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Poolside: geospatial agent results

Helps you choose the best Poolside model for geospatial work: GIS and Earth observation agent tasks, vision and knowledge, with cost and time.

Overview

Most intelligent

Not yet ranked

Geospatial Intelligence Index, Text only

Lowest cost

  1. #1Laguna XS 2.1 (Reasoning), Terminus-2$0.054

Cost per agent task, at list prices

Fastest

  1. #1Laguna XS 2.1 (Reasoning), Terminus-217.3 min

Time per agent task, at 100 output tokens/s

  • For cost, the cheapest per agent task are Laguna XS 2.1 (Reasoning) ($0.054).
  • For speed, the fastest per agent task are Laguna XS 2.1 (Reasoning) (17.3 min).
  • For context window, the largest are Laguna XS 2.1: 262K tokens.

Cost per task (USD)

lower is better

Cost per task (USD)Cost per task (USD) in US dollars: Laguna XS 2.1 (Reasoning), Terminus-2: $0.054Laguna XS 2.1 (Reasoning) · Terminus-2$0.054
Data table: Cost per task (USD)
Cost per task (USD)
ModelDeveloperCost per task (USD)
Laguna XS 2.1 (Reasoning), Terminus-2Poolside$0.054

Time per task (min)

lower is better

Time per task (min)Time per task (min): Laguna XS 2.1 (Reasoning), Terminus-2: 17.3 minLaguna XS 2.1 (Reasoning) · Terminus-217.3 min
Data table: Time per task (min)
Time per task (min)
ModelDeveloperTime per task (at 100 output tokens/s)
Laguna XS 2.1 (Reasoning), Terminus-2Poolside17.3 min

Models

Laguna XS 2.1

Open weightsText only

262K token context · Released 2 July 2026

$0.10 input · $0.20 output per 1M tokens

Intelligence

Text only

No Poolside model is ranked on the Geospatial Intelligence Index yet.

Text + vision

No Poolside model has a Text + vision score yet: that view also needs Vision agents and Vision results.

Provisional, so not on these charts: Laguna XS 2.1 (Reasoning). Their finished evaluations are in the charts below.

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 (max), Claude Code: 86; GPT-6.1 Sol (xhigh), Codex: 82; DeepSeek V4.1 Flash (max), Codex: 76; Laguna XS 2.1 (Reasoning), Terminus-2: 370255075100Claude Opus 5.5 (max)Claude Code86GPT-6.1 Sol (xhigh)Codex82DeepSeek V4.1 Flash (max)Codex76Laguna XS 2.1 (Reasoning)Terminus-237
How to read this chart

Bars. Every Poolside 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
Geospatial Agent Index: Text only
ModelDeveloperGeospatial Agent Index: Text only
Claude Opus 5.5 (max), Claude CodeAnthropic86
GPT-6.1 Sol (xhigh), CodexOpenAI82
DeepSeek V4.1 Flash (max), CodexDeepSeek76
Laguna XS 2.1 (Reasoning), Terminus-2Poolside37

Text + vision

No Poolside model has a Text + vision score yet: that view also needs Vision agents and Vision results.

Earth-Bench V2

higher is better

Earth-Bench V2Scores 0 to 100: Claude Opus 5.5 (max), Claude Code: 80; DeepSeek V4.1 Flash (max), Claude Code: 77; Laguna XS 2.1 (Reasoning), Terminus-2: 44Claude Opus 5.5 (max) · Claude Code80DeepSeek V4.1 Flash (max) · Claude Code77Laguna XS 2.1 (Reasoning) · Terminus-244
Data table: Earth-Bench V2
Earth-Bench V2
ModelDeveloperEarth-Bench V2
Claude Opus 5.5 (max), Claude CodeAnthropic80
DeepSeek V4.1 Flash (max), Claude CodeDeepSeek77
Laguna XS 2.1 (Reasoning), Terminus-2Poolside44

GeoBenchX

higher is better

GeoBenchXScores 0 to 100: Claude Opus 5.5 (max), Claude Code: 84; GPT-6.1 Sol (medium), Codex: 79; Laguna XS 2.1 (Reasoning), Terminus-2: 33Claude Opus 5.5 (max) · Claude Code84GPT-6.1 Sol (medium) · Codex79Laguna XS 2.1 (Reasoning) · Terminus-233
Data table: GeoBenchX

GS-QA2

higher is better

GS-QA2Scores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 94; GPT-6.1 Sol (xhigh), Codex: 94; Laguna XS 2.1 (Reasoning), Terminus-2: 35Claude Opus 5.5 (xhigh) · Claude Code94GPT-6.1 Sol (xhigh) · Codex94Laguna XS 2.1 (Reasoning) · Terminus-235
Data table: GS-QA2

GeoAgentBench

higher is better

GeoAgentBenchScores 0 to 100: Claude Opus 5.5 (high), Claude Code: 100; GPT-6.1 Sol (medium), Codex: 100; Laguna XS 2.1 (Reasoning), Terminus-2: 63Claude Opus 5.5 (high) · Claude Code100GPT-6.1 Sol (medium) · Codex100Laguna XS 2.1 (Reasoning) · Terminus-263
Data table: GeoAgentBench
GeoAgentBench
ModelDeveloperGeoAgentBench
Claude Opus 5.5 (high), Claude CodeAnthropic100
GPT-6.1 Sol (medium), CodexOpenAI100
Laguna XS 2.1 (Reasoning), Terminus-2Poolside63

GeoAnalystBench

higher is better

GeoAnalystBenchScores 0 to 100: DeepSeek V4 Pro (max), Terminus-2: 100; GLM-5.2 (high), Terminus-2: 100; Laguna XS 2.1 (Reasoning), Terminus-2: 63DeepSeek V4 Pro (max) · Terminus-2100GLM-5.2 (high) · Terminus-2100Laguna XS 2.1 (Reasoning) · Terminus-263
Data table: GeoAnalystBench
GeoAnalystBench
ModelDeveloperGeoAnalystBench
DeepSeek V4 Pro (max), Terminus-2DeepSeek100
GLM-5.2 (high), Terminus-2Z.ai100
Laguna XS 2.1 (Reasoning), Terminus-2Poolside63

OGD4All

higher is better

OGD4AllScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 97; Claude Opus 5.5 (max), Claude Code: 97; Laguna XS 2.1 (Reasoning), Terminus-2: 65Kimi K3 (Reasoning) · Kimi Code97Claude Opus 5.5 (max) · Claude Code97Laguna XS 2.1 (Reasoning) · Terminus-265
Data table: OGD4All

GeoNatureAgent

higher is better

GeoNatureAgentScores 0 to 100: Gemma 4 31B (Reasoning), Terminus-2: 100; MiniMax M3.1 Flash (max), Terminus-2: 100; Laguna XS 2.1 (Reasoning), Terminus-2: 36Gemma 4 31B (Reasoning) · Terminus-2100MiniMax M3.1 Flash (max) · Terminus-2100Laguna XS 2.1 (Reasoning) · Terminus-236
Data table: GeoNatureAgent

MultiGlobeQA

higher is better

MultiGlobeQAScores 0 to 100: Claude Opus 5.5 (max), Claude Code: 80; DeepSeek V4.1 Flash (max), Claude Code: 78; Laguna XS 2.1 (Reasoning), Terminus-2: 59Claude Opus 5.5 (max) · Claude Code80DeepSeek V4.1 Flash (max) · Claude Code78Laguna XS 2.1 (Reasoning) · Terminus-259
Data table: MultiGlobeQA

GeoAgentEval

higher is better

GeoAgentEvalScores 0 to 100: Claude Opus 5.5 (medium), Claude Code: 100; GLM-5.3-Flash (max), Claude Code: 96; Laguna XS 2.1 (Reasoning), Terminus-2: 60Claude Opus 5.5 (medium) · Claude Code100GLM-5.3-Flash (max) · Claude Code96Laguna XS 2.1 (Reasoning) · Terminus-260
Data table: GeoAgentEval

Cost

Cost per task

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

Cost per taskCost per task in US dollars: Laguna XS 2.1 (Reasoning), Terminus-2: $0.054; GPT-6.1 Sol (xhigh), Codex: $0.085; Claude Opus 5.5 (max), Claude Code: $1.14$0$0.50$1.00$1.50$2.00$0.054Laguna XS 2.1(Reasoning)Terminus-2$0.085GPT-6.1 Sol(xhigh)Codex$1.14Claude Opus5.5 (max)Claude Code
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 Poolside 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
Cost per task
ModelDeveloperCost per task
Laguna XS 2.1 (Reasoning), Terminus-2Poolside$0.054
GPT-6.1 Sol (xhigh), CodexOpenAI$0.085
Claude Opus 5.5 (max), Claude CodeAnthropic$1.14

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: Laguna XS 2.1 (Reasoning), Terminus-2: 37.23, $0.054; Claude Opus 5.5 (max), Claude Code: 85.79, $1.14; GPT-6.1 Sol (xhigh), Codex: 81.82, $0.085; DeepSeek V4.1 Flash (max), Codex: 76.39, $0.077Most attractive quadrant30405060708090100$0.02$0.05$0.10$0.20$0.50$1.00$2.00Cost per task (USD, log scale)Geospatial Agent IndexLaguna XS 2.1 (Reasoning), Terminus-2Claude Opus 5.5 (max), Claude CodeGPT-6.1 Sol (xhigh), CodexDeepSeek V4.1 Flash (max), Codex
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
Geospatial Agent Index vs. cost per task
ModelGeospatial Agent IndexCost per task (USD)On the Pareto line
Laguna XS 2.1 (Reasoning), Terminus-237$0.054Yes
Claude Opus 5.5 (max), Claude Code86$1.14Yes
GPT-6.1 Sol (xhigh), Codex82$0.085Yes
DeepSeek V4.1 Flash (max), Codex76$0.077Yes

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 (max), Claude Code: 5.8 min; Laguna XS 2.1 (Reasoning), Terminus-2: 17.3 min051015200.8 minGPT-6.1 Sol(xhigh)Codex5.8 minClaude Opus5.5 (max)Claude Code17.3 minLaguna XS 2.1(Reasoning)Terminus-2
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 Poolside 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
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
GPT-6.1 Sol (xhigh), CodexOpenAI0.8 min
Claude Opus 5.5 (max), Claude CodeAnthropic5.8 min
Laguna XS 2.1 (Reasoning), Terminus-2Poolside17.3 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
Geospatial Agent Index vs. time per taskGeospatial Agent Index (0 to 100) against time per task (minutes), log scale: Laguna XS 2.1 (Reasoning), Terminus-2: 37.23, 17.3 min; Claude Opus 5.5 (max), Claude Code: 85.79, 5.8 min; GPT-6.1 Sol (xhigh), Codex: 81.82, 0.8 min; DeepSeek V4.1 Flash (max), Codex: 76.39, 9.5 minMost attractive quadrant304050607080901000.5 min1 min2 min5 min10 min20 minTime per task (minutes, log scale)Geospatial Agent IndexLaguna XS 2.1 (Reasoning), Terminus-2Claude Opus 5.5 (max), Claude CodeGPT-6.1 Sol (xhigh), CodexDeepSeek V4.1 Flash (max), Codex
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
Laguna XS 2.1 (Reasoning), Terminus-23717.3 minNo
Claude Opus 5.5 (max), Claude Code865.8 minYes
GPT-6.1 Sol (xhigh), Codex820.8 minYes
DeepSeek V4.1 Flash (max), Codex769.5 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-QA2GeoAgentBenchGeoAnalystBenchOGD4AllGeoNatureAgentMultiGlobeQAGeoAgentEval
Laguna XS 2.1 (Reasoning), Terminus-2Ranked443335636365365960

Frequently asked questions

What Poolside models do you benchmark?
We benchmark 1 Poolside model: Laguna XS 2.1.
What is the cheapest Poolside model per task?
Laguna XS 2.1 (Reasoning), Terminus-2, at $0.054 per agent task at list prices, #33 of 89 on the Geospatial Agent Index.
What is the fastest Poolside model?
Laguna XS 2.1 (Reasoning), Terminus-2, at 17.3 min per agent task at 100 output tokens per second, #89 of 89.
Does Poolside offer open-weights models?
Yes: Laguna XS 2.1. Their weights are published, so you can run them on your own hardware.
Which Poolside models can read satellite imagery?
No Poolside model we benchmark takes images as input. They can still work with satellite data through code.
How do Poolside models do on Earth observation, GIS and geospatial knowledge?
Which Poolside model is best for agent work, and in which harness?
Laguna XS 2.1 (Reasoning), Terminus-2, with 37.2 on the Geospatial Agent Index, #81 of 89, in the Terminus-2 harness.
Are any Poolside models still being benchmarked?
No. Every Poolside model listed has finished its benchmark and is ranked.
How should I choose between Poolside models for geospatial work?
Start from the work. For agent work with data and code, Laguna XS 2.1 (Reasoning), Terminus-2; for the lowest cost, Laguna XS 2.1 (Reasoning), Terminus-2; to run it yourself, Laguna XS 2.1. Each model page shows its scores on every evaluation.