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Xiaomi: geospatial agent results

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

Overview

Most intelligent

Not yet ranked

Geospatial Intelligence Index, Text only

Lowest cost

  1. #1MiMo V2.6 Flash (Reasoning), MiMo Code$0.047

Cost per agent task, at list prices

Fastest

  1. #1MiMo V2.6 Flash (Reasoning), MiMo Code5.3 min (estimated)

Time per agent task, at 100 output tokens/s

  • For cost, the cheapest per agent task are MiMo V2.6 Flash (Reasoning) ($0.047).
  • For speed, the fastest per agent task are MiMo V2.6 Flash (Reasoning) (5.3 min (estimated)).
  • For context window, the largest are MiMo V2.6 Flash: 1M tokens.

Cost per task (USD)

lower is better

Cost per task (USD)Cost per task (USD) in US dollars: MiMo V2.6 Flash (Reasoning), MiMo Code: $0.047MiMo V2.6 Flash (Reasoning) · MiMo Code$0.047
Data table: Cost per task (USD)
Cost per task (USD)
ModelDeveloperCost per task (USD)
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi$0.047

Time per task (min)

lower is better

Time per task (min)Time per task (min): MiMo V2.6 Flash (Reasoning), MiMo Code: 5.3 minMiMo V2.6 Flash (Reasoning) · MiMo Code5.3 min
Data table: Time per task (min)
Time per task (min)
ModelDeveloperTime per task (at 100 output tokens/s)
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi5.3 min

Models

MiMo V2.6 Flash

Open weightsReads images

1M token context

$0.14 input · $0.28 output per 1M tokens

Intelligence

Text only

No Xiaomi model is ranked on the Geospatial Intelligence Index yet.

Text + vision

No Xiaomi 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; Kimi K3 (Reasoning), Kimi Code: 75; MiMo V2.6 Flash (Reasoning), MiMo Code: 640255075100Claude Opus 5.5 (xhigh)Claude Code85GPT-6.1 Sol (xhigh)Codex82Kimi K3 (Reasoning)Kimi Code75MiMo V2.6 Flash (Reasoning)MiMo Code64
How to read this chart

Bars. Every Xiaomi 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
Kimi K3 (Reasoning), Kimi CodeMoonshot AI75
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi64

Text + vision

No Xiaomi model has a Text + vision score yet: that view also needs Vision agents and Vision results.

Earth-Bench V2

higher is better

Earth-Bench V2Scores 0 to 100: Claude Opus 5.5 (max), Claude Code: 80; GPT-6 Sol (high), Codex: 75; MiMo V2.6 Flash (Reasoning), MiMo Code: 55Claude Opus 5.5 (max) · Claude Code80GPT-6 Sol (high) · Codex75MiMo V2.6 Flash (Reasoning) · MiMo Code55
Data table: Earth-Bench V2
Earth-Bench V2
ModelDeveloperEarth-Bench V2
Claude Opus 5.5 (max), Claude CodeAnthropic80
GPT-6 Sol (high), CodexOpenAI75
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi55

GeoBenchX

higher is better

GeoBenchXScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 83; GPT-6.1 Sol (medium), Codex: 79; MiMo V2.6 Flash (Reasoning), MiMo Code: 57Claude Opus 5.5 (xhigh) · Claude Code83GPT-6.1 Sol (medium) · Codex79MiMo V2.6 Flash (Reasoning) · MiMo Code57
Data table: GeoBenchX

GS-QA2

higher is better

GS-QA2Scores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 94; GPT-6.1 Sol (xhigh), Codex: 94; MiMo V2.6 Flash (Reasoning), MiMo Code: 82Claude Opus 5.5 (xhigh) · Claude Code94GPT-6.1 Sol (xhigh) · Codex94MiMo V2.6 Flash (Reasoning) · MiMo Code82
Data table: GS-QA2

GeoAgentBench

higher is better

GeoAgentBenchScores 0 to 100: Claude Opus 5.5 (high), Claude Code: 100; GPT-6.1 Sol (medium), Codex: 100; MiMo V2.6 Flash (Reasoning), MiMo Code: 90Claude Opus 5.5 (high) · Claude Code100GPT-6.1 Sol (medium) · Codex100MiMo V2.6 Flash (Reasoning) · MiMo Code90
Data table: GeoAgentBench
GeoAgentBench
ModelDeveloperGeoAgentBench
Claude Opus 5.5 (high), Claude CodeAnthropic100
GPT-6.1 Sol (medium), CodexOpenAI100
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi90

GeoAnalystBench

higher is better

GeoAnalystBenchScores 0 to 100: DeepSeek V4 Pro (max), Terminus-2: 100; GLM-5.2 (high), Terminus-2: 100; MiMo V2.6 Flash (Reasoning), MiMo Code: 79DeepSeek V4 Pro (max) · Terminus-2100GLM-5.2 (high) · Terminus-2100MiMo V2.6 Flash (Reasoning) · MiMo Code79
Data table: GeoAnalystBench
GeoAnalystBench
ModelDeveloperGeoAnalystBench
DeepSeek V4 Pro (max), Terminus-2DeepSeek100
GLM-5.2 (high), Terminus-2Z.ai100
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi79

OGD4All

higher is better

OGD4AllScores 0 to 100: Kimi K3 (Reasoning), Kimi Code: 97; Claude Opus 4.8 (high), Claude Code: 97; MiMo V2.6 Flash (Reasoning), MiMo Code: 84Kimi K3 (Reasoning) · Kimi Code97Claude Opus 4.8 (high) · Claude Code97MiMo V2.6 Flash (Reasoning) · MiMo Code84
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; MiMo V2.6 Flash (Reasoning), MiMo Code: 92MiniMax M3.1 Flash (max) · Terminus-2100Mistral Large 4 (high) · Terminus-2100MiMo V2.6 Flash (Reasoning) · MiMo Code92
Data table: GeoNatureAgent
GeoNatureAgent
ModelDeveloperGeoNatureAgent
MiniMax M3.1 Flash (max), Terminus-2MiniMax100
Mistral Large 4 (high), Terminus-2Mistral AI100
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi92

MultiGlobeQA

higher is better

MultiGlobeQAScores 0 to 100: Claude Sonnet 5.5 (xhigh), Claude Code: 79; Kimi K3 (Reasoning), Kimi Code: 77; MiMo V2.6 Flash (Reasoning), MiMo Code: 56Claude Sonnet 5.5 (xhigh) · Claude Code79Kimi K3 (Reasoning) · Kimi Code77MiMo V2.6 Flash (Reasoning) · MiMo Code56
Data table: MultiGlobeQA

GeoAgentEval

higher is better

GeoAgentEvalScores 0 to 100: Claude Opus 5.5 (medium), Claude Code: 100; GLM-5.3-Flash (max), Claude Code: 96; MiMo V2.6 Flash (Reasoning), MiMo Code: 76Claude Opus 5.5 (medium) · Claude Code100GLM-5.3-Flash (max) · Claude Code96MiMo V2.6 Flash (Reasoning) · MiMo Code76
Data table: GeoAgentEval

Cost

Cost per task

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

Cost per taskCost per task in US dollars: MiMo V2.6 Flash (Reasoning), MiMo Code: $0.047; 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$0.047MiMo V2.6Flash(Reasoning)MiMo Code$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 Xiaomi 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
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi$0.047
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: MiMo V2.6 Flash (Reasoning), MiMo Code: 64.44, $0.047; Claude Opus 5.5 (xhigh), Claude Code: 84.65, $0.25; GPT-6.1 Sol (xhigh), Codex: 81.82, $0.085; Kimi K3 (Reasoning), Kimi Code: 74.82, $0.32Most attractive quadrant60657075808590$0.02$0.05$0.10$0.20$0.50Cost per task (USD, log scale)Geospatial Agent IndexMiMo V2.6 Flash (Reasoning), MiMo CodeClaude Opus 5.5 (xhigh), Claude CodeGPT-6.1 Sol (xhigh), CodexKimi K3 (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
MiMo V2.6 Flash (Reasoning), MiMo Code64$0.047Yes
Claude Opus 5.5 (xhigh), Claude Code85$0.25Yes
GPT-6.1 Sol (xhigh), Codex82$0.085Yes
Kimi K3 (Reasoning), Kimi Code75$0.32No

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; MiMo V2.6 Flash (Reasoning), MiMo Code: 5.3 min01.534.560.8 minGPT-6.1 Sol(xhigh)Codex1.4 minClaude Opus5.5 (xhigh)Claude Code5.3 minMiMo V2.6Flash(Reasoning)MiMo 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 Xiaomi 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
MiMo V2.6 Flash (Reasoning), MiMo CodeXiaomi5.3 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: MiMo V2.6 Flash (Reasoning), MiMo Code: 64.44, 5.3 min; Claude Opus 5.5 (xhigh), Claude Code: 84.65, 1.4 min; GPT-6.1 Sol (xhigh), Codex: 81.82, 0.8 min; Kimi K3 (Reasoning), Kimi Code: 74.82, 1.5 minMost attractive quadrant606570758085900.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexMiMo V2.6 Flash (Reasoning), MiMo CodeClaude Opus 5.5 (xhigh), Claude CodeGPT-6.1 Sol (xhigh), CodexKimi K3 (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
MiMo V2.6 Flash (Reasoning), MiMo Code645.3 minNo
Claude Opus 5.5 (xhigh), Claude Code851.4 minYes
GPT-6.1 Sol (xhigh), Codex820.8 minYes
Kimi K3 (Reasoning), Kimi Code751.5 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-QA2GeoAgentBenchGeoAnalystBenchOGD4AllGeoNatureAgentMultiGlobeQAGeoAgentEval
MiMo V2.6 Flash (Reasoning), MiMo CodeRanked555782907984925676

Frequently asked questions

What Xiaomi models do you benchmark?
We benchmark 1 Xiaomi model: MiMo V2.6 Flash.
What is the cheapest Xiaomi model per task?
MiMo V2.6 Flash (Reasoning), MiMo Code, at $0.047 per agent task at list prices, #29 of 82 on the Geospatial Agent Index.
What is the fastest Xiaomi model?
MiMo V2.6 Flash (Reasoning), MiMo Code, at 5.3 min (estimated) per agent task at 100 output tokens per second, #63 of 82.
Does Xiaomi offer open-weights models?
Yes: MiMo V2.6 Flash. Their weights are published, so you can run them on your own hardware.
Which Xiaomi models can read satellite imagery?
MiMo V2.6 Flash takes images as input.
How do Xiaomi models do on Earth observation, GIS and geospatial knowledge?
Which Xiaomi model is best for agent work, and in which harness?
MiMo V2.6 Flash (Reasoning), MiMo Code, with 64.4 on the Geospatial Agent Index, #54 of 82, in the MiMo Code harness.
Are any Xiaomi models still being benchmarked?
No. Every Xiaomi model listed has finished its benchmark and is ranked.
How should I choose between Xiaomi models for geospatial work?
Start from the work. For agent work with data and code, MiMo V2.6 Flash (Reasoning), MiMo Code; for the lowest cost, MiMo V2.6 Flash (Reasoning), MiMo Code; to run it yourself, MiMo V2.6 Flash. Each model page shows its scores on every evaluation.