron 3 Nano Omni 30B (Reasoning)Terminus-2$0.13Claude Opus 5.5 (xhigh)Claude Code$0.25
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

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
Geospatial Agent Index vs. cost per taskGeospatial Agent Index (0 to 100) against cost per task in US dollars, log scale: Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 21.13, $0.13; Nemotron 3 Ultra (high), Terminus-2: 67.93, $0.067; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 13.44, $0.013; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 52.45, $0.0096; Claude Opus 5.5 (xhigh), Claude Code: 84.65, $0.25; GPT-6.1 Sol (xhigh), Codex: 81.82, $0.085; GLM-5.3 (max), Claude Code: 73.4, $0.48Most attractive quadrant0102030405060708090100$0.005$0.01$0.02$0.05$0.10$0.20$0.50$1.00Cost per task (USD, log scale)Geospatial Agent IndexNemotron 3 Ultra (high), Terminus-2Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2Claude Opus 5.5 (xhigh), Claude CodeGPT-6.1 Sol (xhigh), CodexGLM-5.3 (max), Claude Code
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

Time

Time per task

Average minutes per agent task, at 100 output tokens/s · Lower is better

Time per taskTime per task: GPT-6.1 Sol (xhigh), Codex: 0.8 min; Claude Opus 5.5 (xhigh), Claude Code: 1.4 min; Nemotron 3 Ultra (high), Terminus-2: 2.4 min; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 3.8 min; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 4.6 min; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 4.8 min01.534.56GPT-6.1 Sol (xhigh)Codex0.8 minClaude Opus 5.5 (xhigh)Claude Code1.4 minNemotron 3 Ultra (high)Terminus-22.4 minNemotron 3.5 Super VL 120B (Reasoning)Terminus-23.8 minNemotron 3 Nano Omni 30B (Reasoning)Terminus-24.6 minNemotron 3.5 Lightning 30B (Reasoning)Terminus-24.8 min
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

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
Geospatial Agent Index vs. time per taskGeospatial Agent Index (0 to 100) against time per task (minutes), log scale: Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 21.13, 4.6 min; Nemotron 3 Ultra (high), Terminus-2: 67.93, 2.4 min; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 13.44, 4.8 min; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 52.45, 3.8 min; Claude Opus 5.5 (xhigh), Claude Code: 84.65, 1.4 min; GPT-6.1 Sol (xhigh), Codex: 81.82, 0.8 min; GLM-5.3 (max), Claude Code: 73.4, 4.3 minMost attractive quadrant01020304050607080901000.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexNemotron 3 Ultra (high), Terminus-2Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2Claude Opus 5.5 (xhigh), Claude CodeGPT-6.1 Sol (xhigh), CodexGLM-5.3 (max), Claude Code
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
Geospatial Agent Index vs. time per task
ModelGeospatial Agent IndexTime per task (minutes)On the Pareto line
Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2214.6 minNo
Nemotron 3 Ultra (high), Terminus-2682.4 minNo
Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2134.8 minNo
Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2523.8 minNo
Claude Opus 5.5 (xhigh), Claude Code851.4 minYes
GPT-6.1 Sol (xhigh), Codex820.8 minYes
GLM-5.3 (max), Claude Code734.3 minNo

Every benchmark

Score by benchmark

Share answered correctly, 0 to 100 · Darker is better; hatched: provisional, evaluation not finished

Data table: Score by benchmark
Every benchmark
ModelStatusEarth-Bench V2GeoBenchXGS-QA2GeoAgentBenchGeoAnalystBenchOGD4AllGeoNatureAgentMultiGlobeQAGeoAgentEvalGeospatial knowledge
Nemotron 3 Ultra (high), Terminus-2Ranked69657096639292729291
Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2Ranked46535968378589828092
Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2Ranked312392816490494885
Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2Ranked1620410329064683690

Frequently asked questions

What NVIDIA models do you benchmark?
Which is the most intelligent NVIDIA model for geospatial work?
Nemotron 3 Ultra (high), with 73.7 on the Geospatial Intelligence Index (Text only), #39 of 61.
What is the cheapest NVIDIA model per task?
Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2, at $0.0096 per agent task at list prices, #12 of 74 on the Geospatial Agent Index.
What is the fastest NVIDIA model?
Nemotron 3 Ultra (high), Terminus-2, at 2.4 min per agent task at 100 output tokens per second, #39 of 74.
Does NVIDIA offer open-weights models?
Yes: Nemotron 3 Ultra, Nemotron 3.5 Super VL 120B, Nemotron 3 Nano Omni 30B and Nemotron 3.5 Lightning 30B. Their weights are published, so you can run them on your own hardware.
Which NVIDIA models can read satellite imagery?
How do NVIDIA models do on Earth observation, GIS and geospatial knowledge?
Which NVIDIA model is best for agent work, and in which harness?
Nemotron 3 Ultra (high), Terminus-2, with 67.9 on the Geospatial Agent Index, #45 of 74, in the Terminus-2 harness.
How does NVIDIA's best model compare with the overall leader?
Nemotron 3 Ultra (high) scores 73.7 on the Geospatial Intelligence Index (Text only), 15.0 points behind the leader, Anthropic's Claude Opus 5.5 (xhigh), with 88.7.
Are any NVIDIA models still being benchmarked?
No. Every NVIDIA model listed has finished its benchmark and is ranked.
How should I choose between NVIDIA models for geospatial work?
Start from the job. For the strongest answers, Nemotron 3 Ultra (high); for agent work with data and code, Nemotron 3 Ultra (high), Terminus-2; for the lowest cost, Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2; to run it yourself, Nemotron 3 Ultra. Each model page shows its scores on every evaluation.