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DeepSeek: geospatial agent results

Helps you choose the best DeepSeek model for geospatial work: GIS and Earth observation agent tasks, vision and knowledge, with cost and time.

Overview

  • For intelligence, the top DeepSeek models are DeepSeek V4 Pro (max) (79), DeepSeek V4 Pro (high) (79) and DeepSeek V4 Flash (high) (78) on the Geospatial Intelligence Index (Text only).
  • For cost, the cheapest per agent task are DeepSeek V4 Flash (max) ($0.062), DeepSeek V4 Flash (high) ($0.065) and DeepSeek V4.1 Flash (max) ($0.067).
  • For speed, the fastest per agent task are DeepSeek V4 Pro (high) (4.0 min), DeepSeek V4 Pro (max) (4.9 min) and DeepSeek V4 Flash (max) (5.7 min).
  • For context window, the largest are DeepSeek V4 Flash: 1M tokens, DeepSeek V4 Pro: 1M tokens and DeepSeek V4.1 Flash: 1M tokens.
  • Still being benchmarked (provisional): DeepSeek V4 Pro (high).

Intelligence

Geospatial Intelligence Index, Text only · Higher is better

IntelligenceScores 0 to 100: DeepSeek V4 Pro (max): 79; DeepSeek V4 Pro (high): 79; DeepSeek V4 Flash (high): 78; DeepSeek V4 Flash (max): 770255075100DeepSeek V4 Pro (max)79DeepSeek V4 Pro (high)79DeepSeek V4 Flash (high)78DeepSeek V4 Flash (max)77
Data table: Intelligence
Intelligence
ModelDeveloperIntelligence
DeepSeek V4 Pro (max)DeepSeek79
DeepSeek V4 Pro (high)DeepSeek79
DeepSeek V4 Flash (high)DeepSeek78
DeepSeek V4 Flash (max)DeepSeek77

Cost per task

USD per agent task · Lower is better

Cost per taskCost per task in US dollars: DeepSeek V4 Flash (max), Terminus-2: $0.062; DeepSeek V4 Flash (high), Terminus-2: $0.065; DeepSeek V4.1 Flash (max), Codex: $0.067; DeepSeek V4 Flash (high), Codex: $0.085; DeepSeek V4 Pro (high), Terminus-2: $0.30; DeepSeek V4 Pro (max), Terminus-2: $0.35$0$0.10$0.20$0.30$0.40DeepSeek V4 Flash (max)Terminus-2$0.062DeepSeek V4 Flash (high)Terminus-2$0.065DeepSeek V4.1 Flash (max)Codex$0.067DeepSeek V4 Flash (high)Codex$0.085DeepSeek V4 Pro (high)Terminus-2$0.30DeepSeek V4 Pro (max)Terminus-2$0.35
Data table: Cost per task

Time per task

Minutes per agent task · Lower is better

Time per taskTime per task: DeepSeek V4 Pro (high), Terminus-2: 4.0 min; DeepSeek V4 Pro (max), Terminus-2: 4.9 min; DeepSeek V4 Flash (max), Terminus-2: 5.7 min; DeepSeek V4 Flash (high), Terminus-2: 5.9 min; DeepSeek V4 Flash (high), Codex: 6.5 min; DeepSeek V4.1 Flash (max), Codex: 8.6 min02.557.510DeepSeek V4 Pro (high)Terminus-24.0 minDeepSeek V4 Pro (max)Terminus-24.9 minDeepSeek V4 Flash (max)Terminus-25.7 minDeepSeek V4 Flash (high)Terminus-25.9 minDeepSeek V4 Flash (high)Codex6.5 minDeepSeek V4.1 Flash (max)Codex8.6 min
Data table: Time per task
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
DeepSeek V4 Pro (high), Terminus-2DeepSeek4.0 min
DeepSeek V4 Pro (max), Terminus-2DeepSeek4.9 min
DeepSeek V4 Flash (max), Terminus-2DeepSeek5.7 min
DeepSeek V4 Flash (high), Terminus-2DeepSeek5.9 min
DeepSeek V4 Flash (high), CodexDeepSeek6.5 min
DeepSeek V4.1 Flash (max), CodexDeepSeek8.6 min

Models

DeepSeek V4 Flash

Open weightsText only

1M token context · Released 31 July 2026

$0.44 input · $1.32 output per 1M tokens

DeepSeek V4 Pro

Open weightsText only

1M token context · Released 13 August 2026

$1.32 input · $3.96 output per 1M tokens

DeepSeek V4.1 Flash

Open weightsReads images

1M token context · Released 10 September 2026

$0.30 input · $1.20 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

Geospatial Intelligence Index: Text onlyScores 0 to 100: Claude Opus 5.5 (xhigh): 89; GPT-6.1 Sol (medium): 86; DeepSeek V4 Pro (max): 79; Gemini 3.8 Flash (low): 79; DeepSeek V4 Pro (high): 79; DeepSeek V4 Flash (high): 78; DeepSeek V4 Flash (max): 770255075100Claude Opus 5.5 (xhigh)89GPT-6.1 Sol (medium)86DeepSeek V4 Pro (max)79Gemini 3.8 Flash (low)79DeepSeek V4 Pro (high)79DeepSeek V4 Flash (high)78DeepSeek V4 Flash (max)77
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 DeepSeek 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
Geospatial Intelligence Index: Text only
ModelDeveloperGeospatial Intelligence Index: Text only
Claude Opus 5.5 (xhigh)Anthropic89
GPT-6.1 Sol (medium)OpenAI86
DeepSeek V4 Pro (max)DeepSeek79
Gemini 3.8 Flash (low)Google79
DeepSeek V4 Pro (high)DeepSeek79
DeepSeek V4 Flash (high)DeepSeek78
DeepSeek V4 Flash (max)DeepSeek77

Text + vision

No DeepSeek model has a Text + vision score yet: that view also needs Vision agents and Vision results.

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

Geospatial Agent Index: Text onlyScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 85; GPT-6.1 Sol (xhigh), Codex: 82; GLM-5.3 (max), Claude Code: 73; DeepSeek V4 Flash (high), Terminus-2: 72; DeepSeek V4 Pro (max), Terminus-2: 71; DeepSeek V4.1 Flash (max), Codex: 71; DeepSeek V4 Pro (high), Terminus-2: 70; DeepSeek V4 Flash (max), Terminus-2: 69; DeepSeek V4 Flash (high), Codex: 590255075100Claude Opus 5.5 (xhigh)Claude Code85GPT-6.1 Sol (xhigh)Codex82GLM-5.3 (max)Claude Code73DeepSeek V4 Flash (high)Terminus-272DeepSeek V4 Pro (max)Terminus-271DeepSeek V4.1 Flash (max)Codex71DeepSeek V4 Pro (high)Terminus-270DeepSeek V4 Flash (max)Terminus-269DeepSeek V4 Flash (high)Codex59
How to read this chart

Bars. Every DeepSeek 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

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

Geospatial Agent Index: Text + visionScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 88; GPT-6.1 Sol (xhigh), Codex: 84; GLM-5.3-Flash (max), Claude Code: 73; DeepSeek V4.1 Flash (max), Codex: 730255075100Claude Opus 5.5 (xhigh)Claude Code88GPT-6.1 Sol (xhigh)Codex84GLM-5.3-Flash (max)Claude Code73DeepSeek V4.1 Flash (max)Codex73
How to read this chart

Bars. Every DeepSeek 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
Geospatial Agent Index: Text + vision
ModelDeveloperGeospatial Agent Index: Text + vision
Claude Opus 5.5 (xhigh), Claude CodeAnthropic88
GPT-6.1 Sol (xhigh), CodexOpenAI84
GLM-5.3-Flash (max), Claude CodeZ.ai73
DeepSeek V4.1 Flash (max), CodexDeepSeek73

Provisional, so not on the index charts: DeepSeek V4 Pro (high), Codex. 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
Earth-Bench V2Scores 0 to 100: Claude Opus 5.5 (high), Claude Code: 79; GPT-6 Sol (high), Codex: 75; DeepSeek V4 Flash (high), Terminus-2: 72; DeepSeek V4 Pro (high), Terminus-2: 68; DeepSeek V4 Pro (max), Terminus-2: 67; DeepSeek V4.1 Flash (max), Codex: 66; DeepSeek V4 Flash (max), Terminus-2: 66; DeepSeek V4 Flash (high), Codex: 57; DeepSeek V4 Pro (high), Codex: 57 (provisional, not yet ranked)0255075100Claude Opus 5.5 (high)Claude Code79GPT-6 Sol (high)Codex75DeepSeek V4 Flash (high)Terminus-272DeepSeek V4 Pro (high)Terminus-268DeepSeek V4 Pro (max)Terminus-267DeepSeek V4.1 Flash (max)Codex66DeepSeek V4 Flash (max)Terminus-266DeepSeek V4 Flash (high)Codex57DeepSeek V4 Pro (high)Codex · provisional57
Data table: Earth-Bench V2

GeoBenchX

Maps, numbers and lists from country and city data; some tasks should be declined · 0 to 100 · Higher is better

  • Striped: provisional, not yet ranked
GeoBenchXScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 83; GPT-6.1 Sol (medium), Codex: 79; DeepSeek V4 Pro (max), Terminus-2: 67; DeepSeek V4 Pro (high), Terminus-2: 65; DeepSeek V4 Flash (high), Terminus-2: 65; DeepSeek V4 Flash (max), Terminus-2: 62; DeepSeek V4.1 Flash (max), Codex: 56; DeepSeek V4 Flash (high), Codex: 47; DeepSeek V4 Pro (high), Codex: 66 (provisional, not yet ranked)0255075100Claude Opus 5.5 (xhigh)Claude Code83GPT-6.1 Sol (medium)Codex79DeepSeek V4 Pro (max)Terminus-267DeepSeek V4 Pro (high)Terminus-265DeepSeek V4 Flash (high)Terminus-265DeepSeek V4 Flash (max)Terminus-262DeepSeek V4.1 Flash (max)Codex56DeepSeek V4 Flash (high)Codex47DeepSeek V4 Pro (high)Codex · provisional66
Data table: GeoBenchX

GS-QA2

Spatial data reasoning over map and elevation data · 0 to 100 · Higher is better

GS-QA2Scores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 94; GPT-6.1 Sol (xhigh), Codex: 94; DeepSeek V4.1 Flash (max), Codex: 89; DeepSeek V4 Pro (max), Terminus-2: 80; DeepSeek V4 Flash (max), Terminus-2: 80; DeepSeek V4 Flash (high), Terminus-2: 79; DeepSeek V4 Pro (high), Terminus-2: 77; DeepSeek V4 Flash (high), Codex: 740255075100Claude Opus 5.5 (xhigh)Claude Code94GPT-6.1 Sol (xhigh)Codex94DeepSeek V4.1 Flash (max)Codex89DeepSeek V4 Pro (max)Terminus-280DeepSeek V4 Flash (max)Terminus-280DeepSeek V4 Flash (high)Terminus-279DeepSeek V4 Pro (high)Terminus-277DeepSeek V4 Flash (high)Codex74
Data table: GS-QA2

GeoAgentBench

Guided workflows: multi-step spatial execution · 0 to 100 · Higher is better

GeoAgentBenchScores 0 to 100: Claude Opus 5.5 (high), Claude Code: 100; GPT-6.1 Sol (medium), Codex: 100; DeepSeek V4 Flash (max), Terminus-2: 96; DeepSeek V4 Pro (max), Terminus-2: 96; DeepSeek V4 Pro (high), Terminus-2: 96; DeepSeek V4 Flash (high), Terminus-2: 94; DeepSeek V4.1 Flash (max), Codex: 90; DeepSeek V4 Flash (high), Codex: 880255075100Claude Opus 5.5 (high)Claude Code100GPT-6.1 Sol (medium)Codex100DeepSeek V4 Flash (max)Terminus-296DeepSeek V4 Pro (max)Terminus-296DeepSeek V4 Pro (high)Terminus-296DeepSeek V4 Flash (high)Terminus-294DeepSeek V4.1 Flash (max)Codex90DeepSeek V4 Flash (high)Codex88
Data table: GeoAgentBench

GeoAnalystBench

Guided workflows: spatial workflow and code generation · 0 to 100 · Higher is better

GeoAnalystBenchScores 0 to 100: DeepSeek V4 Pro (max), Terminus-2: 100; DeepSeek V4 Pro (high), Terminus-2: 100; GLM-5.2 (high), Terminus-2: 100; Claude Opus 5.5 (high), Claude Code: 100; DeepSeek V4 Flash (max), Terminus-2: 95; DeepSeek V4 Flash (high), Terminus-2: 89; DeepSeek V4 Flash (high), Codex: 89; DeepSeek V4.1 Flash (max), Codex: 880255075100DeepSeek V4 Pro (max)Terminus-2100DeepSeek V4 Pro (high)Terminus-2100GLM-5.2 (high)Terminus-2100Claude Opus 5.5 (high)Claude Code100DeepSeek V4 Flash (max)Terminus-295DeepSeek V4 Flash (high)Terminus-289DeepSeek V4 Flash (high)Codex89DeepSeek V4.1 Flash (max)Codex88
Data table: GeoAnalystBench

Vision

ThinkGeo

Vision agents: agents that look at images and use code · 0 to 100 · Higher is better

ThinkGeoScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 96; GPT-6.1 Sol (xhigh), Codex: 88; DeepSeek V4.1 Flash (max), Codex: 70025507510096Claude Opus5.5 (xhigh)Claude Code88GPT-6.1 Sol(xhigh)Codex70DeepSeek V4.1Flash (max)Codex
Data table: ThinkGeo

OpenEarthAgent

Vision agents: agents that look at images and use code · 0 to 100 · Higher is better

OpenEarthAgentScores 0 to 100: Claude Opus 5.5 (medium), Claude Code: 100; GPT-6.1 Sol (medium), Codex: 100; DeepSeek V4.1 Flash (max), Codex: 940255075100100Claude Opus5.5 (medium)Claude Code100GPT-6.1 Sol(medium)Codex94DeepSeek V4.1Flash (max)Codex
Data table: OpenEarthAgent
OpenEarthAgent
ModelDeveloperOpenEarthAgent
Claude Opus 5.5 (medium), Claude CodeAnthropic100
GPT-6.1 Sol (medium), CodexOpenAI100
DeepSeek V4.1 Flash (max), CodexDeepSeek94

Knowledge

Geospatial knowledge

Questions on geospatial knowledge · 0 to 100 · Higher is better

Geospatial knowledgeScores 0 to 100: Claude Sonnet 4.6 (high): 98; Gemini 3.8 Flash (low): 94; DeepSeek V4 Pro (high): 92; DeepSeek V4 Pro (max): 92; DeepSeek V4 Flash (max): 91; DeepSeek V4 Flash (high): 910255075100Claude Sonnet 4.6 (high)98Gemini 3.8 Flash (low)94DeepSeek V4 Pro (high)92DeepSeek V4 Pro (max)92DeepSeek V4 Flash (max)91DeepSeek V4 Flash (high)91
Data table: Geospatial knowledge
Geospatial knowledge
ModelDeveloperGeospatial knowledge
Claude Sonnet 4.6 (high)Anthropic98
Gemini 3.8 Flash (low)Google94
DeepSeek V4 Pro (high)DeepSeek92
DeepSeek V4 Pro (max)DeepSeek92
DeepSeek V4 Flash (max)DeepSeek91
DeepSeek V4 Flash (high)DeepSeek91

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: DeepSeek V4 Flash (max), Terminus-2: $0.062; DeepSeek V4 Flash (high), Terminus-2: $0.065; DeepSeek V4.1 Flash (max), Codex: $0.067; GPT-6.1 Sol (xhigh), Codex: $0.085; DeepSeek V4 Flash (high), Codex: $0.085; Claude Opus 5.5 (xhigh), Claude Code: $0.25; DeepSeek V4 Pro (high), Terminus-2: $0.30; DeepSeek V4 Pro (max), Terminus-2: $0.35; DeepSeek V4 Pro (high), Codex: $0.64 (provisional, not yet ranked)$0$0.20$0.40$0.60$0.80DeepSeek V4 Flash (max)Terminus-2$0.062DeepSeek V4 Flash (high)Terminus-2$0.065DeepSeek V4.1 Flash (max)Codex$0.067GPT-6.1 Sol (xhigh)Codex$0.085DeepSeek V4 Flash (high)Codex$0.085Claude Opus 5.5 (xhigh)Claude Code$0.25DeepSeek V4 Pro (high)Terminus-2$0.30DeepSeek V4 Pro (max)Terminus-2$0.35DeepSeek 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 DeepSeek 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
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; DeepSeek V4 Flash (high), Codex: 59.29, $0.085; DeepSeek V4.1 Flash (max), Codex: 70.68, $0.067; Claude Opus 5.5 (xhigh), Claude Code: 84.65, $0.25; GPT-6.1 Sol (xhigh), Codex: 81.82, $0.085; GLM-5.3 (max), Claude Code: 73.4, $0.48Most attractive quadrant5560657075808590$0.05$0.10$0.20$0.50$1.00Cost per task (USD, log scale)Geospatial Agent IndexDeepSeek V4 Flash (high), Terminus-2DeepSeek V4 Flash (max), Terminus-2DeepSeek V4 Pro (max), Terminus-2DeepSeek V4.1 Flash (max), CodexDeepSeek V4 Pro (high), Terminus-2DeepSeek V4 Flash (high), CodexClaude Opus 5.5 (xhigh), Claude CodeGPT-6.1 Sol (xhigh), CodexGLM-5.3 (max), Claude Code
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
Geospatial Agent Index vs. cost per task
ModelGeospatial Agent IndexCost per task (USD)On the Pareto line
DeepSeek V4 Flash (max), Terminus-269$0.062Yes
DeepSeek V4 Flash (high), Terminus-272$0.065Yes
DeepSeek V4 Pro (max), Terminus-271$0.35No
DeepSeek V4 Pro (high), Terminus-270$0.30No
DeepSeek V4 Flash (high), Codex59$0.085No
DeepSeek V4.1 Flash (max), Codex71$0.067No
Claude Opus 5.5 (xhigh), Claude Code85$0.25Yes
GPT-6.1 Sol (xhigh), Codex82$0.085Yes
GLM-5.3 (max), Claude Code73$0.48No

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-6.1 Sol (xhigh), Codex: 0.8 min; Claude Opus 5.5 (xhigh), Claude Code: 1.4 min; DeepSeek V4 Pro (high), Terminus-2: 4.0 min; DeepSeek V4 Pro (max), Terminus-2: 4.9 min; DeepSeek V4 Flash (max), Terminus-2: 5.7 min; DeepSeek V4 Flash (high), Terminus-2: 5.9 min; DeepSeek V4 Flash (high), Codex: 6.5 min; DeepSeek V4.1 Flash (max), Codex: 8.6 min; DeepSeek V4 Pro (high), Codex: 4.9 min (provisional, not yet ranked)02.557.510GPT-6.1 Sol (xhigh)Codex0.8 minClaude Opus 5.5 (xhigh)Claude Code1.4 minDeepSeek V4 Pro (high)Terminus-24.0 minDeepSeek V4 Pro (max)Terminus-24.9 minDeepSeek V4 Flash (max)Terminus-25.7 minDeepSeek V4 Flash (high)Terminus-25.9 minDeepSeek V4 Flash (high)Codex6.5 minDeepSeek V4.1 Flash (max)Codex8.6 minDeepSeek V4 Pro (high)Codex · provisional4.9 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 DeepSeek 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
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
GPT-6.1 Sol (xhigh), CodexOpenAI0.8 min
Claude Opus 5.5 (xhigh), Claude CodeAnthropic1.4 min
DeepSeek V4 Pro (high), Terminus-2DeepSeek4.0 min
DeepSeek V4 Pro (max), Terminus-2DeepSeek4.9 min
DeepSeek V4 Flash (max), Terminus-2DeepSeek5.7 min
DeepSeek V4 Flash (high), Terminus-2DeepSeek5.9 min
DeepSeek V4 Flash (high), CodexDeepSeek6.5 min
DeepSeek V4.1 Flash (max), CodexDeepSeek8.6 min
DeepSeek V4 Pro (high), CodexDeepSeek4.9 min

Geospatial Agent Index vs. time per task

Geospatial Agent Index, Text only; time per task (minutes) · Higher and further left is better

  • Most attractive quadrant
  • Pareto line
Geospatial Agent Index vs. time per taskGeospatial Agent Index (0 to 100) against time per task (minutes), log scale: DeepSeek V4 Flash (max), Terminus-2: 69.11, 5.7 min; DeepSeek V4 Flash (high), Terminus-2: 71.86, 5.9 min; DeepSeek V4 Pro (max), Terminus-2: 71, 4.9 min; DeepSeek V4 Pro (high), Terminus-2: 70.31, 4.0 min; DeepSeek V4 Flash (high), Codex: 59.29, 6.5 min; DeepSeek V4.1 Flash (max), Codex: 70.68, 8.6 min; Claude Opus 5.5 (xhigh), Claude Code: 84.65, 1.4 min; GPT-6.1 Sol (xhigh), Codex: 81.82, 0.8 min; GLM-5.3 (max), Claude Code: 73.4, 4.3 minMost attractive quadrant55606570758085900.5 min1 min2 min5 min10 minTime per task (minutes, log scale)Geospatial Agent IndexDeepSeek V4 Flash (high), Terminus-2DeepSeek V4 Pro (max), Terminus-2DeepSeek V4.1 Flash (max), CodexDeepSeek V4 Pro (high), Terminus-2DeepSeek V4 Flash (max), Terminus-2DeepSeek V4 Flash (high), CodexClaude Opus 5.5 (xhigh), Claude CodeGPT-6.1 Sol (xhigh), CodexGLM-5.3 (max), Claude Code
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
Geospatial Agent Index vs. time per task
ModelGeospatial Agent IndexTime per task (minutes)On the Pareto line
DeepSeek V4 Flash (max), Terminus-2695.7 minNo
DeepSeek V4 Flash (high), Terminus-2725.9 minNo
DeepSeek V4 Pro (max), Terminus-2714.9 minNo
DeepSeek V4 Pro (high), Terminus-2704.0 minNo
DeepSeek V4 Flash (high), Codex596.5 minNo
DeepSeek V4.1 Flash (max), Codex718.6 minNo
Claude Opus 5.5 (xhigh), Claude Code851.4 minYes
GPT-6.1 Sol (xhigh), Codex820.8 minYes
GLM-5.3 (max), Claude Code734.3 minNo

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-QA2GeoAgentBenchGeoAnalystBenchThinkGeoOpenEarthAgentGeospatial knowledge
DeepSeek V4 Flash (high), Terminus-2Ranked7265799489No dataNo data91
DeepSeek V4 Pro (max), Terminus-2Ranked67678096100No dataNo data92
DeepSeek V4.1 Flash (max), CodexRanked66568990887094No data
DeepSeek V4 Pro (high), Terminus-2Ranked68657796100No dataNo data92
DeepSeek V4 Flash (max), Terminus-2Ranked6662809695No dataNo data91
DeepSeek V4 Flash (high), CodexRanked5747748889No dataNo data91
DeepSeek V4 Pro (high), CodexProvisional57 (provisional)66 (provisional)No dataNo dataNo dataNo dataNo data92

Frequently asked questions

What DeepSeek models do you benchmark?
We benchmark 3 DeepSeek models: DeepSeek V4 Flash, DeepSeek V4 Pro and DeepSeek V4.1 Flash.
Which is the most intelligent DeepSeek model for geospatial work?
DeepSeek V4 Pro (max), with 79.0 on the Geospatial Intelligence Index (Text only), #23 of 61.
What is the cheapest DeepSeek model per task?
DeepSeek V4 Flash (max), Terminus-2, at $0.062 per agent task at list prices, #32 of 74 on the Geospatial Agent Index.
What is the fastest DeepSeek model?
DeepSeek V4 Pro (high), Terminus-2, at 4.0 min per agent task at 100 output tokens per second, #50 of 74.
Does DeepSeek offer open-weights models?
Yes: DeepSeek V4 Flash, DeepSeek V4 Pro and DeepSeek V4.1 Flash. Their weights are published, so you can run them on your own hardware.
Which DeepSeek models can read satellite imagery?
DeepSeek V4.1 Flash takes images as input.
How do DeepSeek models do on Earth observation, GIS and geospatial knowledge?
Which DeepSeek model is best for agent work, and in which harness?
DeepSeek V4 Flash (high), Terminus-2, with 71.9 on the Geospatial Agent Index, #24 of 74, in the Terminus-2 harness.
How does DeepSeek's best model compare with the overall leader?
DeepSeek V4 Pro (max) scores 79.0 on the Geospatial Intelligence Index (Text only), 9.7 points behind the leader, Anthropic's Claude Opus 5.5 (xhigh), with 88.7.
Are any DeepSeek models still being benchmarked?
Yes. DeepSeek V4 Pro (high), Codex is still being benchmarked, so their results are provisional: not yet ranked.
How should I choose between DeepSeek models for geospatial work?
Start from the job. For the strongest answers, DeepSeek V4 Pro (max); for agent work with data and code, DeepSeek V4 Flash (high), Terminus-2; for the lowest cost, DeepSeek V4 Flash (max), Terminus-2; to run it yourself, DeepSeek V4 Flash. Each model page shows its scores on every evaluation.