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Kimi Code: geospatial agent results

The command-line coding agent made by Moonshot AI. 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 Kimi Code on GIS and Earth observation tasks, and shows how the same model does in other harnesses.

Made by Moonshot AI · Version 2.1.1, pinned · 3 models from 1 lab · 3 entries, 3 ranked

Overview

Lowest cost

  1. #1Kimi K2.7 Code (Reasoning)$0.18
  2. #2Kimi K2.6 (Reasoning)$0.27
  3. #3Kimi K3 (Reasoning)$0.32

Cost per agent task, at list prices

Fastest

  1. #1Kimi K3 (Reasoning)1.5 min (estimated)
  2. #2Kimi K2.7 Code (Reasoning)1.9 min (estimated)
  3. #3Kimi K2.6 (Reasoning)3.0 min (estimated)

Time per agent task, at 100 output tokens/s

  • In Kimi Code, the best entry is Kimi K3 (Reasoning), 19th of 83 entries on the Geospatial Agent Index, at 75.1.
  • 3 of the models in Kimi Code also run in another harness at the same reasoning level. Against the best other harness, Kimi Code scores within noise for 3.

Models

Geospatial Agent Index: every model in Kimi 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 Kimi CodeScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 75; Kimi K2.7 Code (Reasoning), Kimi Code: 72; Kimi K2.6 (Reasoning), Kimi Code: 70025507510075Kimi K3(Reasoning)Kimi Code72Kimi K2.7Code(Reasoning)Kimi Code70Kimi K2.6(Reasoning)Kimi Code
How to read this chart

Bars. Every entry in Kimi Code with a result, highlighted. Provisional: not yet ranked.

Data table: Geospatial Agent Index: every model in Kimi Code
Geospatial Agent Index: every model in Kimi Code
ModelDeveloperGeospatial Agent Index: every model in Kimi Code
Kimi K3 (Reasoning), Kimi CodeMoonshot AI75
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI72
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI70

Same model elsewhere

The models we benchmark in Kimi Code and in another harness, at the same reasoning level: an agent is a harness plus a model, so the gap between the bars is the harness. Kimi Code is highlighted; gaps under about 3 points are within noise.

Kimi K3 (Reasoning)

higher is better

Kimi K3 (Reasoning)Scores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 75; Kimi K3 (Reasoning), Terminus-2: 73Kimi K3 (Reasoning) · Kimi Code75Kimi K3 (Reasoning) · Terminus-273
Data table: Kimi K3 (Reasoning)
Kimi K3 (Reasoning)
ModelDeveloperKimi K3 (Reasoning)
Kimi K3 (Reasoning), Kimi CodeMoonshot AI75
Kimi K3 (Reasoning), Terminus-2Moonshot AI73

Kimi K2.7 Code (Reasoning)

higher is better

Kimi K2.7 Code (Reasoning)Scores 0 to 100: Kimi K2.7 Code (Reasoning), Kimi Code: 72; Kimi K2.7 Code (Reasoning), Terminus-2: 72Kimi K2.7 Code (Reasoning) · Kimi Code72Kimi K2.7 Code (Reasoning) · Terminus-272
Data table: Kimi K2.7 Code (Reasoning)
Kimi K2.7 Code (Reasoning)
ModelDeveloperKimi K2.7 Code (Reasoning)
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI72
Kimi K2.7 Code (Reasoning), Terminus-2Moonshot AI72

Kimi K2.6 (Reasoning)

higher is better

Kimi K2.6 (Reasoning)Scores 0 to 100: Kimi K2.6 (Reasoning), Kimi Code: 70; Kimi K2.6 (Reasoning), Terminus-2: 70Kimi K2.6 (Reasoning) · Kimi Code70Kimi K2.6 (Reasoning) · Terminus-270
Data table: Kimi K2.6 (Reasoning)
Kimi K2.6 (Reasoning)
ModelDeveloperKimi K2.6 (Reasoning)
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI70
Kimi K2.6 (Reasoning), Terminus-2Moonshot AI70

By evaluation

Earth-Bench V2

higher is better

Earth-Bench V2Scores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 75; Kimi K2.7 Code (Reasoning), Kimi Code: 73; Kimi K2.6 (Reasoning), Kimi Code: 66Kimi K3 (Reasoning) · Kimi Code75Kimi K2.7 Code (Reasoning) · Kimi Code73Kimi K2.6 (Reasoning) · Kimi Code66
Data table: Earth-Bench V2
Earth-Bench V2
ModelDeveloperEarth-Bench V2
Kimi K3 (Reasoning), Kimi CodeMoonshot AI75
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI73
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI66

GeoBenchX

higher is better

GeoBenchXScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 65; Kimi K2.6 (Reasoning), Kimi Code: 64; Kimi K2.7 Code (Reasoning), Kimi Code: 64Kimi K3 (Reasoning) · Kimi Code65Kimi K2.6 (Reasoning) · Kimi Code64Kimi K2.7 Code (Reasoning) · Kimi Code64
Data table: GeoBenchX
GeoBenchX
ModelDeveloperGeoBenchX
Kimi K3 (Reasoning), Kimi CodeMoonshot AI65
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI64
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI64

GS-QA2

higher is better

GS-QA2Scores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 85; Kimi K2.7 Code (Reasoning), Kimi Code: 80; Kimi K2.6 (Reasoning), Kimi Code: 80Kimi K3 (Reasoning) · Kimi Code85Kimi K2.7 Code (Reasoning) · Kimi Code80Kimi K2.6 (Reasoning) · Kimi Code80
Data table: GS-QA2

GeoAgentBench

higher is better

GeoAgentBenchScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 96Kimi K3 (Reasoning) · Kimi Code96
Data table: GeoAgentBench
GeoAgentBench
ModelDeveloperGeoAgentBench
Kimi K3 (Reasoning), Kimi CodeMoonshot AI96

GeoAnalystBench

higher is better

GeoAnalystBenchScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 100Kimi K3 (Reasoning) · Kimi Code100
Data table: GeoAnalystBench
GeoAnalystBench
ModelDeveloperGeoAnalystBench
Kimi K3 (Reasoning), Kimi CodeMoonshot AI100

OGD4All

higher is better

OGD4AllScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 97Kimi K3 (Reasoning) · Kimi Code97
Data table: OGD4All
OGD4All
ModelDeveloperOGD4All
Kimi K3 (Reasoning), Kimi CodeMoonshot AI97

GeoNatureAgent

higher is better

GeoNatureAgentScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 100Kimi K3 (Reasoning) · Kimi Code100
Data table: GeoNatureAgent
GeoNatureAgent
ModelDeveloperGeoNatureAgent
Kimi K3 (Reasoning), Kimi CodeMoonshot AI100

MultiGlobeQA

higher is better

MultiGlobeQAScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 77Kimi K3 (Reasoning) · Kimi Code77
Data table: MultiGlobeQA
MultiGlobeQA
ModelDeveloperMultiGlobeQA
Kimi K3 (Reasoning), Kimi CodeMoonshot AI77

GeoAgentEval

higher is better

GeoAgentEvalScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 96Kimi K3 (Reasoning) · Kimi Code96
Data table: GeoAgentEval
GeoAgentEval
ModelDeveloperGeoAgentEval
Kimi K3 (Reasoning), Kimi CodeMoonshot AI96

Cost

Cost per task

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

Cost per taskCost per task in US dollars: Kimi K2.7 Code (Reasoning), Kimi Code: $0.18; Kimi K2.6 (Reasoning), Kimi Code: $0.27; Kimi K3 (Reasoning), Kimi Code: $0.32$0$0.10$0.20$0.30$0.40$0.18Kimi K2.7Code(Reasoning)Kimi Code$0.27Kimi K2.6(Reasoning)Kimi Code$0.32Kimi K3(Reasoning)Kimi 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 entry in Kimi Code with a result, highlighted. Provisional: not yet ranked.

Data table: Cost per task
Cost per task
ModelDeveloperCost per task
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI$0.18
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI$0.27
Kimi K3 (Reasoning), Kimi CodeMoonshot AI$0.32

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: Kimi K2.6 (Reasoning), Kimi Code: 70.04, $0.27; Kimi K2.7 Code (Reasoning), Kimi Code: 72.33, $0.18; Kimi K3 (Reasoning), Kimi Code: 75.1, $0.32Most attractive quadrant6970717273747576$0.10$0.20$0.50Cost per task (USD, log scale)Geospatial Agent IndexKimi K3 (Reasoning), Kimi CodeKimi K2.7 Code (Reasoning), Kimi CodeKimi K2.6 (Reasoning), Kimi 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
Kimi K2.6 (Reasoning), Kimi Code70$0.27No
Kimi K2.7 Code (Reasoning), Kimi Code72$0.18Yes
Kimi K3 (Reasoning), Kimi Code75$0.32Yes

Time

Time per task

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

Time per taskTime per task: Kimi K3 (Reasoning), Kimi Code: 1.5 min; Kimi K2.7 Code (Reasoning), Kimi Code: 1.9 min; Kimi K2.6 (Reasoning), Kimi Code: 3.0 min012341.5 minKimi K3(Reasoning)Kimi Code1.9 minKimi K2.7Code(Reasoning)Kimi Code3.0 minKimi K2.6(Reasoning)Kimi Code
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 Kimi Code with a result, highlighted. Provisional: not yet ranked.

Data table: Time per task
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
Kimi K3 (Reasoning), Kimi CodeMoonshot AI1.5 min
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI1.9 min
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI3.0 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: Kimi K2.6 (Reasoning), Kimi Code: 70.04, 3.0 min; Kimi K2.7 Code (Reasoning), Kimi Code: 72.33, 1.9 min; Kimi K3 (Reasoning), Kimi Code: 75.1, 1.5 minMost attractive quadrant69707172737475761 min2 min5 minTime per task (minutes, log scale)Geospatial Agent IndexKimi K3 (Reasoning), Kimi CodeKimi K2.7 Code (Reasoning), Kimi CodeKimi K2.6 (Reasoning), Kimi 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
Kimi K2.6 (Reasoning), Kimi Code703.0 minNo
Kimi K2.7 Code (Reasoning), Kimi Code721.9 minNo
Kimi K3 (Reasoning), Kimi Code751.5 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
Kimi K3 (Reasoning), Kimi CodeRanked75658596100971007796663092
Kimi K2.7 Code (Reasoning), Kimi CodeRanked736480No dataNo dataNo dataNo dataNo dataNo data563192
Kimi K2.6 (Reasoning), Kimi CodeRanked666480No dataNo dataNo dataNo dataNo dataNo data602892

About Kimi Code

What it is
The command-line coding agent made by Moonshot AI. It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Version
2.1.1, the same pinned build for every run
Models we run in it
3 from Moonshot AI
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 Kimi Code?
Which model is best in Kimi Code?
Kimi K3 (Reasoning), with 75.1 on the Geospatial Agent Index, 19th of 83 entries.
What is the cheapest model in Kimi Code?
Kimi K2.7 Code (Reasoning), at $0.18 per agent task at list prices.
What is the fastest model in Kimi Code?
Kimi K3 (Reasoning), at 1.5 min (estimated) per agent task.
Is Kimi Code better than other harnesses?
It depends on the model. Kimi K3 (Reasoning) scores 75.1 in Kimi Code and 73.1 in Terminus-2; Kimi K2.6 (Reasoning) scores 70.0 in Kimi Code and 69.6 in Terminus-2. Gaps under about 3 points are within noise.
Who makes Kimi Code?
Moonshot AI. The command-line coding agent made by Moonshot AI. It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Which version of Kimi Code do you run?
2.1.1, pinned: every run uses the same build.
Are any models in Kimi Code still being benchmarked?
No. Every entry in Kimi Code has finished its benchmark and is ranked.