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MiniMax: geospatial agent results

Helps you choose the best MiniMax model for geospatial work: GIS and Earth observation agent tasks, vision and knowledge, with cost and time.

Overview

Most intelligent

  1. #1MiniMax M3.1 Flash (max) Open weights60

Geospatial Intelligence Index, Text only

Lowest cost

  1. #1MiniMax M3.1 Flash (max), Terminus-2$0.018

Cost per agent task, at list prices

Fastest

  1. #1MiniMax M3.1 Flash (max), Terminus-28.7 min

Time per agent task, at 100 output tokens/s

  • For intelligence, the top MiniMax models are MiniMax M3.1 Flash (max) (60) on the Geospatial Intelligence Index (Text only).
  • For cost, the cheapest per agent task are MiniMax M3.1 Flash (max) ($0.018).
  • For speed, the fastest per agent task are MiniMax M3.1 Flash (max) (8.7 min).
  • For context window, the largest are MiniMax M3.1 Flash: 1M tokens.

Intelligence

Geospatial Intelligence Index, Text only · Higher is better

IntelligenceScores 0 to 100: MiniMax M3.1 Flash (max): 60025507510060MiniMax M3.1Flash (max)
Data table: Intelligence
Intelligence
ModelDeveloperIntelligence
MiniMax M3.1 Flash (max)MiniMax60

Cost per task

USD per agent task · Lower is better

Cost per taskCost per task in US dollars: MiniMax M3.1 Flash (max), Terminus-2: $0.018$0$0.005$0.01$0.015$0.02$0.018MiniMax M3.1Flash (max)Terminus-2
Data table: Cost per task
Cost per task
ModelDeveloperCost per task
MiniMax M3.1 Flash (max), Terminus-2MiniMax$0.018

Time per task

Minutes per agent task · Lower is better

Time per taskTime per task: MiniMax M3.1 Flash (max), Terminus-2: 8.7 min02.557.5108.7 minMiniMax M3.1Flash (max)Terminus-2
Data table: Time per task
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
MiniMax M3.1 Flash (max), Terminus-2MiniMax8.7 min

Models

MiniMax M3.1 Flash

Open weightsReads images

1M token context · Released 19 October 2026

$0.15 input · $0.60 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; MiniMax M3.1 Flash (max): 600255075100Claude Opus 5.5 (xhigh)89GPT-6.1 Sol (medium)86DeepSeek V4 Pro (max)79MiniMax M3.1 Flash (max)60
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 MiniMax 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
MiniMax M3.1 Flash (max)MiniMax60

Text + vision

No MiniMax 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; MiniMax M3.1 Flash (max), Terminus-2: 520255075100Claude Opus 5.5 (xhigh)Claude Code85GPT-6.1 Sol (xhigh)Codex82GLM-5.3 (max)Claude Code73MiniMax M3.1 Flash (max)Terminus-252
How to read this chart

Bars. Every MiniMax 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
MiniMax M3.1 Flash (max), Terminus-2MiniMax52

Text + vision

No MiniMax 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; MiniMax M3.1 Flash (max), Terminus-2: 39025507510079Claude Opus5.5 (high)Claude Code75GPT-6 Sol(high)Codex39MiniMax M3.1Flash (max)Terminus-2
Data table: Earth-Bench V2
Earth-Bench V2
ModelDeveloperEarth-Bench V2
Claude Opus 5.5 (high), Claude CodeAnthropic79
GPT-6 Sol (high), CodexOpenAI75
MiniMax M3.1 Flash (max), Terminus-2MiniMax39

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; MiniMax M3.1 Flash (max), Terminus-2: 44025507510083Claude Opus5.5 (xhigh)Claude Code79GPT-6.1 Sol(medium)Codex44MiniMax M3.1Flash (max)Terminus-2
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; MiniMax M3.1 Flash (max), Terminus-2: 71025507510094Claude Opus5.5 (xhigh)Claude Code94GPT-6.1 Sol(xhigh)Codex71MiniMax M3.1Flash (max)Terminus-2
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; MiniMax M3.1 Flash (max), Terminus-2: 880255075100100Claude Opus5.5 (high)Claude Code100GPT-6.1 Sol(medium)Codex88MiniMax M3.1Flash (max)Terminus-2
Data table: GeoAgentBench
GeoAgentBench
ModelDeveloperGeoAgentBench
Claude Opus 5.5 (high), Claude CodeAnthropic100
GPT-6.1 Sol (medium), CodexOpenAI100
MiniMax M3.1 Flash (max), Terminus-2MiniMax88

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; MiniMax M3.1 Flash (max), Terminus-2: 470255075100100DeepSeek V4Pro (max)Terminus-2100GLM-5.2(high)Terminus-247MiniMax M3.1Flash (max)Terminus-2
Data table: GeoAnalystBench
GeoAnalystBench
ModelDeveloperGeoAnalystBench
DeepSeek V4 Pro (max), Terminus-2DeepSeek100
GLM-5.2 (high), Terminus-2Z.ai100
MiniMax M3.1 Flash (max), Terminus-2MiniMax47

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; MiniMax M3.1 Flash (max): 89025507510098Claude Sonnet4.6 (high)94Gemini 3.8Flash (low)89MiniMax M3.1Flash (max)
Data table: Geospatial knowledge
Geospatial knowledge
ModelDeveloperGeospatial knowledge
Claude Sonnet 4.6 (high)Anthropic98
Gemini 3.8 Flash (low)Google94
MiniMax M3.1 Flash (max)MiniMax89

Cost

Cost per task

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

Cost per taskCost per task in US dollars: MiniMax M3.1 Flash (max), Terminus-2: $0.018; GPT-6.1 Sol (xhigh), Codex: $0.085; Claude Opus 5.5 (xhigh), Claude Code: $0.25$0$0.10$0.20$0.30$0.40$0.018MiniMax M3.1Flash (max)Terminus-2$0.085GPT-6.1 Sol(xhigh)Codex$0.25Claude Opus5.5 (xhigh)Claude 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 MiniMax 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
MiniMax M3.1 Flash (max), Terminus-2MiniMax$0.018
GPT-6.1 Sol (xhigh), CodexOpenAI$0.085
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: MiniMax M3.1 Flash (max), Terminus-2: 51.55, $0.018; 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 quadrant45505560657075808590$0.01$0.02$0.05$0.10$0.20$0.50$1.00Cost per task (USD, log scale)Geospatial Agent IndexMiniMax M3.1 Flash (max), 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
MiniMax M3.1 Flash (max), Terminus-252$0.018Yes
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; MiniMax M3.1 Flash (max), Terminus-2: 8.7 min02.557.5100.8 minGPT-6.1 Sol(xhigh)Codex1.4 minClaude Opus5.5 (xhigh)Claude Code8.7 minMiniMax M3.1Flash (max)Terminus-2
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 MiniMax 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
MiniMax M3.1 Flash (max), Terminus-2MiniMax8.7 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: MiniMax M3.1 Flash (max), Terminus-2: 51.55, 8.7 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 quadrant455055606570758085900.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexMiniMax M3.1 Flash (max), 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
MiniMax M3.1 Flash (max), Terminus-2528.7 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-QA2GeoAgentBenchGeoAnalystBenchGeospatial knowledge
MiniMax M3.1 Flash (max), Terminus-2Ranked394471884789

Frequently asked questions

What MiniMax models do you benchmark?
We benchmark 1 MiniMax model: MiniMax M3.1 Flash.
Which is the most intelligent MiniMax model for geospatial work?
MiniMax M3.1 Flash (max), with 60.5 on the Geospatial Intelligence Index (Text only), #49 of 61.
What is the cheapest MiniMax model per task?
MiniMax M3.1 Flash (max), Terminus-2, at $0.018 per agent task at list prices, #17 of 74 on the Geospatial Agent Index.
What is the fastest MiniMax model?
MiniMax M3.1 Flash (max), Terminus-2, at 8.7 min per agent task at 100 output tokens per second, #73 of 74.
Does MiniMax offer open-weights models?
Yes: MiniMax M3.1 Flash. Their weights are published, so you can run them on your own hardware.
Which MiniMax models can read satellite imagery?
MiniMax M3.1 Flash takes images as input.
How do MiniMax models do on Earth observation, GIS and geospatial knowledge?
Which MiniMax model is best for agent work, and in which harness?
MiniMax M3.1 Flash (max), Terminus-2, with 51.5 on the Geospatial Agent Index, #59 of 74, in the Terminus-2 harness.
How does MiniMax's best model compare with the overall leader?
MiniMax M3.1 Flash (max) scores 60.5 on the Geospatial Intelligence Index (Text only), 28.2 points behind the leader, Anthropic's Claude Opus 5.5 (xhigh), with 88.7.
Are any MiniMax models still being benchmarked?
No. Every MiniMax model listed has finished its benchmark and is ranked.
How should I choose between MiniMax models for geospatial work?
Start from the job. For the strongest answers, MiniMax M3.1 Flash (max); for agent work with data and code, MiniMax M3.1 Flash (max), Terminus-2; for the lowest cost, MiniMax M3.1 Flash (max), Terminus-2; to run it yourself, MiniMax M3.1 Flash. Each model page shows its scores on every evaluation.