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MiniMax Code: geospatial agent results

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

Made by MiniMax · Version 0.7.4, pinned · 1 model from 1 lab · 1 entry, 1 ranked

Overview

Lowest cost

  1. #1MiniMax M3 (Reasoning)$0.15

Cost per agent task, at list prices

Fastest

  1. #1MiniMax M3 (Reasoning)4.9 min (estimated)

Time per agent task, at 100 output tokens/s

  • In MiniMax Code, the best entry is MiniMax M3 (Reasoning), 51st of 83 entries on the Geospatial Agent Index, at 68.2.

Models

Geospatial Agent Index: every model in MiniMax 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 MiniMax CodeScores 0 to 100: 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: 69; GLM-5.3 (low), Terminus-2: 68; MiniMax M3 (Reasoning), MiniMax Code: 68; Nemotron 3 Ultra (high), Terminus-2: 680255075100DeepSeek V4 Flash (max)Terminus-269Qwen 3.8 27B (Reasoning)Qwen Code69GPT-5.6 Terra (high)Codex69Claude Sonnet 5.5 (low)Claude Code69GLM-5.3 (low)Terminus-268MiniMax M3 (Reasoning)MiniMax Code68Nemotron 3 Ultra (high)Terminus-268
How to read this chart

Bars. Every entry in MiniMax 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 MiniMax Code
Geospatial Agent Index: every model in MiniMax Code
ModelDeveloperGeospatial Agent Index: every model in MiniMax Code
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
GLM-5.3 (low), Terminus-2Z.ai68
MiniMax M3 (Reasoning), MiniMax CodeMiniMax68
Nemotron 3 Ultra (high), Terminus-2NVIDIA68

Same model elsewhere

Not yet: every model in MiniMax Code runs only in MiniMax 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 Pro (high), Terminus-2: 68; MiniMax M3 (Reasoning), MiniMax Code: 68; GLM-5.2 (high), Terminus-2: 68; GLM-5.2 (max), Terminus-2: 68; Kimi K2.6 (Reasoning), Terminus-2: 68; Claude Haiku 5.5 (medium), Claude Code: 68; Claude Haiku 5.5 (max), Claude Code: 67DeepSeek V4 Pro (high) · Terminus-268MiniMax M3 (Reasoning) · MiniMax Code68GLM-5.2 (high) · Terminus-268GLM-5.2 (max) · Terminus-268Kimi K2.6 (Reasoning) · Terminus-268Claude Haiku 5.5 (medium) · Claude Code68Claude Haiku 5.5 (max) · Claude Code67
Data table: Earth-Bench V2

GeoBenchX

higher is better

GeoBenchXScores 0 to 100: Gemini 3.1 Flash-Lite (high), Terminus-2: 61; GLM-5.2 (Non-reasoning), Terminus-2: 60; Mistral Large 4 (high), Terminus-2: 59; MiniMax M3 (Reasoning), MiniMax Code: 59; GLM-5.3-Flash (low), Terminus-2: 58; GLM-5.3-Flash (max), Terminus-2: 57; GPT-6 Luna (medium), Codex: 57Gemini 3.1 Flash-Lite (high) · Terminus-261GLM-5.2 (Non-reasoning) · Terminus-260Mistral Large 4 (high) · Terminus-259MiniMax M3 (Reasoning) · MiniMax Code59GLM-5.3-Flash (low) · Terminus-258GLM-5.3-Flash (max) · Terminus-257GPT-6 Luna (medium) · Codex57
Data table: GeoBenchX

GS-QA2

higher is better

GS-QA2Scores 0 to 100: Mistral Large 4 (high), Terminus-2: 79; Claude Haiku 5.5 (medium), Claude Code: 79; Claude Opus 4.7 (high), Claude Code: 79; Claude Sonnet 5.5 (medium), Claude Code: 79; MiniMax M3 (Reasoning), MiniMax Code: 78; GLM-5.2 (high), Terminus-2: 78; GPT-6 Luna (xhigh), Codex: 78Mistral Large 4 (high) · Terminus-279Claude Haiku 5.5 (medium) · Claude Code79Claude Opus 4.7 (high) · Claude Code79Claude Sonnet 5.5 (medium) · Claude Code79MiniMax M3 (Reasoning) · MiniMax Code78GLM-5.2 (high) · Terminus-278GPT-6 Luna (xhigh) · Codex78
Data table: GS-QA2

GeoAgentBench

higher is better

GeoAgentBenchScores 0 to 100: 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: 94; MiniMax M3 (Reasoning), MiniMax Code: 94DeepSeek 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-294MiniMax M3 (Reasoning) · MiniMax Code94
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; MiniMax M3 (Reasoning), MiniMax Code: 79; Kimi K2.6 (Reasoning), Terminus-2: 79; Qwen 3.8 27B (Reasoning), Qwen Code: 79; GPT-6 Luna (low), Codex: 79GLM-5.3-Flash (max) · Terminus-284GLM-5.3 (max) · Terminus-284MiMo V2.6 Flash (Reasoning) · MiMo Code84MiniMax M3 (Reasoning) · MiniMax Code79Kimi K2.6 (Reasoning) · Terminus-279Qwen 3.8 27B (Reasoning) · Qwen Code79GPT-6 Luna (low) · Codex79
Data table: GeoAnalystBench

OGD4All

higher is better

OGD4AllScores 0 to 100: Muse Glimmer 30B (high), Terminus-2: 95; Mistral Large 4 (high), Terminus-2: 95; Claude Haiku 4.5 (Reasoning), Claude Code: 95; Claude Haiku 5.5 (low), Claude Code: 95; Claude Opus 4.7 (high), Claude Code: 95; MiniMax M3 (Reasoning), MiniMax Code: 94; Step 5 Preview (Reasoning), Terminus-2: 94Muse Glimmer 30B (high) · Terminus-295Mistral Large 4 (high) · Terminus-295Claude Haiku 4.5 (Reasoning) · Claude Code95Claude Haiku 5.5 (low) · Claude Code95Claude Opus 4.7 (high) · Claude Code95MiniMax M3 (Reasoning) · MiniMax Code94Step 5 Preview (Reasoning) · Terminus-294
Data table: OGD4All

GeoNatureAgent

higher is better

GeoNatureAgentScores 0 to 100: MiniMax M3.1 Flash (max), Terminus-2: 100; Mistral Large 4 (high), Terminus-2: 100; MiniMax M3 (Reasoning), MiniMax Code: 93; Claude Haiku 4.5 (Reasoning), Claude Code: 93; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 92; Nemotron 3 Ultra (high), Terminus-2: 92; Step 5 Preview (Reasoning), Terminus-2: 88MiniMax M3.1 Flash (max) · Terminus-2100Mistral Large 4 (high) · Terminus-2100MiniMax M3 (Reasoning) · MiniMax Code93Claude Haiku 4.5 (Reasoning) · Claude Code93Nemotron 3.5 Super VL 120B (Reasoning) ·Terminus-292Nemotron 3 Ultra (high) · Terminus-292Step 5 Preview (Reasoning) · Terminus-288
Data table: GeoNatureAgent

MultiGlobeQA

higher is better

MultiGlobeQAScores 0 to 100: Qwen 3.8 27B (xhigh), Terminus-2: 74; GLM-5.3-Flash (max), Claude Code: 74; Claude Sonnet 5 (high), Claude Code: 73; MiniMax M3 (Reasoning), MiniMax Code: 73; Step 5 Preview (Reasoning), Terminus-2: 73; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 73; Claude Haiku 5.5 (medium), Claude Code: 73Qwen 3.8 27B (xhigh) · Terminus-274GLM-5.3-Flash (max) · Claude Code74Claude Sonnet 5 (high) · Claude Code73MiniMax M3 (Reasoning) · MiniMax Code73Step 5 Preview (Reasoning) · Terminus-273Nemotron 3.5 Super VL 120B (Reasoning) ·Terminus-273Claude Haiku 5.5 (medium) · Claude Code73
Data table: MultiGlobeQA

GeoAgentEval

higher is better

GeoAgentEvalScores 0 to 100: MiniMax M3 (Reasoning), MiniMax Code: 92; Mistral Large 4 (high), Terminus-2: 92; Muse Glimmer 30B (high), Terminus-2: 92; Qwen 3.8 27B (xhigh), Terminus-2: 92; DeepSeek V4.1 Flash (max), Claude Code: 92; Claude Haiku 5.5 (medium), Claude Code: 92; Claude Haiku 5.5 (high), Claude Code: 92MiniMax M3 (Reasoning) · MiniMax Code92Mistral Large 4 (high) · Terminus-292Muse Glimmer 30B (high) · Terminus-292Qwen 3.8 27B (xhigh) · Terminus-292DeepSeek V4.1 Flash (max) · Claude Code92Claude Haiku 5.5 (medium) · Claude Code92Claude Haiku 5.5 (high) · Claude Code92
Data table: GeoAgentEval

Cost

Cost per task

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

Cost per taskCost per task in US dollars: Gemini 3.8 Flash (low), Terminus-2: $0.11; Qwen 3.8 27B (xhigh), Terminus-2: $0.12; Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2: $0.13; Step 5 Preview (Reasoning), Terminus-2: $0.13; Claude Opus 5.5 (high), Claude Code: $0.13; MiniMax M3 (Reasoning), MiniMax Code: $0.15; Kimi K2.7 Code (Reasoning), Kimi Code: $0.18$0$0.05$0.10$0.15$0.20Gemini 3.8 Flash (low)Terminus-2$0.11Qwen 3.8 27B (xhigh)Terminus-2$0.12Nemotron 3 Nano Omni 30B (Reasoning)Terminus-2$0.13Step 5 Preview (Reasoning)Terminus-2$0.13Claude Opus 5.5 (high)Claude Code$0.13MiniMax M3 (Reasoning)MiniMax Code$0.15Kimi K2.7 Code (Reasoning)Kimi Code$0.18
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 MiniMax 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: MiniMax M3 (Reasoning), MiniMax Code: 68.24, $0.15; GLM-5.3 (low), Terminus-2: 68.41, $0.27; Nemotron 3 Ultra (high), Terminus-2: 67.93, $0.067; Claude Sonnet 5.5 (low), Claude Code: 69.25, $0.043; GPT-5.6 Terra (high), Codex: 69.32, $0.10; Qwen 3.8 27B (Reasoning), Qwen Code: 69.33, $0.22; DeepSeek V4 Flash (max), Terminus-2: 69.4, $0.062Most attractive quadrant67.667.86868.268.468.668.86969.269.469.6$0.02$0.05$0.10$0.20$0.50Cost per task (USD, log scale)Geospatial Agent IndexMiniMax M3 (Reasoning), MiniMax CodeDeepSeek V4 Flash (max), Terminus-2Claude Sonnet 5.5 (low), Claude CodeQwen 3.8 27B (Reasoning), Qwen CodeGPT-5.6 Terra (high), CodexGLM-5.3 (low), Terminus-2Nemotron 3 Ultra (high), 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 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
MiniMax M3 (Reasoning), MiniMax Code68$0.15No
GLM-5.3 (low), Terminus-268$0.27No
Nemotron 3 Ultra (high), Terminus-268$0.067No
Claude Sonnet 5.5 (low), Claude Code69$0.043Yes
GPT-5.6 Terra (high), Codex69$0.10No
Qwen 3.8 27B (Reasoning), Qwen Code69$0.22No
DeepSeek V4 Flash (max), Terminus-269$0.062Yes

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 MiniMax 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: MiniMax M3 (Reasoning), MiniMax Code: 68.24, 4.9 min; GLM-5.3 (low), Terminus-2: 68.41, 2.7 min; Nemotron 3 Ultra (high), Terminus-2: 67.93, 2.4 min; Claude Sonnet 5.5 (low), Claude Code: 69.25, 0.4 min; GPT-5.6 Terra (high), Codex: 69.32, 0.8 min; Qwen 3.8 27B (Reasoning), Qwen Code: 69.33, 4.7 min; DeepSeek V4 Flash (max), Terminus-2: 69.4, 5.7 minMost attractive quadrant67.667.86868.268.468.668.86969.269.469.60.2 min0.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexMiniMax M3 (Reasoning), MiniMax CodeDeepSeek V4 Flash (max), Terminus-2Qwen 3.8 27B (Reasoning), Qwen CodeGPT-5.6 Terra (high), CodexClaude Sonnet 5.5 (low), Claude CodeGLM-5.3 (low), Terminus-2Nemotron 3 Ultra (high), 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
MiniMax M3 (Reasoning), MiniMax Code684.9 minNo
GLM-5.3 (low), Terminus-2682.7 minNo
Nemotron 3 Ultra (high), Terminus-2682.4 minNo
Claude Sonnet 5.5 (low), Claude Code690.4 minYes
GPT-5.6 Terra (high), Codex690.8 minYes
Qwen 3.8 27B (Reasoning), Qwen Code694.7 minYes
DeepSeek V4 Flash (max), Terminus-2695.7 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-QA2GeoAgentBenchGeoAnalystBenchOGD4AllGeoNatureAgentMultiGlobeQAGeoAgentEval
MiniMax M3 (Reasoning), MiniMax CodeRanked685978947994937392

About MiniMax Code

Made by
MiniMax
What it is
The command-line coding agent made by MiniMax. It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Version
0.7.4, the same pinned build for every run
Models we run in it
1 from MiniMax
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 MiniMax Code?
Which model is best in MiniMax Code?
MiniMax M3 (Reasoning), with 68.2 on the Geospatial Agent Index, 51st of 83 entries.
What is the cheapest model in MiniMax Code?
MiniMax M3 (Reasoning), at $0.15 per agent task at list prices.
What is the fastest model in MiniMax Code?
MiniMax M3 (Reasoning), at 4.9 min (estimated) per agent task.
Is MiniMax Code better than other harnesses?
We cannot say yet: every model in MiniMax Code runs only in MiniMax Code so far, so there is no like-for-like comparison. The Geospatial Agent Index ranks every entry, in every harness.
Who makes MiniMax Code?
MiniMax. The command-line coding agent made by MiniMax. It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Which version of MiniMax Code do you run?
0.7.4, pinned: every run uses the same build.
Are any models in MiniMax Code still being benchmarked?
No. Every entry in MiniMax Code has finished its benchmark and is ranked.