Axis Spatial

Qwen 3.8 27B (xhigh), Qwen Code: geospatial agent results

Qwen 3.8 27B (xhigh), Qwen Code by Alibaba (Qwen), run under Qwen Code: the model's Geospatial Intelligence Index and groups, and this entry's Geospatial Agent Index, score on each evaluation and each kind of geospatial work, cost, time and token use.

Summary

Geospatial Intelligence Index, Text only
75.1, rank 33 of 46 (every reasoning level)
Geospatial Intelligence Index, Text + vision
Not yet ranked. Not run: Vision agents (all 64 tasks), Vision (8 of 136 image questions).
Vision
Not run: 8 of 136 questions
Geospatial Agent Index, Text only
68, rank 37 of 56 entries (every reasoning level)
Geospatial Agent Index, Text + vision
Not run: Vision agents
Cost per task
$0.22
Time per task (at 100 output tokens/s)
4.7 min (estimated)
Tokens per attempt
840k
Turns per attempt
17.6
Attempts scored
488
Map and trace judge
Claude Sonnet 5.5. 169 passes await review; 13 could not be reviewed (the judge's safety filter declined them).

Geospatial Intelligence Index

Qwen 3.8 27B at each reasoning level we run, with its four group scores. One bar per model, at its highest reasoning level that is ranked. The level is in brackets. Other levels are under 'Show every reasoning level'. See the Geospatial Intelligence Index.

Qwen 3.8 27B: Geospatial Intelligence Index at each reasoning level
ModelGeospatial Intelligence Index: Text onlyGeospatial Intelligence Index: Text + visionRank, Text + vision (every reasoning level)Agent tasks (4/8)Vision (2/8)Guided workflows (1/8)Geospatial knowledge (1/8)Scored in
Qwen 3.8 27B (xhigh) Open weights (default view)75.1Not yet rankedNot yet rankedNot run: Vision agents (all 64 tasks)Not run: 8 of 136 questions8691Qwen Code

Not yet ranked: Qwen 3.8 27B (xhigh): Not run: Vision agents (all 64 tasks), Vision (8 of 136 image questions).

Geospatial Agent Index entries

Every entry of Qwen 3.8 27B: each harness and reasoning level we run. See the Geospatial Agent Index.

Qwen 3.8 27B: Geospatial Agent Index entries
ModelHarnessGeospatial Agent IndexRankCost per taskTime per task (at 100 output tokens/s)
Qwen 3.8 27B (xhigh), Qwen Code (this page)Qwen Code6837$0.224.7 min (estimated)
Qwen 3.8 27B (xhigh), Terminus-2Terminus-237, not every task attempted yet (27 of 488 tasks attempted)Not yet ranked$0.0559.2 min

* Not every task attempted yet: the score may still change. What the asterisk means.

Evaluations

Score by evaluation

Share of tasks passed, 0 to 100 · Higher is better

Score by evaluationScores 0 to 100: GS-QA2: 75; Earth-Bench V2: 68; GeoBenchX: 62; Vision agents: No data025507510075GS-QA268Earth-BenchV262GeoBenchX
Data table: Score by evaluation
Score by evaluation
EvaluationScoreTasks with a scored attemptCost per taskTime per task (at 100 output tokens/s)
Earth-Bench V268117 of 117$0.328.6 min (estimated)
GeoBenchX62150 of 150$0.224.1 min (estimated)
GS-QA275221 of 221$0.163.0 min (estimated)
Vision agentsNo data0 of 64No dataNo data

Score by task format and kind of work

Score by task format

Share of tasks passed in each task format, 0 to 100 · Higher is better

Score by task formatScores 0 to 100: Single-answer questions: 74; Multi-step analysis: 61; Recognising infeasible requests: 600255075100Single-answer questions74Multi-step analysis61Recognising infeasible requests60
How to read this chart

What this shows. The same attempts as the index, grouped by task format. Groups with few tasks are less certain. About these breakdowns.

Data table: Score by task format
Score by task format
Task formatScoreTasks with a scored attempt
Multi-step analysis6179 of 79
Single-answer questions74349 of 349
Recognising infeasible requests6060 of 60

Score by kind of work

Share of tasks passed in each kind of work, 0 to 100 · Higher is better

Score by kind of workScores 0 to 100: Raster and remote sensing: 76; Vector and overlay: 75; Mapping and cartography: 63; Climate and time series: 61; Recognising infeasible requests: 60; Spatial statistics and interpolation: 540255075100Raster and remote sensing76Vector and overlay75Mapping and cartography63Climate and time series61Recognising infeasible requests60Spatial statistics and interpolation54
How to read this chart

What this shows. The same attempts as the index, grouped by kind of work. Groups with few tasks are less certain. About these breakdowns.

Data table: Score by kind of work
Score by kind of work
Kind of workScoreTasks with a scored attempt
Raster and remote sensing76181 of 181
Vector and overlay75118 of 118
Climate and time series6159 of 59
Spatial statistics and interpolation5413 of 13
Mapping and cartography6357 of 57
Recognising infeasible requests6060 of 60

Score by data type

Share of tasks passed in each data type, 0 to 100 · Higher is better

Score by data typeScores 0 to 100: Climate or gridded time series: 80; Elevation or terrain: 78; Other gridded data: 76; Vector only: 69; Optical multispectral: 640255075100Climate or gridded time series80Elevation or terrain78Other gridded data76Vector only69Optical multispectral64
How to read this chart

What this shows. The same attempts as the index, grouped by data type. Groups with few tasks are less certain. About these breakdowns.

Data table: Score by data type
Score by data type
Data typeScoreTasks with a scored attempt
Optical multispectral6484 of 84
Elevation or terrain78108 of 108
Climate or gridded time series8010 of 10
Other gridded data7645 of 45
Vector only69186 of 186

Score by task length

Share of tasks passed in each task length, 0 to 100 · Higher is better

Score by task lengthScores 0 to 100: Short tasks: 88; Medium tasks: 68; Long tasks: 520255075100Short tasks88Medium tasks68Long tasks52
How to read this chart

What this shows. The same attempts as the index, grouped by task length. Groups with few tasks are less certain. About these breakdowns.

Data table: Score by task length
Score by task length
Task lengthScoreTasks with a scored attempt
Short tasks88182 of 182
Medium tasks68142 of 142
Long tasks52164 of 164

Compared with other models

Geospatial Agent Index

Equal average of Earth-Bench V2, GeoBenchX and GS-QA2, 0 to 100 · Higher is better

Geospatial Agent IndexScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 85; Claude Opus 5.5 (high), Claude Code: 82; GPT-6.1 Sol (xhigh), Codex: 82; GPT-6.1 Sol (medium), Codex: 82; Claude Opus 5.5 (medium), Claude Code: 80; GPT-6.1 Sol (high), Codex: 80; Claude Opus 5 (high), Claude Code: 80; GPT-6 Sol (high), Codex: 80; Claude Sonnet 5.5 (xhigh), Claude Code: 79; Claude Sonnet 5.5 (high), Claude Code: 78; Claude Opus 4.8 (high), Claude Code: 76; Claude Haiku 5.5 (xhigh), Claude Code: 76; Claude Haiku 5.5 (high), Claude Code: 76; GPT-5.6 Sol (high), Codex: 75; Claude Sonnet 5 (high), Claude Code: 74; Claude Opus 4.7 (high), Claude Code: 74; GLM-5.3 (max), Claude Code: 73; GPT-5.6 Luna (xhigh), Codex: 73; GPT-6 Luna (xhigh), Codex: 73; Claude Haiku 5.5 (medium), Claude Code: 72; Kimi K3 (Reasoning), Terminus-2: 72; DeepSeek V4 Flash (high), Terminus-2: 72; Kimi K2.7 Code (Reasoning), Kimi Code: 72; GLM-5.2 (max), Terminus-2: 72; GPT-5.6 Luna (high), Codex: 72; GPT-6 Luna (high), Codex: 71; Kimi K2.7 Code (Reasoning), Terminus-2: 71; DeepSeek V4 Pro (max), Terminus-2: 71; Claude Sonnet 5.5 (medium), Claude Code: 71; GLM-5.2 (high), Terminus-2: 71; GLM-5.3 (max), Terminus-2: 70; DeepSeek V4 Pro (high), Terminus-2: 70; Kimi K2.6 (Reasoning), Terminus-2: 70; GPT-5.6 Terra (high), Codex: 69; DeepSeek V4 Flash (max), Terminus-2: 69; Kimi K2.6 (Reasoning), Kimi Code: 69; Qwen 3.8 27B (xhigh), Qwen Code: 68; GPT-5.6 Luna (medium), Codex: 66; GLM-5.3 (low), Terminus-2: 66; GLM-5.2 (Non-reasoning), Terminus-2: 63; GLM-5.3-Flash (max), Terminus-2: 63; GLM-5.3-Flash (low), Terminus-2: 62; GPT-5.6 Luna (low), Codex: 61; Claude Haiku 4.5 (Reasoning), Claude Code: 58; GPT-6 Luna (medium), Codex: 57; Gemma 4 26B A4B (Reasoning), Terminus-2: 56; GPT-6 Luna (low), Codex: 51; gpt-oss-120b (high), Terminus-2: 46; GLM-4.7-Flash (Reasoning), Terminus-2: 43; gpt-oss-120b (high), Codex: 43; GLM-4.7-Flash (Reasoning), Claude Code: 41; gpt-oss-20b (high), Terminus-2: 33; Mistral Large 3 (Non-reasoning), Terminus-2: 33; gpt-oss-120b (low), Terminus-2: 31; gpt-oss-20b (high), Codex: 9; Llama 3.3 70B (Non-reasoning), Terminus-2: 80255075100Claude Opus 5.5 (xhigh)Claude Code85Claude Opus 5.5 (high)Claude Code82GPT-6.1 Sol (xhigh)Codex82GPT-6.1 Sol (medium)Codex82Claude Opus 5.5 (medium)Claude Code80GPT-6.1 Sol (high)Codex80Claude Opus 5 (high)Claude Code80GPT-6 Sol (high)Codex80Claude Sonnet 5.5 (xhigh)Claude Code79Claude Sonnet 5.5 (high)Claude Code78Claude Opus 4.8 (high)Claude Code76Claude Haiku 5.5 (xhigh)Claude Code76Claude Haiku 5.5 (high)Claude Code76GPT-5.6 Sol (high)Codex75Claude Sonnet 5 (high)Claude Code74Claude Opus 4.7 (high)Claude Code74GLM-5.3 (max)Claude Code73GPT-5.6 Luna (xhigh)Codex73GPT-6 Luna (xhigh)Codex73Claude Haiku 5.5 (medium)Claude Code72Kimi K3 (Reasoning)Terminus-272DeepSeek V4 Flash (high)Terminus-272Kimi K2.7 Code (Reasoning)Kimi Code72GLM-5.2 (max)Terminus-272GPT-5.6 Luna (high)Codex72GPT-6 Luna (high)Codex71Kimi K2.7 Code (Reasoning)Terminus-271DeepSeek V4 Pro (max)Terminus-271Claude Sonnet 5.5 (medium)Claude Code71GLM-5.2 (high)Terminus-271GLM-5.3 (max)Terminus-270DeepSeek V4 Pro (high)Terminus-270Kimi K2.6 (Reasoning)Terminus-270GPT-5.6 Terra (high)Codex69DeepSeek V4 Flash (max)Terminus-269Kimi K2.6 (Reasoning)Kimi Code69Qwen 3.8 27B (xhigh)Qwen Code68GPT-5.6 Luna (medium)Codex66GLM-5.3 (low)Terminus-266GLM-5.2 (Non-reasoning)Terminus-263GLM-5.3-Flash (max)Terminus-263GLM-5.3-Flash (low)Terminus-262GPT-5.6 Luna (low)Codex61Claude Haiku 4.5 (Reasoning)Claude Code58GPT-6 Luna (medium)Codex57Gemma 4 26B A4B (Reasoning)Terminus-256GPT-6 Luna (low)Codex51gpt-oss-120b (high)Terminus-246GLM-4.7-Flash (Reasoning)Terminus-243gpt-oss-120b (high)Codex43GLM-4.7-Flash (Reasoning)Claude Code41gpt-oss-20b (high)Terminus-233Mistral Large 3 (Non-reasoning)Terminus-233gpt-oss-120b (low)Terminus-231gpt-oss-20b (high)Codex9Llama 3.3 70B (Non-reasoning)Terminus-28
How to read this chart

What this metric means. The equal average of the three text evaluations (Earth-Bench V2, GeoBenchX and GS-QA2). The Text + vision view adds Vision agents. Each evaluation score is the share of its tasks passed, 0 to 100.

Not yet ranked. An entry is ranked only when it has an attempt on every task of every index evaluation. Entries without that are listed as not yet ranked, with the tasks attempted, for example "36 of 117 Earth-Bench V2 tasks", and are left out of the ranking, the index charts and the Pareto lines.

Stripes. A striped bar marks an entry that has not attempted every task yet, so its score may still change (methodology).

One attempt per task. Scores use one attempt per task. An entry without an attempt on every task is marked with the tasks attempted, for example "177 of 488 tasks".

Data table: Geospatial Agent Index
Geospatial Agent Index
ModelDeveloperGeospatial Agent IndexStatusEvaluations scoredTasks attempted
Claude Opus 5.5 (xhigh), Claude CodeAnthropic85Complete3 of 3488 of 488 tasks
Claude Opus 5.5 (high), Claude CodeAnthropic82Complete3 of 3488 of 488 tasks
GPT-6.1 Sol (xhigh), CodexOpenAI82Complete3 of 3488 of 488 tasks
GPT-6.1 Sol (medium), CodexOpenAI82Complete3 of 3488 of 488 tasks
Claude Opus 5.5 (medium), Claude CodeAnthropic80Complete3 of 3488 of 488 tasks
GPT-6.1 Sol (high), CodexOpenAI80Complete3 of 3488 of 488 tasks
Claude Opus 5 (high), Claude CodeAnthropic80Complete3 of 3488 of 488 tasks
GPT-6 Sol (high), CodexOpenAI80Complete3 of 3488 of 488 tasks
Claude Sonnet 5.5 (xhigh), Claude CodeAnthropic79Complete3 of 3488 of 488 tasks
Claude Sonnet 5.5 (high), Claude CodeAnthropic78Complete3 of 3488 of 488 tasks
Claude Opus 4.8 (high), Claude CodeAnthropic76Complete3 of 3488 of 488 tasks
Claude Haiku 5.5 (xhigh), Claude CodeAnthropic76Complete3 of 3488 of 488 tasks
Claude Haiku 5.5 (high), Claude CodeAnthropic76Complete3 of 3488 of 488 tasks
GPT-5.6 Sol (high), CodexOpenAI75Complete3 of 3488 of 488 tasks
Claude Sonnet 5 (high), Claude CodeAnthropic74Complete3 of 3488 of 488 tasks
Claude Opus 4.7 (high), Claude CodeAnthropic74Complete3 of 3488 of 488 tasks
GLM-5.3 (max), Claude CodeZ.ai73Complete3 of 3488 of 488 tasks
GPT-5.6 Luna (xhigh), CodexOpenAI73Complete3 of 3488 of 488 tasks
GPT-6 Luna (xhigh), CodexOpenAI73Complete3 of 3488 of 488 tasks
Claude Haiku 5.5 (medium), Claude CodeAnthropic72Complete3 of 3488 of 488 tasks
Kimi K3 (Reasoning), Terminus-2Moonshot AI72Complete3 of 3488 of 488 tasks
DeepSeek V4 Flash (high), Terminus-2DeepSeek72Complete3 of 3488 of 488 tasks
Kimi K2.7 Code (Reasoning), Kimi CodeMoonshot AI72Complete3 of 3488 of 488 tasks
GLM-5.2 (max), Terminus-2Z.ai72Complete3 of 3488 of 488 tasks
GPT-5.6 Luna (high), CodexOpenAI72Complete3 of 3488 of 488 tasks
GPT-6 Luna (high), CodexOpenAI71Complete3 of 3488 of 488 tasks
Kimi K2.7 Code (Reasoning), Terminus-2Moonshot AI71Complete3 of 3488 of 488 tasks
DeepSeek V4 Pro (max), Terminus-2DeepSeek71Complete3 of 3488 of 488 tasks
Claude Sonnet 5.5 (medium), Claude CodeAnthropic71Complete3 of 3488 of 488 tasks
GLM-5.2 (high), Terminus-2Z.ai71Complete3 of 3488 of 488 tasks
GLM-5.3 (max), Terminus-2Z.ai70Complete3 of 3488 of 488 tasks
DeepSeek V4 Pro (high), Terminus-2DeepSeek70Complete3 of 3488 of 488 tasks
Kimi K2.6 (Reasoning), Terminus-2Moonshot AI70Complete3 of 3488 of 488 tasks
GPT-5.6 Terra (high), CodexOpenAI69Complete3 of 3488 of 488 tasks
DeepSeek V4 Flash (max), Terminus-2DeepSeek69Complete3 of 3488 of 488 tasks
Kimi K2.6 (Reasoning), Kimi CodeMoonshot AI69Complete3 of 3488 of 488 tasks
Qwen 3.8 27B (xhigh), Qwen CodeAlibaba (Qwen)68Complete3 of 3488 of 488 tasks
GPT-5.6 Luna (medium), CodexOpenAI66Complete3 of 3488 of 488 tasks
GLM-5.3 (low), Terminus-2Z.ai66Complete3 of 3488 of 488 tasks
GLM-5.2 (Non-reasoning), Terminus-2Z.ai63Complete3 of 3488 of 488 tasks
GLM-5.3-Flash (max), Terminus-2Z.ai63Complete3 of 3488 of 488 tasks
GLM-5.3-Flash (low), Terminus-2Z.ai62Complete3 of 3488 of 488 tasks
GPT-5.6 Luna (low), CodexOpenAI61Complete3 of 3488 of 488 tasks
Claude Haiku 4.5 (Reasoning), Claude CodeAnthropic58Complete3 of 3488 of 488 tasks
GPT-6 Luna (medium), CodexOpenAI57Complete3 of 3488 of 488 tasks
Gemma 4 26B A4B (Reasoning), Terminus-2Google56Complete3 of 3488 of 488 tasks
GPT-6 Luna (low), CodexOpenAI51Complete3 of 3488 of 488 tasks
gpt-oss-120b (high), Terminus-2OpenAI46Complete3 of 3488 of 488 tasks
GLM-4.7-Flash (Reasoning), Terminus-2Z.ai43Complete3 of 3488 of 488 tasks
gpt-oss-120b (high), CodexOpenAI43Complete3 of 3488 of 488 tasks
GLM-4.7-Flash (Reasoning), Claude CodeZ.ai41Complete3 of 3488 of 488 tasks
gpt-oss-20b (high), Terminus-2OpenAI33Complete3 of 3488 of 488 tasks
Mistral Large 3 (Non-reasoning), Terminus-2Mistral AI33Complete3 of 3488 of 488 tasks
gpt-oss-120b (low), Terminus-2OpenAI31Complete3 of 3488 of 488 tasks
gpt-oss-20b (high), CodexOpenAI9Complete3 of 3488 of 488 tasks
Llama 3.3 70B (Non-reasoning), Terminus-2Meta8Complete3 of 3488 of 488 tasks

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 (max), Terminus-2: 69.11, $0.062; DeepSeek V4 Flash (high), Terminus-2: 71.86, $0.065; DeepSeek V4 Pro (max), Terminus-2: 71, $0.35; DeepSeek V4 Pro (high), Terminus-2: 70.31, $0.30; Gemma 4 26B A4B (Reasoning), Terminus-2: 55.79, $0.021; GLM-4.7-Flash (Reasoning), Terminus-2: 43.17, $0.037; GLM-5.2 (high), Terminus-2: 70.82, $0.22; GLM-5.2 (Non-reasoning), Terminus-2: 63.34, $0.099; GLM-5.2 (max), Terminus-2: 71.73, $0.32; GLM-5.3-Flash (low), Terminus-2: 61.76, $0.048; GLM-5.3-Flash (max), Terminus-2: 62.51, $0.094; GLM-5.3 (low), Terminus-2: 65.65, $0.27; GLM-5.3 (max), Terminus-2: 70.45, $0.38; gpt-oss-120b (low), Terminus-2: 31.18, $0.0046; gpt-oss-120b (high), Terminus-2: 45.57, $0.026; gpt-oss-20b (high), Terminus-2: 33.48, $0.017; Kimi K2.6 (Reasoning), Terminus-2: 69.57, $0.089; Kimi K2.7 Code (Reasoning), Terminus-2: 71.08, $0.11; Kimi K3 (Reasoning), Terminus-2: 71.99, $0.23; Llama 3.3 70B (Non-reasoning), Terminus-2: 8.22, $0.023; Mistral Large 3 (Non-reasoning), Terminus-2: 32.89, $0.026; Claude Haiku 4.5 (Reasoning), Claude Code: 57.52, $0.092; GLM-4.7-Flash (Reasoning), Claude Code: 41.27, $0.27; GLM-5.3 (max), Claude Code: 73.4, $0.48; gpt-oss-120b (high), Codex: 42.81, $0.26; gpt-oss-20b (high), Codex: 9.31, $0.094; Kimi K2.6 (Reasoning), Kimi Code: 69.05, $0.27; Kimi K2.7 Code (Reasoning), Kimi Code: 71.76, $0.18; Qwen 3.8 27B (xhigh), Qwen Code: 68.32, $0.22; Claude Opus 5.5 (medium), Claude Code: 80.46, $0.11; Claude Opus 5.5 (high), Claude Code: 82.41, $0.13; Claude Sonnet 5.5 (medium), Claude Code: 70.95, $0.046; Claude Sonnet 5.5 (high), Claude Code: 78.38, $0.055; Claude Haiku 5.5 (medium), Claude Code: 72.34, $0.0063; Claude Haiku 5.5 (high), Claude Code: 75.7, $0.0085; Claude Opus 5.5 (xhigh), Claude Code: 84.65, $0.25; Claude Sonnet 5.5 (xhigh), Claude Code: 79.01, $0.11; Claude Haiku 5.5 (xhigh), Claude Code: 75.72, $0.017; Claude Opus 5 (high), Claude Code: 79.75, $0.32; Claude Sonnet 5 (high), Claude Code: 74.37, $0.20; Claude Opus 4.8 (high), Claude Code: 75.98, $0.26; Claude Opus 4.7 (high), Claude Code: 73.56, $0.34; 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: 75.05, $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 quadrant0102030405060708090100$0.002$0.005$0.01$0.02$0.05$0.10$0.20$0.50$1.00Cost per task (USD, log scale)Geospatial Agent IndexClaude Opus 5.5 (xhigh), Claude CodeClaude Opus 5.5 (high), Claude CodeGPT-6.1 Sol (xhigh), CodexGPT-6.1 Sol (medium), CodexClaude Opus 5.5 (medium), Claude CodeGPT-6.1 Sol (high), CodexClaude Opus 5 (high), Claude CodeGPT-6 Sol (high), CodexClaude Sonnet 5.5 (xhigh), Claude CodeClaude Sonnet 5.5 (high), Claude CodeClaude Opus 4.8 (high), Claude CodeClaude Haiku 5.5 (xhigh), Claude CodeClaude Haiku 5.5 (high), Claude CodeGPT-5.6 Sol (high), CodexClaude Sonnet 5 (high), Claude CodeClaude Opus 4.7 (high), Claude CodeGLM-5.3 (max), Claude CodeGPT-5.6 Luna (xhigh), CodexGPT-6 Luna (xhigh), CodexClaude Haiku 5.5 (medium), Claude CodeKimi K3 (Reasoning), Terminus-2DeepSeek V4 Flash (high), Terminus-2Kimi K2.7 Code (Reasoning), Kimi CodeGLM-5.2 (max), Terminus-2GPT-5.6 Luna (high), CodexGPT-6 Luna (high), CodexKimi K2.7 Code (Reasoning), Terminus-2DeepSeek V4 Pro (max), Terminus-2Claude Sonnet 5.5 (medium), Claude CodeGLM-5.2 (high), Terminus-2GLM-5.3 (max), Terminus-2DeepSeek V4 Pro (high), Terminus-2Kimi K2.6 (Reasoning), Terminus-2GPT-5.6 Terra (high), CodexDeepSeek V4 Flash (max), Terminus-2Kimi K2.6 (Reasoning), Kimi CodeQwen 3.8 27B (xhigh), Qwen CodeGPT-5.6 Luna (medium), CodexGLM-5.3 (low), Terminus-2GLM-5.2 (Non-reasoning), Terminus-2GLM-5.3-Flash (max), Terminus-2GLM-5.3-Flash (low), Terminus-2GPT-5.6 Luna (low), CodexClaude Haiku 4.5 (Reasoning), Claude CodeGPT-6 Luna (medium), CodexGemma 4 26B A4B (Reasoning), Terminus-2GPT-6 Luna (low), Codexgpt-oss-120b (high), Terminus-2GLM-4.7-Flash (Reasoning), Terminus-2gpt-oss-120b (high), CodexGLM-4.7-Flash (Reasoning), Claude Codegpt-oss-20b (high), Terminus-2Mistral Large 3 (Non-reasoning), Terminus-2gpt-oss-120b (low), Terminus-2gpt-oss-20b (high), CodexLlama 3.3 70B (Non-reasoning), Terminus-2
How to read this chart

Not yet ranked. Entries without an attempt on every index task are not plotted; the table lists them. What is still to run is on the models page.

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
Geospatial Agent Index vs. cost per task
ModelGeospatial Agent IndexCost per task (USD)On the Pareto line
Claude Sonnet 4.6 (high), Terminus-258, not every task attempted yet (177 of 488 tasks attempted)$0.25Not plotted: not yet ranked
DeepSeek V4 Flash (max), Terminus-269$0.062No
DeepSeek V4 Flash (high), Terminus-272$0.065No
DeepSeek V4 Pro (max), Terminus-271$0.35No
DeepSeek V4 Pro (high), Terminus-270$0.30No
Gemini 3.1 Flash-Lite (low), Terminus-248, not every task attempted yet (177 of 488 tasks attempted)$0.012Not plotted: not yet ranked
Gemini 3.1 Flash-Lite (minimal), Terminus-242, not every task attempted yet (177 of 488 tasks attempted)$0.0055Not plotted: not yet ranked
Gemini 3.1 Flash-Lite (high), Terminus-256, not every task attempted yet (175 of 488 tasks attempted)$0.020Not plotted: not yet ranked
Gemini 3.1 Pro (preview) (low), Terminus-257, not every task attempted yet (177 of 488 tasks attempted)$0.057Not plotted: not yet ranked
Gemini 3.1 Pro (preview) (high), Terminus-266, not every task attempted yet (177 of 488 tasks attempted)$0.22Not plotted: not yet ranked
Gemini 3.8 Flash (high), Terminus-248, not every task attempted yet (177 of 488 tasks attempted)$0.31Not plotted: not yet ranked
Gemini 3.8 Flash (low), Terminus-267, not every task attempted yet (177 of 488 tasks attempted)$0.13Not plotted: not yet ranked
Gemma 4 26B A4B (Reasoning), Terminus-256$0.021No
GLM-4.7-Flash (Reasoning), Terminus-243$0.037No
GLM-5.2 (high), Terminus-271$0.22No
GLM-5.2 (Non-reasoning), Terminus-263$0.099No
GLM-5.2 (max), Terminus-272$0.32No
GLM-5.3-Flash (low), Terminus-262$0.048No
GLM-5.3-Flash (max), Terminus-263$0.094No
GLM-5.3 (low), Terminus-266$0.27No
GLM-5.3 (max), Terminus-270$0.38No
gpt-oss-120b (low), Terminus-231$0.0046No
gpt-oss-120b (high), Terminus-246$0.026No
gpt-oss-20b (high), Terminus-233$0.017No
Kimi K2.6 (Reasoning), Terminus-270$0.089No
Kimi K2.7 Code (Reasoning), Terminus-271$0.11No
Kimi K3 (Reasoning), Terminus-272$0.23No
Llama 3.3 70B (Non-reasoning), Terminus-28$0.023No
Mistral Large 3 (Non-reasoning), Terminus-233$0.026No
Qwen 3.8 27B (xhigh), Terminus-237, not every task attempted yet (27 of 488 tasks attempted)$0.055Not plotted: not yet ranked
Claude Haiku 4.5 (Reasoning), Claude Code58$0.092No
GLM-4.7-Flash (Reasoning), Claude Code41$0.27No
GLM-5.2 (max), Claude Code73, not every task attempted yet$0.34Not plotted: not yet ranked
GLM-5.3-Flash (max), Claude Code72, not every task attempted yet (449 of 488 tasks attempted)$0.093Not plotted: not yet ranked
GLM-5.3 (max), Claude Code73$0.48No
DeepSeek V4 Flash (high), Codex59, not every task attempted yet$0.085Not plotted: not yet ranked
DeepSeek V4 Pro (high), Codex62, not every task attempted yet (116 of 488 tasks attempted)$0.64Not plotted: not yet ranked
gpt-oss-120b (high), Codex43$0.26No
gpt-oss-20b (high), Codex9$0.094No
Kimi K2.6 (Reasoning), Kimi Code69$0.27No
Kimi K2.7 Code (Reasoning), Kimi Code72$0.18No
Qwen 3.8 27B (xhigh), Qwen Code68$0.22No
Claude Opus 5.5 (medium), Claude Code80$0.11No
Claude Opus 5.5 (high), Claude Code82$0.13Yes
Claude Sonnet 5.5 (medium), Claude Code71$0.046No
Claude Sonnet 5.5 (high), Claude Code78$0.055No
Claude Haiku 5.5 (medium), Claude Code72$0.0063Yes
Claude Haiku 5.5 (high), Claude Code76$0.0085Yes
Claude Opus 5.5 (xhigh), Claude Code85$0.25Yes
Claude Sonnet 5.5 (xhigh), Claude Code79$0.11No
Claude Haiku 5.5 (xhigh), Claude Code76$0.017Yes
Claude Opus 5 (high), Claude Code80$0.32No
Claude Sonnet 5 (high), Claude Code74$0.20No
Claude Opus 4.8 (high), Claude Code76$0.26No
Claude Opus 4.7 (high), Claude Code74$0.34No
GPT-6.1 Sol (medium), Codex82$0.046Yes
GPT-6.1 Sol (high), Codex80$0.059No
GPT-6.1 Sol (xhigh), Codex82$0.085Yes
GPT-6 Luna (xhigh), Codex73$0.0070Yes
GPT-6 Sol (medium), Codex80, not every task attempted yet (257 of 488 tasks attempted)$0.047Not plotted: not yet ranked
GPT-6 Sol (high), Codex80$0.082No
GPT-6 Luna (medium), Codex57$0.0026Yes
GPT-6 Luna (high), Codex71$0.0040Yes
GPT-5.6 Sol (medium), Codex100, not every task attempted yet (19 of 488 tasks attempted)$0.060Not plotted: not yet ranked
GPT-5.6 Sol (high), Codex75$0.21No
GPT-5.6 Terra (high), Codex69$0.10No
GPT-5.6 Luna (medium), Codex66$0.0094No
GPT-5.6 Luna (high), Codex72$0.016No
GPT-5.6 Luna (xhigh), Codex73$0.021No
GPT-6 Luna (low), Codex51$0.0032No
GPT-5.6 Luna (low), Codex61$0.0058No

* Not every task attempted yet: the score may still change. What the asterisk means.

Comparisons

The ten nearest entries by Geospatial Agent Index. All comparisons.

Specification and settings

Developer
Alibaba (Qwen)
Context window
262,144 tokens
Image input
Yes
Reasoning setting
xhigh
Temperature
0.6
Maximum output tokens
65,536
Input price per 1M tokens
$0.45
Cached input price per 1M tokens
$0.050
Output price per 1M tokens
$3.20

How models are selected.