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Qwen Code: geospatial agent results

The command-line coding agent made by Alibaba (Qwen). It reads the task, writes and runs code in the sandbox, and passes the results back to the model. An agent is a harness plus a model: this page ranks every model we benchmark in Qwen Code on GIS and Earth observation tasks, and shows how the same model does in other harnesses.

Made by Alibaba (Qwen) · Version 0.26.0, pinned · 1 model from 1 lab · 1 entry, 1 ranked

Overview

Lowest cost

  1. #1Qwen 3.8 27B (Reasoning)$0.22

Cost per agent task, at list prices

Fastest

  1. #1Qwen 3.8 27B (Reasoning)4.7 min (estimated)

Time per agent task, at 100 output tokens/s

Models

Geospatial Agent Index: every model in Qwen Code

Text only, 0 to 100 · Higher is better · one bar per model, at its best ranked reasoning level

Geospatial Agent Index: every model in Qwen CodeScores 0 to 100: Kimi K2.6 (Reasoning), Kimi Code: 70; Claude Haiku 5.5 (low), Claude Code: 70; Kimi K2.6 (Reasoning), Terminus-2: 70; DeepSeek V4 Flash (max), Terminus-2: 69; Qwen 3.8 27B (Reasoning), Qwen Code: 69; GPT-5.6 Terra (high), Codex: 69; Claude Sonnet 5.5 (low), Claude Code: 690255075100Kimi K2.6 (Reasoning)Kimi Code70Claude Haiku 5.5 (low)Claude Code70Kimi K2.6 (Reasoning)Terminus-270DeepSeek V4 Flash (max)Terminus-269Qwen 3.8 27B (Reasoning)Qwen Code69GPT-5.6 Terra (high)Codex69Claude Sonnet 5.5 (low)Claude Code69
How to read this chart

Bars. Every entry in Qwen Code with a result, highlighted, and the nearest entries in other harnesses, faded, for context. Provisional: not yet ranked.

Data table: Geospatial Agent Index: every model in Qwen Code
Geospatial Agent Index: every model in Qwen Code
ModelDeveloperGeospatial Agent Index: every model in Qwen Code
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI70
Claude Haiku 5.5 (low), Claude CodeAnthropic70
Kimi K2.6 (Reasoning), Terminus-2Moonshot AI70
DeepSeek V4 Flash (max), Terminus-2DeepSeek69
Qwen 3.8 27B (Reasoning), Qwen CodeAlibaba (Qwen)69
GPT-5.6 Terra (high), CodexOpenAI69
Claude Sonnet 5.5 (low), Claude CodeAnthropic69

Same model elsewhere

Not yet: every model in Qwen Code runs only in Qwen Code so far. The Geospatial Agent Index ranks every entry, in every harness.

By evaluation

Earth-Bench V2

higher is better

Earth-Bench V2Scores 0 to 100: DeepSeek V4 Flash (high), Codex: 70; GLM-5.3-Flash (max), Claude Code: 69; Qwen 3.8 27B (Reasoning), Qwen Code: 69; DeepSeek V4 Pro (max), Terminus-2: 69; Nemotron 3 Ultra (high), Terminus-2: 69; Claude Opus 4.7 (high), Claude Code: 69; DeepSeek V4 Pro (high), Terminus-2: 68DeepSeek V4 Flash (high) · Codex70GLM-5.3-Flash (max) · Claude Code69Qwen 3.8 27B (Reasoning) · Qwen Code69DeepSeek V4 Pro (max) · Terminus-269Nemotron 3 Ultra (high) · Terminus-269Claude Opus 4.7 (high) · Claude Code69DeepSeek V4 Pro (high) · Terminus-268
Data table: Earth-Bench V2

GeoBenchX

higher is better

GeoBenchXScores 0 to 100: GLM-5.3 (low), Terminus-2: 64; Kimi K2.6 (Reasoning), Terminus-2: 64; Kimi K3 (Reasoning), Terminus-2: 64; Qwen 3.8 27B (Reasoning), Qwen Code: 63; Gemma 4 26B A4B (Reasoning), Terminus-2: 63; Kimi K2.7 Code (Reasoning), Terminus-2: 63; GPT-5.6 Luna (medium), Codex: 63GLM-5.3 (low) · Terminus-264Kimi K2.6 (Reasoning) · Terminus-264Kimi K3 (Reasoning) · Terminus-264Qwen 3.8 27B (Reasoning) · Qwen Code63Gemma 4 26B A4B (Reasoning) · Terminus-263Kimi K2.7 Code (Reasoning) · Terminus-263GPT-5.6 Luna (medium) · Codex63
Data table: GeoBenchX

GS-QA2

higher is better

GS-QA2Scores 0 to 100: GLM-5.3-Flash (max), Claude Code: 76; GPT-5.6 Luna (low), Codex: 76; GLM-5.3-Flash (low), Terminus-2: 76; Claude Sonnet 5.5 (low), Claude Code: 76; Qwen 3.8 27B (Reasoning), Qwen Code: 75; Claude Haiku 5.5 (low), Claude Code: 75; DeepSeek V4 Flash (high), Codex: 74GLM-5.3-Flash (max) · Claude Code76GPT-5.6 Luna (low) · Codex76GLM-5.3-Flash (low) · Terminus-276Claude Sonnet 5.5 (low) · Claude Code76Qwen 3.8 27B (Reasoning) · Qwen Code75Claude Haiku 5.5 (low) · Claude Code75DeepSeek V4 Flash (high) · Codex74
Data table: GS-QA2

GeoAgentBench

higher is better

GeoAgentBenchScores 0 to 100: Qwen 3.8 27B (Reasoning), Qwen Code: 94; DeepSeek V4 Flash (high), Terminus-2: 94; GLM-5.2 (high), Terminus-2: 94; GLM-5.2 (max), Terminus-2: 94; GLM-5.3-Flash (low), Terminus-2: 94; GLM-5.3 (max), Terminus-2: 94; Kimi K3 (Reasoning), Terminus-2: 94Qwen 3.8 27B (Reasoning) · Qwen Code94DeepSeek V4 Flash (high) · Terminus-294GLM-5.2 (high) · Terminus-294GLM-5.2 (max) · Terminus-294GLM-5.3-Flash (low) · Terminus-294GLM-5.3 (max) · Terminus-294Kimi K3 (Reasoning) · Terminus-294
Data table: GeoAgentBench

GeoAnalystBench

higher is better

GeoAnalystBenchScores 0 to 100: GLM-5.3-Flash (max), Terminus-2: 84; GLM-5.3 (max), Terminus-2: 84; MiMo V2.6 Flash (Reasoning), MiMo Code: 84; Qwen 3.8 27B (Reasoning), Qwen Code: 79; Kimi K2.6 (Reasoning), Terminus-2: 79; MiniMax M3 (Reasoning), MiniMax Code: 79; GPT-6 Luna (low), Codex: 79GLM-5.3-Flash (max) · Terminus-284GLM-5.3 (max) · Terminus-284MiMo V2.6 Flash (Reasoning) · MiMo Code84Qwen 3.8 27B (Reasoning) · Qwen Code79Kimi K2.6 (Reasoning) · Terminus-279MiniMax M3 (Reasoning) · MiniMax Code79GPT-6 Luna (low) · Codex79
Data table: GeoAnalystBench

OGD4All

higher is better

OGD4AllScores 0 to 100: Qwen 3.8 27B (Reasoning), Qwen Code: 97; GLM-5.3-Flash (max), Claude Code: 97; Claude Opus 5.5 (medium), Claude Code: 97; Claude Sonnet 5.5 (xhigh), Claude Code: 97; Claude Haiku 5.5 (xhigh), Claude Code: 97; Claude Haiku 5.5 (max), Claude Code: 97; GLM-5.3 (max), Claude Code: 96Qwen 3.8 27B (Reasoning) · Qwen Code97GLM-5.3-Flash (max) · Claude Code97Claude Opus 5.5 (medium) · Claude Code97Claude Sonnet 5.5 (xhigh) · Claude Code97Claude Haiku 5.5 (xhigh) · Claude Code97Claude Haiku 5.5 (max) · Claude Code97GLM-5.3 (max) · Claude Code96
Data table: OGD4All

GeoNatureAgent

higher is better

GeoNatureAgentScores 0 to 100: Qwen 3.8 27B (Reasoning), Qwen Code: 100; MiniMax M3.1 Flash (max), Terminus-2: 100; Mistral Large 4 (high), Terminus-2: 100; Muse Glimmer 30B (high), Terminus-2: 100; Qwen 3.8 27B (xhigh), Terminus-2: 100; DeepSeek V4.1 Flash (max), Claude Code: 100; GLM-5.3-Flash (max), Claude Code: 100Qwen 3.8 27B (Reasoning) · Qwen Code100MiniMax M3.1 Flash (max) · Terminus-2100Mistral Large 4 (high) · Terminus-2100Muse Glimmer 30B (high) · Terminus-2100Qwen 3.8 27B (xhigh) · Terminus-2100DeepSeek V4.1 Flash (max) · Claude Code100GLM-5.3-Flash (max) · Claude Code100
Data table: GeoNatureAgent

MultiGlobeQA

higher is better

MultiGlobeQAScores 0 to 100: Claude Sonnet 5.5 (low), Claude Code: 75; Claude Haiku 5.5 (high), Claude Code: 74; Qwen 3.8 27B (Reasoning), Qwen Code: 74; Qwen 3.8 27B (xhigh), Terminus-2: 74; GLM-5.3-Flash (max), Claude Code: 74; Claude Sonnet 5.5 (medium), Claude Code: 74; Claude Sonnet 5 (high), Claude Code: 73Claude Sonnet 5.5 (low) · Claude Code75Claude Haiku 5.5 (high) · Claude Code74Qwen 3.8 27B (Reasoning) · Qwen Code74Qwen 3.8 27B (xhigh) · Terminus-274GLM-5.3-Flash (max) · Claude Code74Claude Sonnet 5.5 (medium) · Claude Code74Claude Sonnet 5 (high) · Claude Code73
Data table: MultiGlobeQA

GeoAgentEval

higher is better

GeoAgentEvalScores 0 to 100: Mistral Large 4 (high), Terminus-2: 92; Muse Glimmer 30B (high), Terminus-2: 92; Nemotron 3 Ultra (high), Terminus-2: 92; Step 5 Preview (Reasoning), Terminus-2: 91; Qwen 3.8 27B (Reasoning), Qwen Code: 88; Claude Haiku 4.5 (Reasoning), Claude Code: 88; GLM-5.3 (max), Claude Code: 88Mistral Large 4 (high) · Terminus-292Muse Glimmer 30B (high) · Terminus-292Nemotron 3 Ultra (high) · Terminus-292Step 5 Preview (Reasoning) · Terminus-291Qwen 3.8 27B (Reasoning) · Qwen Code88Claude Haiku 4.5 (Reasoning) · Claude Code88GLM-5.3 (max) · Claude Code88
Data table: GeoAgentEval

Cost

Cost per task

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

Cost per taskCost per task in US dollars: Claude Sonnet 5 (high), Claude Code: $0.20; DeepSeek V4.1 Flash (max), Claude Code: $0.20; GPT-5.6 Sol (high), Codex: $0.21; GLM-5.2 (high), Terminus-2: $0.22; Qwen 3.8 27B (Reasoning), Qwen Code: $0.22; Kimi K3 (Reasoning), Terminus-2: $0.23; Claude Opus 5.5 (xhigh), Claude Code: $0.25$0$0.10$0.20$0.30$0.40Claude Sonnet 5 (high)Claude Code$0.20DeepSeek V4.1 Flash (max)Claude Code$0.20GPT-5.6 Sol (high)Codex$0.21GLM-5.2 (high)Terminus-2$0.22Qwen 3.8 27B (Reasoning)Qwen Code$0.22Kimi K3 (Reasoning)Terminus-2$0.23Claude 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 entry in Qwen Code with a result, highlighted, and the nearest entries in other harnesses, 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: Qwen 3.8 27B (Reasoning), Qwen Code: 69.33, $0.22; GPT-5.6 Terra (high), Codex: 69.32, $0.10; DeepSeek V4 Flash (max), Terminus-2: 69.4, $0.062; Claude Sonnet 5.5 (low), Claude Code: 69.25, $0.043; Kimi K2.6 (Reasoning), Terminus-2: 69.57, $0.089; Claude Haiku 5.5 (low), Claude Code: 69.85, $0.0053; Kimi K2.6 (Reasoning), Kimi Code: 70.04, $0.27Most attractive quadrant69.169.269.369.469.569.669.769.869.97070.170.2$0.002$0.005$0.01$0.02$0.05$0.10$0.20$0.50Cost per task (USD, log scale)Geospatial Agent IndexQwen 3.8 27B (Reasoning), Qwen CodeKimi K2.6 (Reasoning), Kimi CodeClaude Haiku 5.5 (low), Claude CodeKimi K2.6 (Reasoning), Terminus-2DeepSeek V4 Flash (max), Terminus-2GPT-5.6 Terra (high), CodexClaude Sonnet 5.5 (low), 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 (Reasoning), Qwen Code69$0.22No
GPT-5.6 Terra (high), Codex69$0.10No
DeepSeek V4 Flash (max), Terminus-269$0.062No
Claude Sonnet 5.5 (low), Claude Code69$0.043No
Kimi K2.6 (Reasoning), Terminus-270$0.089No
Claude Haiku 5.5 (low), Claude Code70$0.0053Yes
Kimi K2.6 (Reasoning), Kimi Code70$0.27Yes

Time

Time per task

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

Time per taskTime per task: Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: 4.6 min; Qwen 3.8 27B (Reasoning), Qwen Code: 4.7 min; GLM-5.2 (high), Terminus-2: 4.7 min; gpt-oss-20b (high), Codex: 4.8 min; Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2: 4.8 min; MiniMax M3 (Reasoning), MiniMax Code: 4.9 min; DeepSeek V4 Pro (max), Terminus-2: 4.9 min01.534.56Nemotron 3 Nano Omni 30B (Reasoning)Terminus-24.6 minQwen 3.8 27B (Reasoning)Qwen Code4.7 minGLM-5.2 (high)Terminus-24.7 mingpt-oss-20b (high)Codex4.8 minNemotron 3.5 Lightning 30B (Reasoning)Terminus-24.8 minMiniMax M3 (Reasoning)MiniMax Code4.9 minDeepSeek V4 Pro (max)Terminus-24.9 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 entry in Qwen Code with a result, highlighted, and the nearest entries in other harnesses, 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: Qwen 3.8 27B (Reasoning), Qwen Code: 69.33, 4.7 min; GPT-5.6 Terra (high), Codex: 69.32, 0.8 min; DeepSeek V4 Flash (max), Terminus-2: 69.4, 5.7 min; Claude Sonnet 5.5 (low), Claude Code: 69.25, 0.4 min; Kimi K2.6 (Reasoning), Terminus-2: 69.57, 3.5 min; Claude Haiku 5.5 (low), Claude Code: 69.85, 0.9 min; Kimi K2.6 (Reasoning), Kimi Code: 70.04, 3.0 minMost attractive quadrant69.169.269.369.469.569.669.769.869.97070.170.20.2 min0.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexQwen 3.8 27B (Reasoning), Qwen CodeKimi K2.6 (Reasoning), Kimi CodeClaude Haiku 5.5 (low), Claude CodeGPT-5.6 Terra (high), CodexClaude Sonnet 5.5 (low), Claude CodeKimi K2.6 (Reasoning), Terminus-2DeepSeek V4 Flash (max), Terminus-2
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 (Reasoning), Qwen Code694.7 minNo
GPT-5.6 Terra (high), Codex690.8 minYes
DeepSeek V4 Flash (max), Terminus-2695.7 minNo
Claude Sonnet 5.5 (low), Claude Code690.4 minYes
Kimi K2.6 (Reasoning), Terminus-2703.5 minNo
Claude Haiku 5.5 (low), Claude Code700.9 minYes
Kimi K2.6 (Reasoning), Kimi Code703.0 minYes

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 (Reasoning), Qwen CodeRanked6963759479971007488693791

About Qwen Code

What it is
The command-line coding agent made by Alibaba (Qwen). It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Version
0.26.0, the same pinned build for every run
Models we run in it
1 from Alibaba (Qwen)
How we run it
The same tasks, limits and checks as every other harness: only the agent program changes. Methodology.

Frequently asked questions

Which models do you benchmark in Qwen Code?
Which model is best in Qwen Code?
Qwen 3.8 27B (Reasoning), with 69.3 on the Geospatial Agent Index, 47th of 83 entries.
What is the cheapest model in Qwen Code?
Qwen 3.8 27B (Reasoning), at $0.22 per agent task at list prices.
What is the fastest model in Qwen Code?
Qwen 3.8 27B (Reasoning), at 4.7 min (estimated) per agent task.
Is Qwen Code better than other harnesses?
We cannot say yet: every model in Qwen Code runs only in Qwen Code so far, so there is no like-for-like comparison. The Geospatial Agent Index ranks every entry, in every harness.
Who makes Qwen Code?
Alibaba (Qwen). The command-line coding agent made by Alibaba (Qwen). It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Which version of Qwen Code do you run?
0.26.0, pinned: every run uses the same build.
Are any models in Qwen Code still being benchmarked?
No. Every entry in Qwen Code has finished its benchmark and is ranked.