Axis Spatial

Claude Code – Claude Haiku 5.5 (high) vs. Codex – GPT-6.1 Sol (high): geospatial agent comparison

Comparison between Claude Code – Claude Haiku 5.5 (high) and Codex – GPT-6.1 Sol (high) on the Geospatial Agent Index, each benchmark, cost, time and token use, from the same tasks, agent and limits. Methodology.

Side by side

Claude Code – Claude Haiku 5.5 (high) vs. Codex – GPT-6.1 Sol (high)
MetricClaude Code – Claude Haiku 5.5 (high)Codex – GPT-6.1 Sol (high)
Geospatial Agent Index75* (partial: 1.7 of 3 attempts)80* (partial: 2.8 of 3 attempts)
GeoAgentBench score98100
GeoBenchX score6166
Earth-Bench score5153
GeoAnalystBench score89100
Raster and remote sensing score6972
Vector and overlay score7578
Networks and routing score100100
Climate and time series score7976
Spatial statistics and interpolation score5861
Mapping and cartography score7576
Recognising infeasible requests score5668
Cost per task$0.010$0.080
Time per task1.1 min1.8 min
Tokens per attempt328k143k
Turns per attempt8.17.8
Attempts decided422753
Input price per 1M tokens$0.10$2.00
Output price per 1M tokens$0.50$10.00
Context windowNo dataNo data
Image inputNo dataNo data
DeveloperAnthropicOpenAI

Charts

Geospatial Agent Index

Equal-weight average of the evaluation scores, 0 to 100 · Higher is better

  • Partial coverage
Geospatial Agent IndexCodex – GPT-6.1 Sol (high): 80* (partial coverage); Claude Code – Claude Haiku 5.5 (high): 75* (partial coverage)025507510080*GPT-6.1 Sol (high)75*Claude Haiku 5.5 (high)
Data table: Geospatial Agent Index
Geospatial Agent Index
ModelCreatorGeospatial Agent IndexRange while attempts are pendingBenchmarks coveredCoverage (attempts)
Codex – GPT-6.1 Sol (high)OpenAI80* (partial: 2.8 of 3 attempts)72 to 804 of 4753 of 870 planned attempts
Claude Code – Claude Haiku 5.5 (high)Anthropic75* (partial: 1.7 of 3 attempts)66 to 774 of 4422 of 870 planned attempts

Cost per task

Average cost per task (USD) · Lower is better

  • Partial coverage
Cost per taskClaude Code – Claude Haiku 5.5 (high): $0.010 (partial coverage); Codex – GPT-6.1 Sol (high): $0.080 (partial coverage)$0$0.020$0.040$0.060$0.080$0.010Claude Haiku 5.5 (high)$0.080GPT-6.1 Sol (high)
Data table: Cost per task
Cost per task
ModelCreatorCost per taskCoverage (attempts)
Claude Code – Claude Haiku 5.5 (high)Anthropic$0.010485 of 870 planned attempts
Codex – GPT-6.1 Sol (high)OpenAI$0.080813 of 870 planned attempts

Time per task

Average agent wall time per task · Lower is better

  • Partial coverage
Time per taskClaude Code – Claude Haiku 5.5 (high): 1.1 min (partial coverage); Codex – GPT-6.1 Sol (high): 1.8 min (partial coverage)0m0m1m2m2m1.1 minClaude Haiku 5.5 (high)1.8 minGPT-6.1 Sol (high)
Data table: Time per task
Time per task
ModelCreatorTime per taskCoverage (attempts)
Claude Code – Claude Haiku 5.5 (high)Anthropic1.1 min485 of 870 planned attempts
Codex – GPT-6.1 Sol (high)OpenAI1.8 min813 of 870 planned attempts

Frequently asked questions

Which scores higher on the Geospatial Agent Index, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6.1 Sol (high)?
Codex – GPT-6.1 Sol (high), at 80 against 75 for Claude Code – Claude Haiku 5.5 (high). Claude Code – Claude Haiku 5.5 (high): 422 of 870 planned attempts. Codex – GPT-6.1 Sol (high): 753 of 870 planned attempts. Coverage is partial; scores can change as more attempts are decided.
Which is cheaper per task, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6.1 Sol (high)?
Claude Code – Claude Haiku 5.5 (high), at $0.010 against $0.080 for Codex – GPT-6.1 Sol (high).
Which is faster per task, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6.1 Sol (high)?
Claude Code – Claude Haiku 5.5 (high), at 1.1 min against 1.8 min for Codex – GPT-6.1 Sol (high).
Which has the larger context window, Claude Code – Claude Haiku 5.5 (high) or Codex – GPT-6.1 Sol (high)?
Not known yet: Claude Code – Claude Haiku 5.5 (high) has no context window data and Codex – GPT-6.1 Sol (high) has no context window data.

More: Claude Code – Claude Haiku 5.5 (high) · Codex – GPT-6.1 Sol (high) · all comparisons