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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

Most intelligent

  1. #1Qwen 3.8 27B (xhigh) Open weights75

Geospatial Intelligence Index, Text only

Fastest

  1. #1Qwen 3.8 27B (xhigh), Qwen Code4.7 min (estimated)
  2. #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

IntelligenceScores 0 to 100: Qwen 3.8 27B (xhigh): 75025507510075Qwen 3.8 27B(xhigh)
Data table: Intelligence
Intelligence
ModelDeveloperIntelligence
Qwen 3.8 27B (xhigh)Alibaba (Qwen)75

Cost per task

USD per agent task · Lower is better

Cost per taskCost per task in US dollars: Qwen 3.8 27B (xhigh), Terminus-2: $0.12; Qwen 3.8 27B (xhigh), Qwen Code: $0.22$0$0.10$0.20$0.30$0.40$0.12Qwen 3.8 27B(xhigh)Terminus-2$0.22Qwen 3.8 27B(xhigh)Qwen Code
Data table: Cost per task
Cost per task
ModelDeveloperCost per task
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)$0.12
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)$0.22

Time per task

Minutes per agent task · Lower is better

Time per taskTime per task: Qwen 3.8 27B (xhigh), Qwen Code: 4.7 min; Qwen 3.8 27B (xhigh), Terminus-2: 7.4 min024684.7 minQwen 3.8 27B(xhigh)Qwen Code7.4 minQwen 3.8 27B(xhigh)Terminus-2
Data table: Time per task
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)4.7 min
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (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

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; Qwen 3.8 27B (xhigh): 750255075100Claude Opus 5.5 (xhigh)89GPT-6.1 Sol (medium)86DeepSeek V4 Pro (max)79Qwen 3.8 27B (xhigh)75
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
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
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

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; Qwen 3.8 27B (xhigh), Qwen Code: 68; Qwen 3.8 27B (xhigh), Terminus-2: 660255075100Claude Opus 5.5 (xhigh)Claude Code85GPT-6.1 Sol (xhigh)Codex82GLM-5.3 (max)Claude Code73Qwen 3.8 27B (xhigh)Qwen Code68Qwen 3.8 27B (xhigh)Terminus-266
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
Geospatial Agent Index: Text only
ModelDeveloperGeospatial Agent Index: Text only
Claude Opus 5.5 (xhigh), Claude CodeAnthropic85
GPT-6.1 Sol (xhigh), CodexOpenAI82
GLM-5.3 (max), Claude CodeZ.ai73
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)68
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (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

Earth-Bench V2Scores 0 to 100: Claude Opus 5.5 (high), Claude Code: 79; GPT-6 Sol (high), Codex: 75; Qwen 3.8 27B (xhigh), Qwen Code: 68; Qwen 3.8 27B (xhigh), Terminus-2: 510255075100Claude Opus 5.5 (high)Claude Code79GPT-6 Sol (high)Codex75Qwen 3.8 27B (xhigh)Qwen Code68Qwen 3.8 27B (xhigh)Terminus-251
Data table: Earth-Bench V2
Earth-Bench V2
ModelDeveloperEarth-Bench V2
Claude Opus 5.5 (high), Claude CodeAnthropic79
GPT-6 Sol (high), CodexOpenAI75
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)68
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)51

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; Qwen 3.8 27B (xhigh), Terminus-2: 67; Qwen 3.8 27B (xhigh), Qwen Code: 620255075100Claude Opus 5.5 (xhigh)Claude Code83GPT-6.1 Sol (medium)Codex79Qwen 3.8 27B (xhigh)Terminus-267Qwen 3.8 27B (xhigh)Qwen Code62
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; Qwen 3.8 27B (xhigh), Terminus-2: 79; Qwen 3.8 27B (xhigh), Qwen Code: 750255075100Claude Opus 5.5 (xhigh)Claude Code94GPT-6.1 Sol (xhigh)Codex94Qwen 3.8 27B (xhigh)Terminus-279Qwen 3.8 27B (xhigh)Qwen Code75
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; Qwen 3.8 27B (xhigh), Qwen Code: 94; Qwen 3.8 27B (xhigh), Terminus-2: 620255075100Claude Opus 5.5 (high)Claude Code100GPT-6.1 Sol (medium)Codex100Qwen 3.8 27B (xhigh)Qwen Code94Qwen 3.8 27B (xhigh)Terminus-262
Data table: GeoAgentBench
GeoAgentBench
ModelDeveloperGeoAgentBench
Claude Opus 5.5 (high), Claude CodeAnthropic100
GPT-6.1 Sol (medium), CodexOpenAI100
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)94
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)62

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; Qwen 3.8 27B (xhigh), Terminus-2: 89; Qwen 3.8 27B (xhigh), Qwen Code: 790255075100DeepSeek V4 Pro (max)Terminus-2100GLM-5.2 (high)Terminus-2100Qwen 3.8 27B (xhigh)Terminus-289Qwen 3.8 27B (xhigh)Qwen Code79
Data table: GeoAnalystBench
GeoAnalystBench
ModelDeveloperGeoAnalystBench
DeepSeek V4 Pro (max), Terminus-2DeepSeek100
GLM-5.2 (high), Terminus-2Z.ai100
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)89
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)79

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; Qwen 3.8 27B (xhigh), Qwen Code: 95; Qwen 3.8 27B (xhigh), Terminus-2: 920255075100Claude Opus 4.8 (high)Claude Code97GLM-5.3 (max)Claude Code96Qwen 3.8 27B (xhigh)Qwen Code95Qwen 3.8 27B (xhigh)Terminus-292
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; Qwen 3.8 27B (xhigh), Qwen Code: 100; Step 5 Preview (Reasoning), Terminus-2: 100; GLM-5.3-Flash (max), Claude Code: 1000255075100Qwen 3.8 27B (xhigh)Terminus-2100Qwen 3.8 27B (xhigh)Qwen Code100Step 5 Preview (Reasoning)Terminus-2100GLM-5.3-Flash (max)Claude Code100
Data table: GeoNatureAgent
GeoNatureAgent
ModelDeveloperGeoNatureAgent
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)100
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)100
Step 5 Preview (Reasoning), Terminus-2StepFun100
GLM-5.3-Flash (max), Claude CodeZ.ai100

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; Qwen 3.8 27B (xhigh), Qwen Code: 74; Qwen 3.8 27B (xhigh), Terminus-2: 710255075100Step 5 Preview (Reasoning)Terminus-283Nemotron 3.5 Super VL 120B (Reasoning)Terminus-282Qwen 3.8 27B (xhigh)Qwen Code74Qwen 3.8 27B (xhigh)Terminus-271
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; Qwen 3.8 27B (xhigh), Terminus-2: 92; Qwen 3.8 27B (xhigh), Qwen Code: 880255075100Claude Opus 5.5 (medium)Claude Code100GLM-5.3-Flash (max)Claude Code96Qwen 3.8 27B (xhigh)Terminus-292Qwen 3.8 27B (xhigh)Qwen Code88
Data table: GeoAgentEval
GeoAgentEval
ModelDeveloperGeoAgentEval
Claude Opus 5.5 (medium), Claude CodeAnthropic100
GLM-5.3-Flash (max), Claude CodeZ.ai96
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)92
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)88

Vision

Earth-Bench V2 images

Questions on images, no tools · 0 to 100 · Higher is better

Earth-Bench V2 imagesScores 0 to 100: GPT-5.6 Sol (high): 76; Claude Opus 5.5 (high): 74; Qwen 3.8 27B (xhigh): 69025507510076GPT-5.6 Sol(high)74Claude Opus5.5 (high)69Qwen 3.8 27B(xhigh)
Data table: Earth-Bench V2 images
Earth-Bench V2 images
ModelDeveloperEarth-Bench V2 images
GPT-5.6 Sol (high)OpenAI76
Claude Opus 5.5 (high)Anthropic74
Qwen 3.8 27B (xhigh)Alibaba (Qwen)69

ThinkGeo images

Questions on images, no tools · 0 to 100 · Higher is better

ThinkGeo imagesScores 0 to 100: Claude Sonnet 5.5 (high): 50; GPT-6 Luna (high): 43; Qwen 3.8 27B (xhigh): 37025507510050Claude Sonnet5.5 (high)43GPT-6 Luna(high)37Qwen 3.8 27B(xhigh)
Data table: ThinkGeo images
ThinkGeo images
ModelDeveloperThinkGeo images
Claude Sonnet 5.5 (high)Anthropic50
GPT-6 Luna (high)OpenAI43
Qwen 3.8 27B (xhigh)Alibaba (Qwen)37

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; Qwen 3.8 27B (xhigh): 91025507510098Claude Sonnet4.6 (high)94Gemini 3.8Flash (low)91Qwen 3.8 27B(xhigh)
Data table: Geospatial knowledge
Geospatial knowledge
ModelDeveloperGeospatial knowledge
Claude Sonnet 4.6 (high)Anthropic98
Gemini 3.8 Flash (low)Google94
Qwen 3.8 27B (xhigh)Alibaba (Qwen)91

Cost

Cost per task

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

Cost per taskCost per task in US dollars: GPT-6.1 Sol (xhigh), Codex: $0.085; Qwen 3.8 27B (xhigh), Terminus-2: $0.12; Qwen 3.8 27B (xhigh), Qwen Code: $0.22; Claude Opus 5.5 (xhigh), Claude Code: $0.25$0$0.10$0.20$0.30$0.40GPT-6.1 Sol (xhigh)Codex$0.085Qwen 3.8 27B (xhigh)Terminus-2$0.12Qwen 3.8 27B (xhigh)Qwen Code$0.22Claude 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 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
Cost per task
ModelDeveloperCost per task
GPT-6.1 Sol (xhigh), CodexOpenAI$0.085
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)$0.12
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)$0.22
Claude Opus 5.5 (xhigh), Claude CodeAnthropic$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
Geospatial Agent Index vs. cost per taskGeospatial Agent Index (0 to 100) against cost per task in US dollars, log scale: Qwen 3.8 27B (xhigh), Terminus-2: 65.76, $0.12; Qwen 3.8 27B (xhigh), Qwen Code: 68.32, $0.22; 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 quadrant60657075808590$0.05$0.10$0.20$0.50$1.00Cost per task (USD, log scale)Geospatial Agent IndexQwen 3.8 27B (xhigh), Qwen CodeQwen 3.8 27B (xhigh), 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
Geospatial Agent Index vs. cost per task
ModelGeospatial Agent IndexCost per task (USD)On the Pareto line
Qwen 3.8 27B (xhigh), Terminus-266$0.12No
Qwen 3.8 27B (xhigh), Qwen Code68$0.22No
Claude Opus 5.5 (xhigh), Claude Code85$0.25Yes
GPT-6.1 Sol (xhigh), Codex82$0.085Yes
GLM-5.3 (max), Claude Code73$0.48No

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; Qwen 3.8 27B (xhigh), Qwen Code: 4.7 min; Qwen 3.8 27B (xhigh), Terminus-2: 7.4 min02468GPT-6.1 Sol (xhigh)Codex0.8 minClaude Opus 5.5 (xhigh)Claude Code1.4 minQwen 3.8 27B (xhigh)Qwen Code4.7 minQwen 3.8 27B (xhigh)Terminus-27.4 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 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
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
GPT-6.1 Sol (xhigh), CodexOpenAI0.8 min
Claude Opus 5.5 (xhigh), Claude CodeAnthropic1.4 min
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)4.7 min
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (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
Geospatial Agent Index vs. time per taskGeospatial Agent Index (0 to 100) against time per task (minutes), log scale: Qwen 3.8 27B (xhigh), Terminus-2: 65.76, 7.4 min; Qwen 3.8 27B (xhigh), Qwen Code: 68.32, 4.7 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 quadrant606570758085900.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexQwen 3.8 27B (xhigh), Qwen CodeQwen 3.8 27B (xhigh), 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
Qwen 3.8 27B (xhigh), Terminus-2667.4 minNo
Qwen 3.8 27B (xhigh), Qwen Code684.7 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-QA2GeoAgentBenchGeoAnalystBenchOGD4AllGeoNatureAgentMultiGlobeQAGeoAgentEvalEarth-Bench V2 imagesThinkGeo imagesGeospatial knowledge
Qwen 3.8 27B (xhigh), Qwen CodeRanked6862759479951007488693791
Qwen 3.8 27B (xhigh), Terminus-2Ranked5167796289921007192693791

Frequently asked questions

What Alibaba (Qwen) models do you benchmark?
We benchmark 1 Alibaba (Qwen) model: Qwen 3.8 27B.
Which is the most intelligent Alibaba (Qwen) model for geospatial work?
Qwen 3.8 27B (xhigh), with 75.1 on the Geospatial Intelligence Index (Text only), #37 of 61.
What is the cheapest Alibaba (Qwen) model per task?
Qwen 3.8 27B (xhigh), Terminus-2, at $0.12 per agent task at list prices, #52 of 74 on the Geospatial Agent Index.
What is the fastest Alibaba (Qwen) model?
Qwen 3.8 27B (xhigh), Qwen Code, at 4.7 min (estimated) per agent task at 100 output tokens per second, #55 of 74.
Does Alibaba (Qwen) offer open-weights models?
Yes: Qwen 3.8 27B. Their weights are published, so you can run them on your own hardware.
Which Alibaba (Qwen) models can read satellite imagery?
Qwen 3.8 27B takes images as input. The best on Vision is Qwen 3.8 27B (xhigh), with 53.0.
How do Alibaba (Qwen) models do on Earth observation, GIS and geospatial knowledge?
Which Alibaba (Qwen) model is best for agent work, and in which harness?
Qwen 3.8 27B (xhigh), Qwen Code, with 68.3 on the Geospatial Agent Index, #44 of 74, in the Qwen Code harness.
How does Alibaba (Qwen)'s best model compare with the overall leader?
Qwen 3.8 27B (xhigh) scores 75.1 on the Geospatial Intelligence Index (Text only), 13.5 points behind the leader, Anthropic's Claude Opus 5.5 (xhigh), with 88.7.
Are any Alibaba (Qwen) models still being benchmarked?
No. Every Alibaba (Qwen) model listed has finished its benchmark and is ranked.
How should I choose between Alibaba (Qwen) models for geospatial work?
Start from the job. For the strongest answers, Qwen 3.8 27B (xhigh); for agent work with data and code, Qwen 3.8 27B (xhigh), Qwen Code; for the lowest cost, Qwen 3.8 27B (xhigh), Terminus-2; for reading imagery, Qwen 3.8 27B (xhigh); to run it yourself, Qwen 3.8 27B. Each model page shows its scores on every evaluation.