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
- 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
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
| Model | Developer | Geospatial Agent Index: every model in MiniMax Code |
|---|---|---|
| DeepSeek V4 Flash (max), Terminus-2 | DeepSeek | 69 |
| Qwen 3.8 27B (Reasoning), Qwen Code | Alibaba (Qwen) | 69 |
| GPT-5.6 Terra (high), Codex | OpenAI | 69 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 69 |
| GLM-5.3 (low), Terminus-2 | Z.ai | 68 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 68 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 68 |
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
Data table: Earth-Bench V2
| Model | Developer | Earth-Bench V2 |
|---|---|---|
| DeepSeek V4 Pro (high), Terminus-2 | DeepSeek | 68 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 68 |
| GLM-5.2 (high), Terminus-2 | Z.ai | 68 |
| GLM-5.2 (max), Terminus-2 | Z.ai | 68 |
| Kimi K2.6 (Reasoning), Terminus-2 | Moonshot AI | 68 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 68 |
| Claude Haiku 5.5 (max), Claude Code | Anthropic | 67 |
GeoBenchX
higher is better
Data table: GeoBenchX
| Model | Developer | GeoBenchX |
|---|---|---|
| Gemini 3.1 Flash-Lite (high), Terminus-2 | 61 | |
| GLM-5.2 (Non-reasoning), Terminus-2 | Z.ai | 60 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 59 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 59 |
| GLM-5.3-Flash (low), Terminus-2 | Z.ai | 58 |
| GLM-5.3-Flash (max), Terminus-2 | Z.ai | 57 |
| GPT-6 Luna (medium), Codex | OpenAI | 57 |
GS-QA2
higher is better
Data table: GS-QA2
| Model | Developer | GS-QA2 |
|---|---|---|
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 79 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 79 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 79 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 79 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 78 |
| GLM-5.2 (high), Terminus-2 | Z.ai | 78 |
| GPT-6 Luna (xhigh), Codex | OpenAI | 78 |
GeoAgentBench
higher is better
Data table: GeoAgentBench
| Model | Developer | GeoAgentBench |
|---|---|---|
| DeepSeek V4 Flash (high), Terminus-2 | DeepSeek | 94 |
| GLM-5.2 (high), Terminus-2 | Z.ai | 94 |
| GLM-5.2 (max), Terminus-2 | Z.ai | 94 |
| GLM-5.3-Flash (low), Terminus-2 | Z.ai | 94 |
| GLM-5.3 (max), Terminus-2 | Z.ai | 94 |
| Kimi K3 (Reasoning), Terminus-2 | Moonshot AI | 94 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 94 |
GeoAnalystBench
higher is better
Data table: GeoAnalystBench
| Model | Developer | GeoAnalystBench |
|---|---|---|
| GLM-5.3-Flash (max), Terminus-2 | Z.ai | 84 |
| GLM-5.3 (max), Terminus-2 | Z.ai | 84 |
| MiMo V2.6 Flash (Reasoning), MiMo Code | Xiaomi | 84 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 79 |
| Kimi K2.6 (Reasoning), Terminus-2 | Moonshot AI | 79 |
| Qwen 3.8 27B (Reasoning), Qwen Code | Alibaba (Qwen) | 79 |
| GPT-6 Luna (low), Codex | OpenAI | 79 |
OGD4All
higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Muse Glimmer 30B (high), Terminus-2 | Meta | 95 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 95 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 95 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 95 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 95 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 94 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 94 |
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 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 93 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 93 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 92 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 88 |
MultiGlobeQA
higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 74 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 74 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 73 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 73 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 73 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 73 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 73 |
GeoAgentEval
higher is better
Data table: GeoAgentEval
| Model | Developer | GeoAgentEval |
|---|---|---|
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 92 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 92 |
| Muse Glimmer 30B (high), Terminus-2 | Meta | 92 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 92 |
| DeepSeek V4.1 Flash (max), Claude Code | DeepSeek | 92 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 92 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 92 |
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 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
| Model | Developer | Cost per task |
|---|---|---|
| Gemini 3.8 Flash (low), Terminus-2 | $0.11 | |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | $0.12 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | $0.13 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | $0.13 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | $0.13 |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | $0.15 |
| Kimi K2.7 Code (Reasoning), Kimi Code | Moonshot AI | $0.18 |
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 |
|---|---|---|---|
| MiniMax M3 (Reasoning), MiniMax Code | 68 | $0.15 | No |
| GLM-5.3 (low), Terminus-2 | 68 | $0.27 | No |
| Nemotron 3 Ultra (high), Terminus-2 | 68 | $0.067 | No |
| Claude Sonnet 5.5 (low), Claude Code | 69 | $0.043 | Yes |
| GPT-5.6 Terra (high), Codex | 69 | $0.10 | No |
| Qwen 3.8 27B (Reasoning), Qwen Code | 69 | $0.22 | No |
| DeepSeek V4 Flash (max), Terminus-2 | 69 | $0.062 | 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 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
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 4.6 min |
| Qwen 3.8 27B (Reasoning), Qwen Code | Alibaba (Qwen) | 4.7 min |
| GLM-5.2 (high), Terminus-2 | Z.ai | 4.7 min |
| gpt-oss-20b (high), Codex | OpenAI | 4.8 min |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 4.8 min |
| MiniMax M3 (Reasoning), MiniMax Code | MiniMax | 4.9 min |
| DeepSeek V4 Pro (max), Terminus-2 | DeepSeek | 4.9 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 |
|---|---|---|---|
| MiniMax M3 (Reasoning), MiniMax Code | 68 | 4.9 min | No |
| GLM-5.3 (low), Terminus-2 | 68 | 2.7 min | No |
| Nemotron 3 Ultra (high), Terminus-2 | 68 | 2.4 min | No |
| Claude Sonnet 5.5 (low), Claude Code | 69 | 0.4 min | Yes |
| GPT-5.6 Terra (high), Codex | 69 | 0.8 min | Yes |
| Qwen 3.8 27B (Reasoning), Qwen Code | 69 | 4.7 min | Yes |
| DeepSeek V4 Flash (max), Terminus-2 | 69 | 5.7 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| MiniMax M3 (Reasoning), MiniMax Code | Ranked | 68 | 59 | 78 | 94 | 79 | 94 | 93 | 73 | 92 |
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.
