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
Best on the Geospatial Agent Index
Geospatial Agent Index, Text only
Lowest cost
- #1Kimi K2.7 Code (Reasoning)$0.18
- #2Kimi K2.6 (Reasoning)$0.27
- #3Kimi K3 (Reasoning)$0.32
Cost per agent task, at list prices
Fastest
- #1Kimi K3 (Reasoning)1.5 min (estimated)
- #2Kimi K2.7 Code (Reasoning)1.9 min (estimated)
- #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
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
| Model | Developer | Geospatial Agent Index: every model in Kimi Code |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 75 |
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | 72 |
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | 70 |
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
Data table: Kimi K3 (Reasoning)
| Model | Developer | Kimi K3 (Reasoning) |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 75 |
| Kimi K3 (Reasoning), Terminus-2 | Moonshot AI | 73 |
Kimi K2.7 Code (Reasoning)
higher is better
Data table: Kimi K2.7 Code (Reasoning)
| Model | Developer | Kimi K2.7 Code (Reasoning) |
|---|---|---|
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | 72 |
| Kimi K2.7 Code (Reasoning), Terminus-2 | Moonshot AI | 72 |
Kimi K2.6 (Reasoning)
higher is better
Data table: Kimi K2.6 (Reasoning)
| Model | Developer | Kimi K2.6 (Reasoning) |
|---|---|---|
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | 70 |
| Kimi K2.6 (Reasoning), Terminus-2 | Moonshot AI | 70 |
By evaluation
Earth-Bench V2
higher is better
Data table: Earth-Bench V2
| Model | Developer | Earth-Bench V2 |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 75 |
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | 73 |
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | 66 |
GeoBenchX
higher is better
Data table: GeoBenchX
| Model | Developer | GeoBenchX |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 65 |
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | 64 |
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | 64 |
GS-QA2
higher is better
Data table: GS-QA2
| Model | Developer | GS-QA2 |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 85 |
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | 80 |
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | 80 |
GeoAgentBench
higher is better
Data table: GeoAgentBench
| Model | Developer | GeoAgentBench |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 96 |
GeoAnalystBench
higher is better
Data table: GeoAnalystBench
| Model | Developer | GeoAnalystBench |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 100 |
OGD4All
higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 97 |
GeoNatureAgent
higher is better
Data table: GeoNatureAgent
| Model | Developer | GeoNatureAgent |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 100 |
MultiGlobeQA
higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 77 |
GeoAgentEval
higher is better
Data table: GeoAgentEval
| Model | Developer | GeoAgentEval |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 96 |
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 entry in Kimi Code with a result, highlighted. Provisional: not yet ranked.
Data table: Cost per task
| Model | Developer | Cost per task |
|---|---|---|
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | $0.18 |
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | $0.27 |
| Kimi K3 (Reasoning), Kimi Code | Moonshot 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
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 |
|---|---|---|---|
| Kimi K2.6 (Reasoning), Kimi Code | 70 | $0.27 | No |
| Kimi K2.7 Code (Reasoning), Kimi Code | 72 | $0.18 | Yes |
| Kimi K3 (Reasoning), Kimi Code | 75 | $0.32 | 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 entry in Kimi Code with a result, highlighted. Provisional: not yet ranked.
Data table: Time per task
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 1.5 min |
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | 1.9 min |
| Kimi K2.6 (Reasoning), Kimi Code | Moonshot AI | 3.0 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
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 |
|---|---|---|---|
| Kimi K2.6 (Reasoning), Kimi Code | 70 | 3.0 min | No |
| Kimi K2.7 Code (Reasoning), Kimi Code | 72 | 1.9 min | No |
| Kimi K3 (Reasoning), Kimi Code | 75 | 1.5 min | Yes |
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Ranked | 75 | 65 | 85 | 96 | 100 | 97 | 100 | 77 | 96 | 66 | 30 | 92 |
| Kimi K2.7 Code (Reasoning), Kimi Code | Ranked | 73 | 64 | 80 | No data | No data | No data | No data | No data | No data | 56 | 31 | 92 |
| Kimi K2.6 (Reasoning), Kimi Code | Ranked | 66 | 64 | 80 | No data | No data | No data | No data | No data | No data | 60 | 28 | 92 |
About Kimi Code
- Made by
- Moonshot AI
- 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.
