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
Fastest
- #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
Data table: Cost per task (USD)
| Model | Developer | Cost per task (USD) |
|---|---|---|
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | $0.047 |
Time per task (min)
lower is better
Data table: Time per task (min)
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 5.3 min |
Models
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
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
| Model | Developer | Geospatial Agent Index: Text only |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 85 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 82 |
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 75 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 64 |
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
Data table: Earth-Bench V2
| Model | Developer | Earth-Bench V2 |
|---|---|---|
| Claude Opus 5.5 (max), Claude Code | Anthropic | 80 |
| GPT-6 Sol (high), Codex | OpenAI | 75 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 55 |
GeoBenchX
higher is better
Data table: GeoBenchX
| Model | Developer | GeoBenchX |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 83 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 79 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 57 |
GS-QA2
higher is better
Data table: GS-QA2
| Model | Developer | GS-QA2 |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 94 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 94 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 82 |
GeoAgentBench
higher is better
Data table: GeoAgentBench
| Model | Developer | GeoAgentBench |
|---|---|---|
| Claude Opus 5.5 (high), Claude Code | Anthropic | 100 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 100 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 90 |
GeoAnalystBench
higher is better
Data table: GeoAnalystBench
| Model | Developer | GeoAnalystBench |
|---|---|---|
| DeepSeek V4 Pro (max), Terminus-2 | DeepSeek | 100 |
| GLM-5.2 (high), Terminus-2 | Z.ai | 100 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 79 |
OGD4All
higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 97 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 97 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 84 |
GeoNatureAgent
higher is better
Data table: GeoNatureAgent
| Model | Developer | GeoNatureAgent |
|---|---|---|
| MiniMax M3.1 Flash (max), Terminus-2 | MiniMax | 100 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 100 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 92 |
MultiGlobeQA
higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 79 |
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 77 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 56 |
GeoAgentEval
higher is better
Data table: GeoAgentEval
| Model | Developer | GeoAgentEval |
|---|---|---|
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 100 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 96 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 76 |
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 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
| Model | Developer | Cost per task |
|---|---|---|
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | $0.047 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | $0.085 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | $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
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 |
|---|---|---|---|
| MiMo V2.6 Flash (Reasoning), MiMo Code | 64 | $0.047 | Yes |
| Claude Opus 5.5 (xhigh), Claude Code | 85 | $0.25 | Yes |
| GPT-6.1 Sol (xhigh), Codex | 82 | $0.085 | Yes |
| Kimi K3 (Reasoning), Kimi Code | 75 | $0.32 | No |
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 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
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 0.8 min |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 1.4 min |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 5.3 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
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 |
|---|---|---|---|
| MiMo V2.6 Flash (Reasoning), MiMo Code | 64 | 5.3 min | No |
| Claude Opus 5.5 (xhigh), Claude Code | 85 | 1.4 min | Yes |
| GPT-6.1 Sol (xhigh), Codex | 82 | 0.8 min | Yes |
| Kimi K3 (Reasoning), Kimi Code | 75 | 1.5 min | No |
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 |
|---|---|---|---|---|---|---|---|---|---|---|
| MiMo V2.6 Flash (Reasoning), MiMo Code | Ranked | 55 | 57 | 82 | 90 | 79 | 84 | 92 | 56 | 76 |
