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
- 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
Data table: Cost per task (USD)
| Model | Developer | Cost per task (USD) |
|---|---|---|
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | $0.054 |
Time per task (min)
lower is better
Data table: Time per task (min)
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 17.3 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
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
| Model | Developer | Geospatial Agent Index: Text only |
|---|---|---|
| Claude Opus 5.5 (max), Claude Code | Anthropic | 86 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 82 |
| DeepSeek V4.1 Flash (max), Codex | DeepSeek | 76 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 37 |
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
Data table: Earth-Bench V2
| Model | Developer | Earth-Bench V2 |
|---|---|---|
| Claude Opus 5.5 (max), Claude Code | Anthropic | 80 |
| DeepSeek V4.1 Flash (max), Claude Code | DeepSeek | 77 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 44 |
GeoBenchX
higher is better
Data table: GeoBenchX
| Model | Developer | GeoBenchX |
|---|---|---|
| Claude Opus 5.5 (max), Claude Code | Anthropic | 84 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 79 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 33 |
GS-QA2
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 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 35 |
GeoAgentBench
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 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 63 |
GeoAnalystBench
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 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 63 |
OGD4All
higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 97 |
| Claude Opus 5.5 (max), Claude Code | Anthropic | 97 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 65 |
GeoNatureAgent
higher is better
Data table: GeoNatureAgent
| Model | Developer | GeoNatureAgent |
|---|---|---|
| Gemma 4 31B (Reasoning), Terminus-2 | 100 | |
| MiniMax M3.1 Flash (max), Terminus-2 | MiniMax | 100 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 36 |
MultiGlobeQA
higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Claude Opus 5.5 (max), Claude Code | Anthropic | 80 |
| DeepSeek V4.1 Flash (max), Claude Code | DeepSeek | 78 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 59 |
GeoAgentEval
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 |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 60 |
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 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
| Model | Developer | Cost per task |
|---|---|---|
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | $0.054 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | $0.085 |
| Claude Opus 5.5 (max), Claude Code | Anthropic | $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
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 |
|---|---|---|---|
| Laguna XS 2.1 (Reasoning), Terminus-2 | 37 | $0.054 | Yes |
| Claude Opus 5.5 (max), Claude Code | 86 | $1.14 | Yes |
| GPT-6.1 Sol (xhigh), Codex | 82 | $0.085 | Yes |
| DeepSeek V4.1 Flash (max), Codex | 76 | $0.077 | Yes |
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 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
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 0.8 min |
| Claude Opus 5.5 (max), Claude Code | Anthropic | 5.8 min |
| Laguna XS 2.1 (Reasoning), Terminus-2 | Poolside | 17.3 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
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 |
|---|---|---|---|
| Laguna XS 2.1 (Reasoning), Terminus-2 | 37 | 17.3 min | No |
| Claude Opus 5.5 (max), Claude Code | 86 | 5.8 min | Yes |
| GPT-6.1 Sol (xhigh), Codex | 82 | 0.8 min | Yes |
| DeepSeek V4.1 Flash (max), Codex | 76 | 9.5 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| Laguna XS 2.1 (Reasoning), Terminus-2 | Ranked | 44 | 33 | 35 | 63 | 63 | 65 | 36 | 59 | 60 |
