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Codex: geospatial agent results

The command-line coding agent made by OpenAI. 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 Codex on GIS and Earth observation tasks, and shows how the same model does in other harnesses.

Made by OpenAI · Version 0.162.0, pinned · 11 models from 2 labs · 21 entries, 18 ranked

Overview

Lowest cost

  1. #1GPT-6 Luna (medium)$0.0026
  2. #2GPT-6 Luna (low)$0.0032
  3. #3GPT-6 Luna (high)$0.0040
  4. #4GPT-5.6 Luna (low)$0.0058
  5. #5GPT-6 Luna (xhigh)$0.0070

Cost per agent task, at list prices

Fastest

  1. #1GPT-6.1 Sol (medium)0.4 min
  2. #2GPT-6 Luna (medium)0.4 min
  3. #3GPT-6 Luna (low)0.5 min
  4. #4GPT-6.1 Sol (high)0.5 min
  5. #5GPT-5.6 Luna (low)0.5 min

Time per agent task, at 100 output tokens/s

  • In Codex, the best entry is GPT-6.1 Sol (xhigh), 4th of 83 entries on the Geospatial Agent Index, at 81.8.
  • 5 of the models in Codex also run in another harness at the same reasoning level. Against the best other harness, Codex scores lower for 2 and within noise for 3.
  • Still being benchmarked (provisional): DeepSeek V4 Pro (high), GPT-5.6 Sol (medium), GPT-6 Sol (medium).

Models

One per model

Geospatial Agent Index: every model in Codex

Text only, 0 to 100 · Higher is better · one bar per model, at its best ranked reasoning level

Geospatial Agent Index: every model in CodexScores 0 to 100: GPT-6.1 Sol (xhigh), Codex: 82; GPT-6 Sol (high), Codex: 80; DeepSeek V4.1 Flash (max), Codex: 78; GPT-5.6 Sol (high), Codex: 76; GPT-5.6 Luna (xhigh), Codex: 73; GPT-6 Luna (xhigh), Codex: 73; GPT-5.6 Terra (high), Codex: 69; DeepSeek V4 Flash (high), Codex: 66; gpt-oss-120b (high), Codex: 43; gpt-oss-20b (high), Codex: 90255075100GPT-6.1 Sol (xhigh)Codex82GPT-6 Sol (high)Codex80DeepSeek V4.1 Flash (max)Codex78GPT-5.6 Sol (high)Codex76GPT-5.6 Luna (xhigh)Codex73GPT-6 Luna (xhigh)Codex73GPT-5.6 Terra (high)Codex69DeepSeek V4 Flash (high)Codex66gpt-oss-120b (high)Codex43gpt-oss-20b (high)Codex9
How to read this chart

Bars. Every entry in Codex with a result, highlighted. Provisional: not yet ranked.

Data table: Geospatial Agent Index: every model in Codex
Geospatial Agent Index: every model in Codex
ModelDeveloperGeospatial Agent Index: every model in Codex
GPT-6.1 Sol (xhigh), CodexOpenAI82
GPT-6 Sol (high), CodexOpenAI80
DeepSeek V4.1 Flash (max), CodexDeepSeek78
GPT-5.6 Sol (high), CodexOpenAI76
GPT-5.6 Luna (xhigh), CodexOpenAI73
GPT-6 Luna (xhigh), CodexOpenAI73
GPT-5.6 Terra (high), CodexOpenAI69
DeepSeek V4 Flash (high), CodexDeepSeek66
gpt-oss-120b (high), CodexOpenAI43
gpt-oss-20b (high), CodexOpenAI9

Every reasoning level

Geospatial Agent Index: every entry in Codex

Text only, 0 to 100 · Higher is better · every reasoning level

Geospatial Agent Index: every entry in CodexScores 0 to 100: GPT-6.1 Sol (xhigh), Codex: 82; GPT-6.1 Sol (medium), Codex: 82; GPT-6.1 Sol (high), Codex: 80; GPT-6 Sol (high), Codex: 80; DeepSeek V4.1 Flash (max), Codex: 78; GPT-5.6 Sol (high), Codex: 76; GPT-5.6 Luna (xhigh), Codex: 73; GPT-6 Luna (xhigh), Codex: 73; GPT-5.6 Luna (high), Codex: 72; GPT-6 Luna (high), Codex: 71; GPT-5.6 Terra (high), Codex: 69; GPT-5.6 Luna (medium), Codex: 66; DeepSeek V4 Flash (high), Codex: 66; GPT-5.6 Luna (low), Codex: 61; GPT-6 Luna (medium), Codex: 57; GPT-6 Luna (low), Codex: 51; gpt-oss-120b (high), Codex: 43; gpt-oss-20b (high), Codex: 90255075100GPT-6.1 Sol (xhigh)Codex82GPT-6.1 Sol (medium)Codex82GPT-6.1 Sol (high)Codex80GPT-6 Sol (high)Codex80DeepSeek V4.1 Flash (max)Codex78GPT-5.6 Sol (high)Codex76GPT-5.6 Luna (xhigh)Codex73GPT-6 Luna (xhigh)Codex73GPT-5.6 Luna (high)Codex72GPT-6 Luna (high)Codex71GPT-5.6 Terra (high)Codex69GPT-5.6 Luna (medium)Codex66DeepSeek V4 Flash (high)Codex66GPT-5.6 Luna (low)Codex61GPT-6 Luna (medium)Codex57GPT-6 Luna (low)Codex51gpt-oss-120b (high)Codex43gpt-oss-20b (high)Codex9
How to read this chart

Bars. Every entry in Codex with a result, highlighted. Provisional: not yet ranked.

Data table: Geospatial Agent Index: every entry in Codex

Provisional, so not on the index chart: GPT-6 Sol (medium), GPT-5.6 Sol (medium), DeepSeek V4 Pro (high). Their finished evaluations are in the charts below.

Same model elsewhere

The models we benchmark in Codex and in another harness, at the same reasoning level: an agent is a harness plus a model, so the gap between the bars is the harness. Codex is highlighted; gaps under about 3 points are within noise.

DeepSeek V4 Pro (high)

higher is better

  • Striped: provisional, not yet ranked
DeepSeek V4 Pro (high)Scores 0 to 100: DeepSeek V4 Pro (high), Codex: 73; DeepSeek V4 Pro (high), Terminus-2: 70DeepSeek V4 Pro (high) · Codex · provisional73DeepSeek V4 Pro (high) · Terminus-270
Data table: DeepSeek V4 Pro (high)
DeepSeek V4 Pro (high)
ModelDeveloperDeepSeek V4 Pro (high)
DeepSeek V4 Pro (high), CodexDeepSeek73
DeepSeek V4 Pro (high), Terminus-2DeepSeek70

DeepSeek V4.1 Flash (max)

higher is better

DeepSeek V4.1 Flash (max)Scores 0 to 100: DeepSeek V4.1 Flash (max), Codex: 78; DeepSeek V4.1 Flash (max), Claude Code: 76DeepSeek V4.1 Flash (max) · Codex78DeepSeek V4.1 Flash (max) · Claude Code76
Data table: DeepSeek V4.1 Flash (max)
DeepSeek V4.1 Flash (max)
ModelDeveloperDeepSeek V4.1 Flash (max)
DeepSeek V4.1 Flash (max), CodexDeepSeek78
DeepSeek V4.1 Flash (max), Claude CodeDeepSeek76

gpt-oss-120b (high)

higher is better

gpt-oss-120b (high)Scores 0 to 100: gpt-oss-120b (high), Terminus-2: 46; gpt-oss-120b (high), Codex: 43gpt-oss-120b (high) · Terminus-246gpt-oss-120b (high) · Codex43
Data table: gpt-oss-120b (high)
gpt-oss-120b (high)
ModelDevelopergpt-oss-120b (high)
gpt-oss-120b (high), Terminus-2OpenAI46
gpt-oss-120b (high), CodexOpenAI43

DeepSeek V4 Flash (high)

higher is better

DeepSeek V4 Flash (high)Scores 0 to 100: DeepSeek V4 Flash (high), Terminus-2: 72; DeepSeek V4 Flash (high), Codex: 66; DeepSeek V4 Flash (high), Claude Code: 59DeepSeek V4 Flash (high) · Terminus-272DeepSeek V4 Flash (high) · Codex66DeepSeek V4 Flash (high) · Claude Code59
Data table: DeepSeek V4 Flash (high)
DeepSeek V4 Flash (high)
ModelDeveloperDeepSeek V4 Flash (high)
DeepSeek V4 Flash (high), Terminus-2DeepSeek72
DeepSeek V4 Flash (high), CodexDeepSeek66
DeepSeek V4 Flash (high), Claude CodeDeepSeek59

gpt-oss-20b (high)

higher is better

gpt-oss-20b (high)Scores 0 to 100: gpt-oss-20b (high), Terminus-2: 33; gpt-oss-20b (high), Codex: 9gpt-oss-20b (high) · Terminus-233gpt-oss-20b (high) · Codex9
Data table: gpt-oss-20b (high)
gpt-oss-20b (high)
ModelDevelopergpt-oss-20b (high)
gpt-oss-20b (high), Terminus-2OpenAI33
gpt-oss-20b (high), CodexOpenAI9

By evaluation

Earth-Bench V2

higher is better

  • Striped: provisional, not yet ranked
Earth-Bench V2Scores 0 to 100: DeepSeek V4.1 Flash (max), Codex: 78; GPT-6 Sol (medium), Codex: 77; GPT-6 Sol (high), Codex: 75; DeepSeek V4 Pro (high), Codex: 75; GPT-6.1 Sol (medium), Codex: 72; GPT-6.1 Sol (high), Codex: 72; GPT-6.1 Sol (xhigh), Codex: 72; GPT-5.6 Sol (high), Codex: 70; DeepSeek V4 Flash (high), Codex: 70; GPT-6 Luna (xhigh), Codex: 67; GPT-5.6 Luna (high), Codex: 67; GPT-5.6 Luna (xhigh), Codex: 65; GPT-6 Luna (high), Codex: 63; GPT-5.6 Terra (high), Codex: 62; GPT-5.6 Luna (medium), Codex: 56; GPT-6 Luna (medium), Codex: 50; gpt-oss-120b (high), Codex: 49; GPT-5.6 Luna (low), Codex: 44; GPT-6 Luna (low), Codex: 44; gpt-oss-20b (high), Codex: 6DeepSeek V4.1 Flash (max) · Codex78GPT-6 Sol (medium) · Codex · provisional77GPT-6 Sol (high) · Codex75DeepSeek V4 Pro (high) · Codex · provisional75GPT-6.1 Sol (medium) · Codex72GPT-6.1 Sol (high) · Codex72GPT-6.1 Sol (xhigh) · Codex72GPT-5.6 Sol (high) · Codex70DeepSeek V4 Flash (high) · Codex70GPT-6 Luna (xhigh) · Codex67GPT-5.6 Luna (high) · Codex67GPT-5.6 Luna (xhigh) · Codex65GPT-6 Luna (high) · Codex63GPT-5.6 Terra (high) · Codex62GPT-5.6 Luna (medium) · Codex56GPT-6 Luna (medium) · Codex50gpt-oss-120b (high) · Codex49GPT-5.6 Luna (low) · Codex44GPT-6 Luna (low) · Codex44gpt-oss-20b (high) · Codex6
Data table: Earth-Bench V2

GeoBenchX

higher is better

  • Striped: provisional, not yet ranked
GeoBenchXScores 0 to 100: GPT-6.1 Sol (medium), Codex: 79; GPT-6.1 Sol (xhigh), Codex: 79; GPT-6.1 Sol (high), Codex: 77; GPT-6 Sol (medium), Codex: 75; GPT-6 Sol (high), Codex: 75; GPT-6 Luna (high), Codex: 74; GPT-6 Luna (xhigh), Codex: 73; DeepSeek V4 Pro (high), Codex: 72; GPT-5.6 Sol (high), Codex: 70; GPT-5.6 Luna (xhigh), Codex: 68; GPT-5.6 Luna (high), Codex: 67; GPT-5.6 Terra (high), Codex: 67; DeepSeek V4.1 Flash (max), Codex: 66; GPT-5.6 Luna (medium), Codex: 63; GPT-5.6 Luna (low), Codex: 62; GPT-6 Luna (medium), Codex: 57; gpt-oss-120b (high), Codex: 55; DeepSeek V4 Flash (high), Codex: 54; GPT-6 Luna (low), Codex: 51; gpt-oss-20b (high), Codex: 10GPT-6.1 Sol (medium) · Codex79GPT-6.1 Sol (xhigh) · Codex79GPT-6.1 Sol (high) · Codex77GPT-6 Sol (medium) · Codex · provisional75GPT-6 Sol (high) · Codex75GPT-6 Luna (high) · Codex74GPT-6 Luna (xhigh) · Codex73DeepSeek V4 Pro (high) · Codex · provisional72GPT-5.6 Sol (high) · Codex70GPT-5.6 Luna (xhigh) · Codex68GPT-5.6 Luna (high) · Codex67GPT-5.6 Terra (high) · Codex67DeepSeek V4.1 Flash (max) · Codex66GPT-5.6 Luna (medium) · Codex63GPT-5.6 Luna (low) · Codex62GPT-6 Luna (medium) · Codex57gpt-oss-120b (high) · Codex55DeepSeek V4 Flash (high) · Codex54GPT-6 Luna (low) · Codex51gpt-oss-20b (high) · Codex10
Data table: GeoBenchX

GS-QA2

higher is better

  • Striped: provisional, not yet ranked
GS-QA2Scores 0 to 100: GPT-5.6 Sol (medium), Codex: 100; GPT-6.1 Sol (xhigh), Codex: 94; GPT-6.1 Sol (medium), Codex: 93; GPT-6.1 Sol (high), Codex: 93; DeepSeek V4.1 Flash (max), Codex: 89; GPT-6 Sol (high), Codex: 89; GPT-6 Sol (medium), Codex: 87; GPT-5.6 Sol (high), Codex: 87; GPT-5.6 Luna (xhigh), Codex: 85; GPT-5.6 Luna (high), Codex: 81; GPT-5.6 Luna (medium), Codex: 80; GPT-5.6 Terra (high), Codex: 80; GPT-6 Luna (xhigh), Codex: 78; GPT-6 Luna (high), Codex: 77; GPT-5.6 Luna (low), Codex: 76; DeepSeek V4 Flash (high), Codex: 74; GPT-6 Luna (medium), Codex: 65; GPT-6 Luna (low), Codex: 59; gpt-oss-120b (high), Codex: 25; gpt-oss-20b (high), Codex: 12GPT-5.6 Sol (medium) · Codex · provisional100GPT-6.1 Sol (xhigh) · Codex94GPT-6.1 Sol (medium) · Codex93GPT-6.1 Sol (high) · Codex93DeepSeek V4.1 Flash (max) · Codex89GPT-6 Sol (high) · Codex89GPT-6 Sol (medium) · Codex · provisional87GPT-5.6 Sol (high) · Codex87GPT-5.6 Luna (xhigh) · Codex85GPT-5.6 Luna (high) · Codex81GPT-5.6 Luna (medium) · Codex80GPT-5.6 Terra (high) · Codex80GPT-6 Luna (xhigh) · Codex78GPT-6 Luna (high) · Codex77GPT-5.6 Luna (low) · Codex76DeepSeek V4 Flash (high) · Codex74GPT-6 Luna (medium) · Codex65GPT-6 Luna (low) · Codex59gpt-oss-120b (high) · Codex25gpt-oss-20b (high) · Codex12
Data table: GS-QA2

GeoAgentBench

higher is better

  • Striped: provisional, not yet ranked
GeoAgentBenchScores 0 to 100: GPT-6.1 Sol (medium), Codex: 100; GPT-6.1 Sol (high), Codex: 100; GPT-6.1 Sol (xhigh), Codex: 100; GPT-6 Sol (medium), Codex: 100; GPT-6 Sol (high), Codex: 100; GPT-5.6 Sol (high), Codex: 100; GPT-5.6 Luna (medium), Codex: 98; GPT-5.6 Luna (high), Codex: 98; GPT-5.6 Luna (xhigh), Codex: 98; GPT-6 Luna (xhigh), Codex: 96; GPT-6 Luna (high), Codex: 96; GPT-5.6 Terra (high), Codex: 96; DeepSeek V4.1 Flash (max), Codex: 96; GPT-6 Luna (medium), Codex: 94; DeepSeek V4 Flash (high), Codex: 92; GPT-6 Luna (low), Codex: 92; GPT-5.6 Luna (low), Codex: 86; gpt-oss-120b (high), Codex: 58; gpt-oss-20b (high), Codex: 10GPT-6.1 Sol (medium) · Codex100GPT-6.1 Sol (high) · Codex100GPT-6.1 Sol (xhigh) · Codex100GPT-6 Sol (medium) · Codex · provisional100GPT-6 Sol (high) · Codex100GPT-5.6 Sol (high) · Codex100GPT-5.6 Luna (medium) · Codex98GPT-5.6 Luna (high) · Codex98GPT-5.6 Luna (xhigh) · Codex98GPT-6 Luna (xhigh) · Codex96GPT-6 Luna (high) · Codex96GPT-5.6 Terra (high) · Codex96DeepSeek V4.1 Flash (max) · Codex96GPT-6 Luna (medium) · Codex94DeepSeek V4 Flash (high) · Codex92GPT-6 Luna (low) · Codex92GPT-5.6 Luna (low) · Codex86gpt-oss-120b (high) · Codex58gpt-oss-20b (high) · Codex10
Data table: GeoAgentBench

GeoAnalystBench

higher is better

GeoAnalystBenchScores 0 to 100: GPT-6.1 Sol (medium), Codex: 100; GPT-6.1 Sol (high), Codex: 100; GPT-6 Luna (xhigh), Codex: 100; GPT-6 Sol (high), Codex: 100; GPT-6 Luna (high), Codex: 100; GPT-5.6 Luna (medium), Codex: 100; GPT-5.6 Luna (high), Codex: 100; GPT-5.6 Luna (xhigh), Codex: 100; GPT-6.1 Sol (xhigh), Codex: 95; GPT-5.6 Terra (high), Codex: 95; GPT-5.6 Luna (low), Codex: 95; DeepSeek V4 Flash (high), Codex: 89; GPT-6 Luna (medium), Codex: 89; GPT-5.6 Sol (high), Codex: 89; DeepSeek V4.1 Flash (max), Codex: 88; GPT-6 Luna (low), Codex: 79; gpt-oss-120b (high), Codex: 42; gpt-oss-20b (high), Codex: 11GPT-6.1 Sol (medium) · Codex100GPT-6.1 Sol (high) · Codex100GPT-6 Luna (xhigh) · Codex100GPT-6 Sol (high) · Codex100GPT-6 Luna (high) · Codex100GPT-5.6 Luna (medium) · Codex100GPT-5.6 Luna (high) · Codex100GPT-5.6 Luna (xhigh) · Codex100GPT-6.1 Sol (xhigh) · Codex95GPT-5.6 Terra (high) · Codex95GPT-5.6 Luna (low) · Codex95DeepSeek V4 Flash (high) · Codex89GPT-6 Luna (medium) · Codex89GPT-5.6 Sol (high) · Codex89DeepSeek V4.1 Flash (max) · Codex88GPT-6 Luna (low) · Codex79gpt-oss-120b (high) · Codex42gpt-oss-20b (high) · Codex11
Data table: GeoAnalystBench

ThinkGeo

higher is better

ThinkGeoScores 0 to 100: GPT-6 Sol (medium), Codex: 100; GPT-6.1 Sol (xhigh), Codex: 88; GPT-6.1 Sol (medium), Codex: 87; GPT-6.1 Sol (high), Codex: 81; GPT-5.6 Sol (high), Codex: 74; GPT-6 Sol (high), Codex: 72; DeepSeek V4.1 Flash (max), Codex: 70; GPT-6 Luna (xhigh), Codex: 64; GPT-5.6 Terra (high), Codex: 62; GPT-5.6 Luna (medium), Codex: 62; GPT-5.6 Luna (xhigh), Codex: 62; GPT-6 Luna (medium), Codex: 60; GPT-6 Luna (high), Codex: 60; GPT-5.6 Luna (high), Codex: 57; GPT-5.6 Luna (low), Codex: 57; GPT-6 Luna (low), Codex: 23GPT-6 Sol (medium) · Codex · provisional100GPT-6.1 Sol (xhigh) · Codex88GPT-6.1 Sol (medium) · Codex87GPT-6.1 Sol (high) · Codex81GPT-5.6 Sol (high) · Codex74GPT-6 Sol (high) · Codex72DeepSeek V4.1 Flash (max) · Codex70GPT-6 Luna (xhigh) · Codex64GPT-5.6 Terra (high) · Codex62GPT-5.6 Luna (medium) · Codex62GPT-5.6 Luna (xhigh) · Codex62GPT-6 Luna (medium) · Codex60GPT-6 Luna (high) · Codex60GPT-5.6 Luna (high) · Codex57GPT-5.6 Luna (low) · Codex57GPT-6 Luna (low) · Codex23
Data table: ThinkGeo

OpenEarthAgent

higher is better

OpenEarthAgentScores 0 to 100: GPT-6.1 Sol (medium), Codex: 100; GPT-6.1 Sol (high), Codex: 100; GPT-6 Sol (high), Codex: 100; GPT-5.6 Luna (high), Codex: 100; GPT-5.6 Luna (xhigh), Codex: 100; DeepSeek V4.1 Flash (max), Codex: 94; GPT-6.1 Sol (xhigh), Codex: 94; GPT-6 Luna (xhigh), Codex: 88; GPT-6 Sol (medium), Codex: 88; GPT-6 Luna (high), Codex: 88; GPT-5.6 Terra (high), Codex: 88; GPT-5.6 Luna (medium), Codex: 88; GPT-5.6 Luna (low), Codex: 82; GPT-6 Luna (medium), Codex: 76; GPT-5.6 Sol (high), Codex: 76; GPT-6 Luna (low), Codex: 41GPT-6.1 Sol (medium) · Codex100GPT-6.1 Sol (high) · Codex100GPT-6 Sol (high) · Codex100GPT-5.6 Luna (high) · Codex100GPT-5.6 Luna (xhigh) · Codex100DeepSeek V4.1 Flash (max) · Codex94GPT-6.1 Sol (xhigh) · Codex94GPT-6 Luna (xhigh) · Codex88GPT-6 Sol (medium) · Codex · provisional88GPT-6 Luna (high) · Codex88GPT-5.6 Terra (high) · Codex88GPT-5.6 Luna (medium) · Codex88GPT-5.6 Luna (low) · Codex82GPT-6 Luna (medium) · Codex76GPT-5.6 Sol (high) · Codex76GPT-6 Luna (low) · Codex41
Data table: OpenEarthAgent

Cost

Cost per task

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

  • Striped: provisional, not yet ranked
Cost per taskCost per task in US dollars: GPT-6 Luna (medium), Codex: $0.0026; GPT-6 Luna (low), Codex: $0.0032; GPT-6 Luna (high), Codex: $0.0040; GPT-5.6 Luna (low), Codex: $0.0058; GPT-6 Luna (xhigh), Codex: $0.0070; GPT-5.6 Luna (medium), Codex: $0.0094; GPT-5.6 Luna (high), Codex: $0.016; GPT-5.6 Luna (xhigh), Codex: $0.021; GPT-6.1 Sol (medium), Codex: $0.046; GPT-6 Sol (medium), Codex: $0.047 (provisional, not yet ranked); GPT-6.1 Sol (high), Codex: $0.059; GPT-5.6 Sol (medium), Codex: $0.060 (provisional, not yet ranked); DeepSeek V4.1 Flash (max), Codex: $0.067; GPT-6 Sol (high), Codex: $0.082; GPT-6.1 Sol (xhigh), Codex: $0.085; DeepSeek V4 Flash (high), Codex: $0.085; gpt-oss-20b (high), Codex: $0.094; GPT-5.6 Terra (high), Codex: $0.10; GPT-5.6 Sol (high), Codex: $0.21; gpt-oss-120b (high), Codex: $0.26; DeepSeek V4 Pro (high), Codex: $0.64 (provisional, not yet ranked)$0$0.20$0.40$0.60$0.80GPT-6 Luna (medium)Codex$0.0026GPT-6 Luna (low)Codex$0.0032GPT-6 Luna (high)Codex$0.0040GPT-5.6 Luna (low)Codex$0.0058GPT-6 Luna (xhigh)Codex$0.0070GPT-5.6 Luna (medium)Codex$0.0094GPT-5.6 Luna (high)Codex$0.016GPT-5.6 Luna (xhigh)Codex$0.021GPT-6.1 Sol (medium)Codex$0.046GPT-6 Sol (medium)Codex · provisional$0.047GPT-6.1 Sol (high)Codex$0.059GPT-5.6 Sol (medium)Codex · provisional$0.060DeepSeek V4.1 Flash (max)Codex$0.067GPT-6 Sol (high)Codex$0.082GPT-6.1 Sol (xhigh)Codex$0.085DeepSeek V4 Flash (high)Codex$0.085gpt-oss-20b (high)Codex$0.094GPT-5.6 Terra (high)Codex$0.10GPT-5.6 Sol (high)Codex$0.21gpt-oss-120b (high)Codex$0.26DeepSeek V4 Pro (high)Codex · provisional$0.64
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 Codex with a result, highlighted. 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
Geospatial Agent Index vs. cost per taskGeospatial Agent Index (0 to 100) against cost per task in US dollars, log scale: DeepSeek V4 Flash (high), Codex: 66.06, $0.085; DeepSeek V4.1 Flash (max), Codex: 77.76, $0.067; gpt-oss-120b (high), Codex: 42.81, $0.26; gpt-oss-20b (high), Codex: 9.31, $0.094; GPT-6.1 Sol (medium), Codex: 81.5, $0.046; GPT-6.1 Sol (high), Codex: 80.35, $0.059; GPT-6.1 Sol (xhigh), Codex: 81.82, $0.085; GPT-6 Luna (xhigh), Codex: 72.65, $0.0070; GPT-6 Sol (high), Codex: 79.53, $0.082; GPT-6 Luna (medium), Codex: 57.39, $0.0026; GPT-6 Luna (high), Codex: 71.42, $0.0040; GPT-5.6 Sol (high), Codex: 75.62, $0.21; GPT-5.6 Terra (high), Codex: 69.32, $0.10; GPT-5.6 Luna (medium), Codex: 66.34, $0.0094; GPT-5.6 Luna (high), Codex: 71.7, $0.016; GPT-5.6 Luna (xhigh), Codex: 72.66, $0.021; GPT-6 Luna (low), Codex: 51.2, $0.0032; GPT-5.6 Luna (low), Codex: 60.81, $0.0058Most attractive quadrant0102030405060708090$0.002$0.005$0.01$0.02$0.05$0.10$0.20$0.50Cost per task (USD, log scale)Geospatial Agent IndexGPT-6.1 Sol (xhigh), CodexGPT-6.1 Sol (medium), CodexGPT-5.6 Luna (xhigh), CodexGPT-6 Luna (xhigh), CodexGPT-5.6 Luna (high), CodexGPT-6 Luna (high), CodexGPT-6 Luna (medium), CodexGPT-6.1 Sol (high), CodexGPT-6 Sol (high), CodexDeepSeek V4.1 Flash (max), CodexGPT-5.6 Sol (high), CodexGPT-5.6 Terra (high), CodexGPT-5.6 Luna (medium), CodexDeepSeek V4 Flash (high), CodexGPT-5.6 Luna (low), CodexGPT-6 Luna (low), Codexgpt-oss-120b (high), Codexgpt-oss-20b (high), Codex
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
Time per taskTime per task: GPT-5.6 Sol (medium), Codex: 0.2 min (provisional, not yet ranked); GPT-6 Sol (medium), Codex: 0.3 min (provisional, not yet ranked); GPT-6.1 Sol (medium), Codex: 0.4 min; GPT-6 Luna (medium), Codex: 0.4 min; GPT-6 Luna (low), Codex: 0.5 min; GPT-6.1 Sol (high), Codex: 0.5 min; GPT-5.6 Luna (low), Codex: 0.5 min; GPT-6 Luna (high), Codex: 0.7 min; GPT-6 Sol (high), Codex: 0.8 min; GPT-5.6 Luna (medium), Codex: 0.8 min; GPT-6.1 Sol (xhigh), Codex: 0.8 min; GPT-5.6 Terra (high), Codex: 0.8 min; GPT-5.6 Sol (high), Codex: 1.0 min; GPT-6 Luna (xhigh), Codex: 1.4 min; GPT-5.6 Luna (high), Codex: 1.4 min; GPT-5.6 Luna (xhigh), Codex: 1.7 min; gpt-oss-20b (high), Codex: 4.8 min; DeepSeek V4 Pro (high), Codex: 4.9 min (provisional, not yet ranked); gpt-oss-120b (high), Codex: 6.0 min; DeepSeek V4 Flash (high), Codex: 6.5 min; DeepSeek V4.1 Flash (max), Codex: 8.6 min02.557.510GPT-5.6 Sol (medium)Codex · provisional0.2 minGPT-6 Sol (medium)Codex · provisional0.3 minGPT-6.1 Sol (medium)Codex0.4 minGPT-6 Luna (medium)Codex0.4 minGPT-6 Luna (low)Codex0.5 minGPT-6.1 Sol (high)Codex0.5 minGPT-5.6 Luna (low)Codex0.5 minGPT-6 Luna (high)Codex0.7 minGPT-6 Sol (high)Codex0.8 minGPT-5.6 Luna (medium)Codex0.8 minGPT-6.1 Sol (xhigh)Codex0.8 minGPT-5.6 Terra (high)Codex0.8 minGPT-5.6 Sol (high)Codex1.0 minGPT-6 Luna (xhigh)Codex1.4 minGPT-5.6 Luna (high)Codex1.4 minGPT-5.6 Luna (xhigh)Codex1.7 mingpt-oss-20b (high)Codex4.8 minDeepSeek V4 Pro (high)Codex · provisional4.9 mingpt-oss-120b (high)Codex6.0 minDeepSeek V4 Flash (high)Codex6.5 minDeepSeek V4.1 Flash (max)Codex8.6 min
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 Codex with a result, highlighted. 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
Geospatial Agent Index vs. time per taskGeospatial Agent Index (0 to 100) against time per task (minutes), log scale: DeepSeek V4 Flash (high), Codex: 66.06, 6.5 min; DeepSeek V4.1 Flash (max), Codex: 77.76, 8.6 min; gpt-oss-120b (high), Codex: 42.81, 6.0 min; gpt-oss-20b (high), Codex: 9.31, 4.8 min; GPT-6.1 Sol (medium), Codex: 81.5, 0.4 min; GPT-6.1 Sol (high), Codex: 80.35, 0.5 min; GPT-6.1 Sol (xhigh), Codex: 81.82, 0.8 min; GPT-6 Luna (xhigh), Codex: 72.65, 1.4 min; GPT-6 Sol (high), Codex: 79.53, 0.8 min; GPT-6 Luna (medium), Codex: 57.39, 0.4 min; GPT-6 Luna (high), Codex: 71.42, 0.7 min; GPT-5.6 Sol (high), Codex: 75.62, 1.0 min; GPT-5.6 Terra (high), Codex: 69.32, 0.8 min; GPT-5.6 Luna (medium), Codex: 66.34, 0.8 min; GPT-5.6 Luna (high), Codex: 71.7, 1.4 min; GPT-5.6 Luna (xhigh), Codex: 72.66, 1.7 min; GPT-6 Luna (low), Codex: 51.2, 0.5 min; GPT-5.6 Luna (low), Codex: 60.81, 0.5 minMost attractive quadrant01020304050607080900.2 min0.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexGPT-6.1 Sol (xhigh), CodexGPT-6.1 Sol (medium), CodexGPT-6.1 Sol (high), CodexGPT-6 Sol (high), CodexDeepSeek V4.1 Flash (max), CodexGPT-5.6 Sol (high), CodexGPT-5.6 Luna (xhigh), CodexGPT-6 Luna (xhigh), CodexGPT-5.6 Luna (high), CodexGPT-6 Luna (high), CodexGPT-5.6 Terra (high), CodexGPT-5.6 Luna (medium), CodexDeepSeek V4 Flash (high), CodexGPT-5.6 Luna (low), CodexGPT-6 Luna (medium), CodexGPT-6 Luna (low), Codexgpt-oss-120b (high), Codexgpt-oss-20b (high), Codex
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
Every benchmark
ModelStatusEarth-Bench V2GeoBenchXGS-QA2GeoAgentBenchGeoAnalystBenchThinkGeoOpenEarthAgentEarth-Bench V2 imagesThinkGeo imagesGeospatial knowledge
GPT-6.1 Sol (xhigh), CodexRanked727994100958894724193
GPT-6.1 Sol (medium), CodexRanked72799310010087100723792
GPT-6.1 Sol (high), CodexRanked72779310010081100683793
GPT-6 Sol (medium), CodexProvisional77 (provisional)75 (provisional)87100 (provisional)No data100 (provisional)88 (provisional)No dataNo dataNo data
GPT-6 Sol (high), CodexRanked75758910010072100724191
DeepSeek V4.1 Flash (max), CodexRanked78668996887094No dataNo dataNo data
GPT-5.6 Sol (high), CodexRanked707087100897476764193
GPT-5.6 Luna (xhigh), CodexRanked6568859810062100663791
GPT-6 Luna (xhigh), CodexRanked677378961006488663591
GPT-5.6 Luna (high), CodexRanked6767819810057100683591
GPT-6 Luna (high), CodexRanked637477961006088624392
GPT-5.6 Terra (high), CodexRanked62678096956288743492
GPT-5.6 Luna (medium), CodexRanked566380981006288603591
DeepSeek V4 Flash (high), CodexRanked7054749289No dataNo dataNo dataNo data91
GPT-5.6 Luna (low), CodexRanked44627686955782683390
GPT-6 Luna (medium), CodexRanked50576594896076623790
GPT-6 Luna (low), CodexRanked44515992792341662989
gpt-oss-120b (high), CodexRanked4955255842No dataNo dataNo dataNo data90
gpt-oss-20b (high), CodexRanked610121011No dataNo dataNo dataNo data85
GPT-5.6 Sol (medium), CodexProvisionalNo dataNo data100 (provisional)No dataNo dataNo dataNo dataNo dataNo dataNo data
DeepSeek V4 Pro (high), CodexProvisional75 (provisional)72 (provisional)No dataNo dataNo dataNo dataNo dataNo dataNo data92

About Codex

Made by
OpenAI
What it is
The command-line coding agent made by OpenAI. It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Version
0.162.0, the same pinned build for every run
Models we run in it
11 from OpenAI, DeepSeek
How we run it
The same tasks, limits and checks as every other harness: only the agent program changes. Methodology.

Frequently asked questions

Which models do you benchmark in Codex?
Which model is best in Codex?
GPT-6.1 Sol (xhigh), with 81.8 on the Geospatial Agent Index, 4th of 83 entries.
What is the cheapest model in Codex?
GPT-6 Luna (medium), at $0.0026 per agent task at list prices.
What is the fastest model in Codex?
GPT-6.1 Sol (medium), at 0.4 min per agent task.
Is Codex better than other harnesses?
It depends on the model. DeepSeek V4 Pro (high) scores 73.3 in Codex and 70.3 in Terminus-2; gpt-oss-20b (high) scores 33.5 in Terminus-2 and 9.3 in Codex. Gaps under about 3 points are within noise.
Who makes Codex?
OpenAI. The command-line coding agent made by OpenAI. It reads the task, writes and runs code in the sandbox, and passes the results back to the model.
Which version of Codex do you run?
0.162.0, pinned: every run uses the same build.
Are any models in Codex still being benchmarked?
Yes. GPT-6 Sol (medium); GPT-5.6 Sol (medium); DeepSeek V4 Pro (high) are still being benchmarked, so the results are provisional: not yet ranked.