Alibaba (Qwen): geospatial agent results
Helps you choose the best Alibaba (Qwen) model for geospatial work: GIS and Earth observation agent tasks, vision and knowledge, with cost and time.
Overview
Lowest cost
Cost per agent task, at list prices
Fastest
- #1Qwen 3.8 27B (xhigh), Qwen Code4.7 min (estimated)
- #2Qwen 3.8 27B (xhigh), Terminus-27.4 min
Time per agent task, at 100 output tokens/s
- For intelligence, the top Alibaba (Qwen) models are Qwen 3.8 27B (xhigh) (75) on the Geospatial Intelligence Index (Text only).
- For cost, the cheapest per agent task are Qwen 3.8 27B (xhigh) ($0.12) and Qwen 3.8 27B (xhigh) ($0.22).
- For speed, the fastest per agent task are Qwen 3.8 27B (xhigh) (4.7 min (estimated)) and Qwen 3.8 27B (xhigh) (7.4 min).
- For context window, the largest are Qwen 3.8 27B: 262K tokens.
Intelligence
Geospatial Intelligence Index, Text only · Higher is better
Data table: Intelligence
| Model | Developer | Intelligence |
|---|---|---|
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 75 |
Cost per task
USD per agent task · Lower is better
Data table: Cost per task
| Model | Developer | Cost per task |
|---|---|---|
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | $0.12 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | $0.22 |
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) |
|---|---|---|
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 4.7 min |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 7.4 min |
Models
Qwen 3.8 27B
Open weightsReads images
262K token context · Released 14 August 2026
$0.45 input · $3.20 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 Alibaba (Qwen) 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 |
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 75 |
Text + vision
No Alibaba (Qwen) 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 Alibaba (Qwen) 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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 68 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 66 |
Text + vision
No Alibaba (Qwen) 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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 68 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 51 |
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 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 67 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 62 |
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 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 79 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 75 |
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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 94 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 62 |
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 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 89 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 79 |
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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 95 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 92 |
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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 100 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 100 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 100 |
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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 74 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 71 |
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 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 92 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 88 |
Vision
Earth-Bench V2 images
Questions on images, no tools · 0 to 100 · Higher is better
Data table: Earth-Bench V2 images
| Model | Developer | Earth-Bench V2 images |
|---|---|---|
| GPT-5.6 Sol (high) | OpenAI | 76 |
| Claude Opus 5.5 (high) | Anthropic | 74 |
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 69 |
ThinkGeo images
Questions on images, no tools · 0 to 100 · Higher is better
Data table: ThinkGeo images
| Model | Developer | ThinkGeo images |
|---|---|---|
| Claude Sonnet 5.5 (high) | Anthropic | 50 |
| GPT-6 Luna (high) | OpenAI | 43 |
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 37 |
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 | |
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 91 |
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 Alibaba (Qwen) 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 |
|---|---|---|
| GPT-6.1 Sol (xhigh), Codex | OpenAI | $0.085 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | $0.12 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | $0.22 |
| 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 |
|---|---|---|---|
| Qwen 3.8 27B (xhigh), Terminus-2 | 66 | $0.12 | No |
| Qwen 3.8 27B (xhigh), Qwen Code | 68 | $0.22 | No |
| 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 Alibaba (Qwen) 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 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 4.7 min |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 7.4 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 |
|---|---|---|---|
| Qwen 3.8 27B (xhigh), Terminus-2 | 66 | 7.4 min | No |
| Qwen 3.8 27B (xhigh), Qwen Code | 68 | 4.7 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 | Earth-Bench V2 images | ThinkGeo images | Geospatial knowledge |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Qwen 3.8 27B (xhigh), Qwen Code | Ranked | 68 | 62 | 75 | 94 | 79 | 95 | 100 | 74 | 88 | 69 | 37 | 91 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Ranked | 51 | 67 | 79 | 62 | 89 | 92 | 100 | 71 | 92 | 69 | 37 | 91 |
