Anthropic: geospatial agent results
Helps you choose the best Anthropic model for geospatial work: GIS and Earth observation agent tasks, vision and knowledge, with cost and time.
Overview
Most intelligent
- #1Claude Opus 5.5 (xhigh)89
- #2Claude Opus 5.5 (high)87
- #3Claude Opus 5.5 (medium)85
- #4Claude Opus 5 (high)84
- #5Claude Sonnet 5.5 (xhigh)84
Geospatial Intelligence Index, Text only
Lowest cost
- #1Claude Haiku 5.5 (low), Claude Code$0.0053
- #2Claude Haiku 5.5 (medium), Claude Code$0.0063
- #3Claude Haiku 5.5 (high), Claude Code$0.0085
- #4Claude Haiku 5.5 (xhigh), Claude Code$0.017
- #5Claude Sonnet 5.5 (low), Claude Code$0.043
Cost per agent task, at list prices
Fastest
- #1Claude Opus 5.5 (low), Claude Code0.4 min
- #2Claude Sonnet 5.5 (medium), Claude Code0.4 min
- #3Claude Sonnet 5.5 (low), Claude Code0.4 min
- #4Claude Opus 5.5 (medium), Claude Code0.5 min
- #5Claude Sonnet 5.5 (high), Claude Code0.6 min
Time per agent task, at 100 output tokens/s
- For intelligence, the top Anthropic models are Claude Opus 5.5 (xhigh) (89), Claude Opus 5.5 (high) (87) and Claude Opus 5.5 (medium) (85) on the Geospatial Intelligence Index (Text only).
- For cost, the cheapest per agent task are Claude Haiku 5.5 (low) ($0.0053), Claude Haiku 5.5 (medium) ($0.0063) and Claude Haiku 5.5 (high) ($0.0085).
- For speed, the fastest per agent task are Claude Opus 5.5 (low) (0.4 min), Claude Sonnet 5.5 (medium) (0.4 min) and Claude Sonnet 5.5 (low) (0.4 min).
- For context window, the largest are Claude Sonnet 4.6: 1M tokens, Claude Opus 5.5: 1M tokens and Claude Sonnet 5.5: 1M tokens.
- Still being benchmarked (provisional): Claude Sonnet 4.6 (high).
Intelligence
Geospatial Intelligence Index, Text only · Higher is better
Data table: Intelligence
| Model | Developer | Intelligence |
|---|---|---|
| Claude Opus 5.5 (xhigh) | Anthropic | 89 |
| Claude Opus 5.5 (high) | Anthropic | 87 |
| Claude Opus 5.5 (medium) | Anthropic | 85 |
| Claude Opus 5 (high) | Anthropic | 84 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 84 |
| Claude Sonnet 5.5 (high) | Anthropic | 84 |
| Claude Opus 4.8 (high) | Anthropic | 83 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 82 |
| Claude Opus 5.5 (low) | Anthropic | 82 |
| Claude Haiku 5.5 (high) | Anthropic | 81 |
| Claude Sonnet 5 (high) | Anthropic | 81 |
| Claude Opus 4.7 (high) | Anthropic | 81 |
| Claude Haiku 5.5 (medium) | Anthropic | 79 |
| Claude Sonnet 5.5 (medium) | Anthropic | 79 |
| Claude Haiku 5.5 (low) | Anthropic | 79 |
| Claude Sonnet 5.5 (low) | Anthropic | 78 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 66 |
Cost per task
USD per agent task · Lower is better
Data table: Cost per task
| Model | Developer | Cost per task |
|---|---|---|
| Claude Haiku 5.5 (low), Claude Code | Anthropic | $0.0053 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | $0.0063 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | $0.0085 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | $0.017 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | $0.043 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | $0.046 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | $0.055 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | $0.081 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | $0.092 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | $0.11 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | $0.11 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | $0.13 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | $0.20 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | $0.25 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | $0.26 |
| Claude Opus 5 (high), Claude Code | Anthropic | $0.32 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | $0.34 |
Time per task
Minutes per agent task · Lower is better
Data table: Time per task
| Model | Developer | Time per task (at 100 output tokens/s) |
|---|---|---|
| Claude Opus 5.5 (low), Claude Code | Anthropic | 0.4 min |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 0.4 min |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 0.4 min |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 0.5 min |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 0.6 min |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 0.7 min |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 0.9 min |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 0.9 min |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 0.9 min |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 1.0 min |
| Claude Opus 5 (high), Claude Code | Anthropic | 1.1 min |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 1.2 min |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 1.4 min |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 1.4 min |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 1.5 min |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 1.6 min |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 2.5 min |
Models
Claude Opus 5.5
Closed weightsReads images
1M token context · Released 22 September 2026
$4.00 input · $20.00 output per 1M tokens
Claude Opus 5
Closed weightsReads images
1M token context · Released 24 July 2026
$5.00 input · $25.00 output per 1M tokens
Claude Sonnet 5.5
Closed weightsReads images
1M token context · Released 28 September 2026
$2.00 input · $10.00 output per 1M tokens
Claude Opus 4.8
Closed weightsReads images
1M token context · Released 28 May 2026
$5.00 input · $25.00 output per 1M tokens
Claude Haiku 5.5
Closed weightsReads images
1M token context · Released 7 October 2026
$0.10 input · $0.50 output per 1M tokens
Claude Sonnet 5
Closed weightsReads images
1M token context · Released 30 June 2026
$2.00 input · $10.00 output per 1M tokens
Claude Opus 4.7
Closed weightsReads images
1M token context · Released 16 April 2026
$5.00 input · $25.00 output per 1M tokens
Claude Haiku 4.5
Closed weightsReads images
200K token context · Released 15 October 2025
$1.00 input · $5.00 output per 1M tokens
Claude Sonnet 4.6
Closed weightsReads images
1M token context · Released 17 February 2026
$3.00 input · $15.00 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
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 Anthropic 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
| Model | Developer | Geospatial Intelligence Index: Text only |
|---|---|---|
| Claude Opus 5.5 (xhigh) | Anthropic | 89 |
| Claude Opus 5.5 (high) | Anthropic | 87 |
| GPT-6.1 Sol (medium) | OpenAI | 86 |
| Claude Opus 5.5 (medium) | Anthropic | 85 |
| Claude Opus 5 (high) | Anthropic | 84 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 84 |
| Claude Sonnet 5.5 (high) | Anthropic | 84 |
| Claude Opus 4.8 (high) | Anthropic | 83 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 82 |
| Claude Opus 5.5 (low) | Anthropic | 82 |
| Claude Haiku 5.5 (high) | Anthropic | 81 |
| Claude Sonnet 5 (high) | Anthropic | 81 |
| Claude Opus 4.7 (high) | Anthropic | 81 |
| Claude Haiku 5.5 (medium) | Anthropic | 79 |
| DeepSeek V4 Pro (max) | DeepSeek | 79 |
| Claude Sonnet 5.5 (medium) | Anthropic | 79 |
| Gemini 3.8 Flash (low) | 79 | |
| Claude Haiku 5.5 (low) | Anthropic | 79 |
| Claude Sonnet 5.5 (low) | Anthropic | 78 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 66 |
Text + vision
Geospatial Intelligence Index: Text + vision
Weighted average of agent tasks, vision, guided workflows and geospatial knowledge, 0 to 100 · Higher is better
How to read this chart
What this metric means. A weighted average of group scores, each 0 to 100: Agent tasks 4/8, Vision 2/8, Guided workflows 1/8, Geospatial knowledge 1/8.
Bars. Every Anthropic 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 + vision
| Model | Developer | Geospatial Intelligence Index: Text + vision |
|---|---|---|
| Claude Opus 5.5 (xhigh) | Anthropic | 83 |
| Claude Opus 5.5 (high) | Anthropic | 81 |
| GPT-6.1 Sol (xhigh) | OpenAI | 80 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 79 |
| Claude Sonnet 5.5 (high) | Anthropic | 78 |
| Claude Opus 5 (high) | Anthropic | 77 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 75 |
| Claude Opus 4.8 (high) | Anthropic | 74 |
| Claude Sonnet 5 (high) | Anthropic | 73 |
| Claude Opus 4.7 (high) | Anthropic | 73 |
| Claude Haiku 5.5 (high) | Anthropic | 72 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 53 |
Provisional, so not on these charts: Claude Sonnet 4.6 (high). Their finished evaluations are in the charts below.
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 Anthropic 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 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 82 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 82 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 80 |
| Claude Opus 5 (high), Claude Code | Anthropic | 80 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 79 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 78 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 76 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 76 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 76 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 74 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 74 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 74 |
| GLM-5.3 (max), Claude Code | Z.ai | 73 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 72 |
| Kimi K3 (Reasoning), Terminus-2 | Moonshot AI | 72 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 71 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 70 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 69 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 58 |
Text + vision
Geospatial Agent Index: Text + vision
Equal average of Earth-Bench V2, GeoBenchX, GS-QA2 and Vision agents, 0 to 100 · Higher is better
How to read this chart
Bars. Every Anthropic 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 + vision
| Model | Developer | Geospatial Agent Index: Text + vision |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 88 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 84 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 84 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 82 |
| Claude Opus 5 (high), Claude Code | Anthropic | 81 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 78 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 78 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 77 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 76 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 75 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 74 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 74 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 73 |
| DeepSeek V4.1 Flash (max), Codex | DeepSeek | 73 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 72 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 71 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 48 |
Provisional, so not on the index charts: Claude Sonnet 4.6 (high), Terminus-2. Their finished evaluations are in the charts below.
Earth-Bench V2
Earth observation data analysis · 0 to 100 · Higher is better
- Striped: provisional, not yet ranked
Data table: Earth-Bench V2
| Model | Developer | Earth-Bench V2 |
|---|---|---|
| Claude Opus 5.5 (high), Claude Code | Anthropic | 79 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 77 |
| GPT-6 Sol (high), Codex | OpenAI | 75 |
| GLM-5.3 (max), Claude Code | Z.ai | 74 |
| Claude Opus 5 (high), Claude Code | Anthropic | 74 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 72 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 72 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 71 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 71 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 69 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 68 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 68 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 68 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 68 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 62 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 59 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 58 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 56 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 51 |
| Claude Sonnet 4.6 (high), Terminus-2 | Anthropic | 59 |
GeoBenchX
Maps, numbers and lists from country and city data; some tasks should be declined · 0 to 100 · Higher is better
Data table: GeoBenchX
| Model | Developer | GeoBenchX |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 83 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 81 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 81 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 79 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 79 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 79 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 77 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 77 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 77 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 77 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 76 |
| Claude Opus 5 (high), Claude Code | Anthropic | 75 |
| Gemini 3.8 Flash (low), Terminus-2 | 74 | |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 73 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 73 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 73 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 72 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 71 |
| Claude Sonnet 4.6 (high), Terminus-2 | Anthropic | 57 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 53 |
GS-QA2
Spatial data reasoning over map and elevation data · 0 to 100 · 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 |
| Claude Opus 5 (high), Claude Code | Anthropic | 91 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 90 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 90 |
| DeepSeek V4.1 Flash (max), Codex | DeepSeek | 89 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 87 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 86 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 84 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 83 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 83 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 81 |
| 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 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 77 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 76 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 75 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 69 |
GeoAgentBench
Guided workflows: multi-step spatial execution · 0 to 100 · Higher is better
Data table: GeoAgentBench
| Model | Developer | GeoAgentBench |
|---|---|---|
| Claude Opus 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Opus 5 (high), Claude Code | Anthropic | 100 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 100 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 98 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 98 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 98 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 98 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 98 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 98 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 98 |
| Gemini 3.8 Flash (low), Terminus-2 | 98 | |
| Claude Sonnet 4.6 (high), Terminus-2 | Anthropic | 96 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 96 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 96 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 96 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 94 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 94 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 92 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 78 |
GeoAnalystBench
Guided workflows: spatial workflow and code generation · 0 to 100 · Higher is better
Data table: GeoAnalystBench
| Model | Developer | GeoAnalystBench |
|---|---|---|
| Claude Sonnet 4.6 (high), Terminus-2 | Anthropic | 100 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 100 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 100 |
| DeepSeek V4 Pro (max), Terminus-2 | DeepSeek | 100 |
| GLM-5.2 (high), Terminus-2 | Z.ai | 100 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 95 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 95 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 95 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 95 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 95 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 95 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 95 |
| Claude Opus 5 (high), Claude Code | Anthropic | 89 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 84 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 74 |
OGD4All
Not in an index: questions on a city's open government data · 0 to 100 · Higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Claude Opus 4.8 (high), Claude Code | Anthropic | 97 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 97 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 97 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 97 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 96 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 96 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 96 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 96 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 96 |
| GLM-5.3 (max), Claude Code | Z.ai | 96 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 95 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 95 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 95 |
| Claude Opus 5 (high), Claude Code | Anthropic | 95 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 95 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 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 |
GeoNatureAgent
Not in an index: environmental raster analysis over named regions · 0 to 100 · Higher is better
Data table: GeoNatureAgent
| Model | Developer | GeoNatureAgent |
|---|---|---|
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 100 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Opus 5 (high), Claude Code | Anthropic | 100 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 100 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 100 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 100 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 100 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 100 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 93 |
MultiGlobeQA
Not in an index: spatial reasoning over facts about places worldwide · 0 to 100 · Higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 83 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 82 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 79 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 79 |
| Claude Opus 5 (high), Claude Code | Anthropic | 79 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 78 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 78 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 78 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 77 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 77 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 75 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 75 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 75 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 74 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 74 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 73 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 73 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 72 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 70 |
GeoAgentEval
Not in an index: building, auditing and repairing a PostGIS database · 0 to 100 · Higher is better
Data table: GeoAgentEval
| Model | Developer | GeoAgentEval |
|---|---|---|
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 96 |
| Claude Opus 5 (high), Claude Code | Anthropic | 96 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 96 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 95 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 92 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 92 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 92 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 92 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 92 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 92 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 92 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 88 |
Vision
ThinkGeo
Vision agents: agents that look at images and use code · 0 to 100 · Higher is better
Data table: ThinkGeo
| Model | Developer | ThinkGeo |
|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 96 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 89 |
| GPT-6.1 Sol (xhigh), Codex | OpenAI | 88 |
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 87 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 87 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 85 |
| Claude Opus 5 (high), Claude Code | Anthropic | 81 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 81 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 77 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 74 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 74 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 74 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 72 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 70 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 70 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 70 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 68 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 62 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 17 |
OpenEarthAgent
Vision agents: agents that look at images and use code · 0 to 100 · Higher is better
Data table: OpenEarthAgent
| Model | Developer | OpenEarthAgent |
|---|---|---|
| Claude Opus 5.5 (medium), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (medium), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (xhigh), Claude Code | Anthropic | 100 |
| Claude Opus 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Sonnet 5.5 (low), Claude Code | Anthropic | 100 |
| Claude Opus 5 (high), Claude Code | Anthropic | 100 |
| GPT-6.1 Sol (medium), Codex | OpenAI | 100 |
| Claude Opus 5.5 (high), Claude Code | Anthropic | 94 |
| Claude Haiku 5.5 (medium), Claude Code | Anthropic | 94 |
| Claude Haiku 5.5 (high), Claude Code | Anthropic | 94 |
| Claude Haiku 5.5 (xhigh), Claude Code | Anthropic | 94 |
| Claude Haiku 5.5 (low), Claude Code | Anthropic | 94 |
| DeepSeek V4.1 Flash (max), Codex | DeepSeek | 94 |
| Claude Sonnet 5.5 (high), Claude Code | Anthropic | 88 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Anthropic | 88 |
| Claude Opus 4.8 (high), Claude Code | Anthropic | 88 |
| Claude Opus 4.7 (high), Claude Code | Anthropic | 88 |
| Claude Sonnet 5 (high), Claude Code | Anthropic | 82 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Anthropic | 24 |
Earth-Bench V2 images
Questions on images, no tools · 0 to 100 · Higher is better
Data table: Earth-Bench V2 images
| Model | Developer | Earth-Bench V2 images |
|---|---|---|
| GPT-5.6 Sol (high) | OpenAI | 76 |
| Claude Opus 5.5 (high) | Anthropic | 74 |
| Claude Opus 5.5 (xhigh) | Anthropic | 70 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 70 |
| Claude Opus 5.5 (medium) | Anthropic | 70 |
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 69 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 68 |
| Claude Sonnet 5.5 (high) | Anthropic | 68 |
| Claude Opus 5 (high) | Anthropic | 68 |
| Claude Opus 4.7 (high) | Anthropic | 68 |
| Claude Sonnet 5.5 (medium) | Anthropic | 68 |
| Claude Opus 4.8 (high) | Anthropic | 66 |
| Claude Sonnet 5 (high) | Anthropic | 66 |
| Claude Haiku 5.5 (high) | Anthropic | 60 |
| Claude Haiku 5.5 (medium) | Anthropic | 56 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 50 |
ThinkGeo images
Questions on images, no tools · 0 to 100 · Higher is better
Data table: ThinkGeo images
| Model | Developer | ThinkGeo images |
|---|---|---|
| Claude Sonnet 5.5 (high) | Anthropic | 50 |
| Claude Opus 5.5 (xhigh) | Anthropic | 48 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 47 |
| Claude Opus 5.5 (high) | Anthropic | 44 |
| Claude Opus 5.5 (medium) | Anthropic | 44 |
| Claude Sonnet 5.5 (medium) | Anthropic | 43 |
| GPT-6 Luna (high) | OpenAI | 43 |
| Claude Opus 5 (high) | Anthropic | 38 |
| Qwen 3.8 27B (xhigh) | Alibaba (Qwen) | 37 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 35 |
| Claude Haiku 5.5 (high) | Anthropic | 35 |
| Claude Haiku 5.5 (medium) | Anthropic | 35 |
| Claude Opus 4.8 (high) | Anthropic | 34 |
| Claude Sonnet 5 (high) | Anthropic | 28 |
| Claude Opus 4.7 (high) | Anthropic | 27 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 19 |
GeoChrono
Not in an index: change over time in aerial image series · 0 to 100 · Higher is better
Data table: GeoChrono
| Model | Developer | GeoChrono |
|---|---|---|
| Claude Sonnet 5.5 (xhigh) | Anthropic | 76 |
| Claude Opus 5.5 (xhigh) | Anthropic | 74 |
| Claude Sonnet 5.5 (high) | Anthropic | 74 |
| Claude Opus 5.5 (high) | Anthropic | 73 |
| Claude Opus 5 (high) | Anthropic | 71 |
| Claude Haiku 5.5 (high) | Anthropic | 68 |
| Claude Opus 4.8 (high) | Anthropic | 59 |
| Claude Sonnet 5 (high) | Anthropic | 55 |
| Claude Opus 4.7 (high) | Anthropic | 54 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 36 |
BigEarthNet.txt
Not in an index: questions on Sentinel-2 optical and Sentinel-1 radar patches · 0 to 100 · Higher is better
Data table: BigEarthNet.txt
| Model | Developer | BigEarthNet.txt |
|---|---|---|
| Claude Opus 5.5 (high) | Anthropic | 60 |
| Claude Opus 5.5 (xhigh) | Anthropic | 58 |
| Claude Opus 5.5 (medium) | Anthropic | 58 |
| Claude Sonnet 5.5 (high) | Anthropic | 57 |
| Claude Sonnet 5.5 (medium) | Anthropic | 56 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 56 |
| Claude Haiku 5.5 (high) | Anthropic | 54 |
| Claude Opus 5 (high) | Anthropic | 53 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 52 |
| Claude Haiku 5.5 (medium) | Anthropic | 50 |
| Claude Opus 4.7 (high) | Anthropic | 49 |
| Claude Opus 4.8 (high) | Anthropic | 48 |
| Claude Sonnet 5 (high) | Anthropic | 47 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 45 |
Knowledge
Geospatial knowledge
Questions on geospatial knowledge · 0 to 100 · Higher is better
Data table: Geospatial knowledge
| Model | Developer | Geospatial knowledge |
|---|---|---|
| Claude Sonnet 4.6 (high) | Anthropic | 98 |
| Claude Opus 5.5 (low) | Anthropic | 98 |
| Claude Sonnet 5.5 (low) | Anthropic | 98 |
| Claude Haiku 5.5 (low) | Anthropic | 96 |
| Claude Opus 5.5 (high) | Anthropic | 95 |
| Claude Opus 5.5 (xhigh) | Anthropic | 94 |
| Claude Sonnet 5.5 (xhigh) | Anthropic | 94 |
| Claude Opus 5.5 (medium) | Anthropic | 94 |
| Gemini 3.8 Flash (low) | 94 | |
| Claude Sonnet 5.5 (high) | Anthropic | 94 |
| Claude Sonnet 5.5 (medium) | Anthropic | 93 |
| Claude Opus 4.8 (high) | Anthropic | 93 |
| Claude Opus 5 (high) | Anthropic | 93 |
| Claude Opus 4.7 (high) | Anthropic | 93 |
| GPT-6.1 Sol (high) | OpenAI | 93 |
| Claude Haiku 5.5 (high) | Anthropic | 92 |
| Claude Haiku 5.5 (xhigh) | Anthropic | 92 |
| Claude Haiku 5.5 (medium) | Anthropic | 92 |
| Claude Sonnet 5 (high) | Anthropic | 91 |
| Claude Haiku 4.5 (Reasoning) | Anthropic | 89 |
Cost
Cost per task
Average cost per agent task (USD) · Lower is better
- Striped: provisional, not yet ranked
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 Anthropic 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
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
Time
Time per task
Average minutes per agent task, at 100 output tokens/s · Lower is better
- Striped: provisional, not yet ranked
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 Anthropic 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
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
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 | ThinkGeo | OpenEarthAgent | Earth-Bench V2 images | ThinkGeo images | GeoChrono | BigEarthNet.txt | Geospatial knowledge |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Claude Opus 5.5 (xhigh), Claude Code | Ranked | 77 | 83 | 94 | 98 | 100 | 96 | 100 | 79 | 100 | 96 | 100 | 70 | 48 | 74 | 58 | 94 |
| Claude Opus 5.5 (high), Claude Code | Ranked | 79 | 79 | 90 | 100 | 100 | 95 | 100 | 78 | 100 | 87 | 94 | 74 | 44 | 73 | 60 | 95 |
| Claude Opus 5.5 (medium), Claude Code | Ranked | 72 | 79 | 90 | 98 | 95 | 97 | 100 | 78 | 100 | 87 | 100 | 70 | 44 | No data | 58 | 94 |
| Claude Opus 5 (high), Claude Code | Ranked | 74 | 75 | 91 | 100 | 89 | 95 | 100 | 79 | 96 | 81 | 100 | 68 | 38 | 71 | 53 | 93 |
| Claude Sonnet 5.5 (xhigh), Claude Code | Ranked | 68 | 81 | 87 | 98 | 95 | 97 | 100 | 79 | 100 | 89 | 88 | 68 | 47 | 76 | 56 | 94 |
| Claude Sonnet 5.5 (high), Claude Code | Ranked | 71 | 81 | 83 | 98 | 100 | 96 | 100 | 77 | 100 | 74 | 88 | 68 | 50 | 74 | 57 | 94 |
| Claude Opus 4.8 (high), Claude Code | Ranked | 71 | 73 | 84 | 96 | 100 | 97 | 100 | 75 | 92 | 68 | 88 | 66 | 34 | 59 | 48 | 93 |
| Claude Haiku 5.5 (xhigh), Claude Code | Ranked | 68 | 77 | 81 | 100 | 95 | 97 | 100 | 75 | 92 | 74 | 94 | 70 | 35 | No data | 52 | 92 |
| Claude Haiku 5.5 (high), Claude Code | Ranked | 72 | 72 | 83 | 98 | 84 | 96 | 100 | 74 | 92 | 62 | 94 | 60 | 35 | 68 | 54 | 92 |
| Claude Sonnet 5 (high), Claude Code | Ranked | 68 | 77 | 77 | 98 | 95 | 95 | 100 | 73 | 92 | 81 | 82 | 66 | 28 | 55 | 47 | 91 |
| Claude Opus 5.5 (low), Claude Code | Ranked | 59 | 77 | 86 | 96 | 100 | 95 | 100 | 78 | 100 | 85 | 100 | No data | No data | No data | No data | 98 |
| Claude Opus 4.7 (high), Claude Code | Ranked | 69 | 73 | 79 | 96 | 100 | 95 | 100 | 77 | 92 | 72 | 88 | 68 | 27 | 54 | 49 | 93 |
| Claude Haiku 5.5 (medium), Claude Code | Ranked | 68 | 71 | 79 | 94 | 95 | 95 | 100 | 73 | 92 | 70 | 94 | 56 | 35 | No data | 50 | 92 |
| Claude Sonnet 5.5 (medium), Claude Code | Ranked | 58 | 76 | 79 | 98 | 95 | 96 | 100 | 74 | 96 | 77 | 100 | 68 | 43 | No data | 56 | 93 |
| Claude Haiku 5.5 (low), Claude Code | Ranked | 62 | 73 | 75 | 92 | 100 | 95 | 100 | 72 | 92 | 70 | 94 | No data | No data | No data | No data | 96 |
| Claude Sonnet 5.5 (low), Claude Code | Ranked | 56 | 77 | 76 | 94 | 95 | 96 | 100 | 75 | 100 | 70 | 100 | No data | No data | No data | No data | 98 |
| Claude Haiku 4.5 (Reasoning), Claude Code | Ranked | 51 | 53 | 69 | 78 | 74 | 95 | 93 | 70 | 88 | 17 | 24 | 50 | 19 | 36 | 45 | 89 |
| Claude Sonnet 4.6 (high), Terminus-2 | Provisional | 59 (provisional) | 57 | No data | 96 | 100 | No data | No data | No data | No data | No data | No data | No data | No data | No data | No data | 98 |
