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StepFun: geospatial agent results

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

Overview

Most intelligent

  1. #1Step 5 Preview (Reasoning) Open weights56

Geospatial Intelligence Index, Text only

Lowest cost

  1. #1Step 5 Preview (Reasoning), Terminus-2$0.087

Cost per agent task, at list prices

Fastest

  1. #1Step 5 Preview (Reasoning), Terminus-29.2 min (estimated)

Time per agent task, at 100 output tokens/s

  • For intelligence, the top StepFun models are Step 5 Preview (Reasoning) (56) on the Geospatial Intelligence Index (Text only).
  • For cost, the cheapest per agent task are Step 5 Preview (Reasoning) ($0.087).
  • For speed, the fastest per agent task are Step 5 Preview (Reasoning) (9.2 min (estimated)).
  • For context window, the largest are Step 5 Preview: 1M tokens.

Intelligence

Geospatial Intelligence Index, Text only · Higher is better

IntelligenceScores 0 to 100: Step 5 Preview (Reasoning): 56025507510056Step 5Preview(Reasoning)
Data table: Intelligence
Intelligence
ModelDeveloperIntelligence
Step 5 Preview (Reasoning)StepFun56

Cost per task

USD per agent task · Lower is better

Cost per taskCost per task in US dollars: Step 5 Preview (Reasoning), Terminus-2: $0.087$0$0.025$0.05$0.075$0.10$0.087Step 5Preview(Reasoning)Terminus-2
Data table: Cost per task
Cost per task
ModelDeveloperCost per task
Step 5 Preview (Reasoning), Terminus-2StepFun$0.087

Time per task

Minutes per agent task · Lower is better

Time per taskTime per task: Step 5 Preview (Reasoning), Terminus-2: 9.2 min02.557.5109.2 minStep 5Preview(Reasoning)Terminus-2
Data table: Time per task
Time per task
ModelDeveloperTime per task (at 100 output tokens/s)
Step 5 Preview (Reasoning), Terminus-2StepFun9.2 min

Models

Step 5 Preview

Open weightsReads images

1M token context

$1.00 input · $2.70 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; Step 5 Preview (Reasoning): 560255075100Claude Opus 5.5 (xhigh)89GPT-6.1 Sol (medium)86DeepSeek V4 Pro (max)79Step 5 Preview (Reasoning)56
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 StepFun 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
Step 5 Preview (Reasoning)StepFun56

Text + vision

No StepFun 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; Step 5 Preview (Reasoning), Terminus-2: 400255075100Claude Opus 5.5 (xhigh)Claude Code85GPT-6.1 Sol (xhigh)Codex82GLM-5.3 (max)Claude Code73Step 5 Preview (Reasoning)Terminus-240
How to read this chart

Bars. Every StepFun 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
Geospatial Agent Index: Text only
ModelDeveloperGeospatial Agent Index: Text only
Claude Opus 5.5 (xhigh), Claude CodeAnthropic85
GPT-6.1 Sol (xhigh), CodexOpenAI82
GLM-5.3 (max), Claude CodeZ.ai73
Step 5 Preview (Reasoning), Terminus-2StepFun40

Text + vision

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

Earth-Bench V2

Earth observation data analysis · 0 to 100 · Higher is better

Earth-Bench V2Scores 0 to 100: Claude Opus 5.5 (high), Claude Code: 79; GPT-6 Sol (high), Codex: 75; Step 5 Preview (Reasoning), Terminus-2: 41025507510079Claude Opus5.5 (high)Claude Code75GPT-6 Sol(high)Codex41Step 5Preview(Reasoning)Terminus-2
Data table: Earth-Bench V2
Earth-Bench V2
ModelDeveloperEarth-Bench V2
Claude Opus 5.5 (high), Claude CodeAnthropic79
GPT-6 Sol (high), CodexOpenAI75
Step 5 Preview (Reasoning), Terminus-2StepFun41

GeoBenchX

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

GeoBenchXScores 0 to 100: Claude Opus 5.5 (xhigh), Claude Code: 83; GPT-6.1 Sol (medium), Codex: 79; Step 5 Preview (Reasoning), Terminus-2: 18025507510083Claude Opus5.5 (xhigh)Claude Code79GPT-6.1 Sol(medium)Codex18Step 5Preview(Reasoning)Terminus-2
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; Step 5 Preview (Reasoning), Terminus-2: 61025507510094Claude Opus5.5 (xhigh)Claude Code94GPT-6.1 Sol(xhigh)Codex61Step 5Preview(Reasoning)Terminus-2
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; Step 5 Preview (Reasoning), Terminus-2: 840255075100100Claude Opus5.5 (high)Claude Code100GPT-6.1 Sol(medium)Codex84Step 5Preview(Reasoning)Terminus-2
Data table: GeoAgentBench
GeoAgentBench
ModelDeveloperGeoAgentBench
Claude Opus 5.5 (high), Claude CodeAnthropic100
GPT-6.1 Sol (medium), CodexOpenAI100
Step 5 Preview (Reasoning), Terminus-2StepFun84

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; GLM-5.2 (high), Terminus-2: 100; Step 5 Preview (Reasoning), Terminus-2: 890255075100100DeepSeek V4Pro (max)Terminus-2100GLM-5.2(high)Terminus-289Step 5Preview(Reasoning)Terminus-2
Data table: GeoAnalystBench
GeoAnalystBench
ModelDeveloperGeoAnalystBench
DeepSeek V4 Pro (max), Terminus-2DeepSeek100
GLM-5.2 (high), Terminus-2Z.ai100
Step 5 Preview (Reasoning), Terminus-2StepFun89

OGD4All

Not in an index: questions on a city's open government data · 0 to 100 · Higher is better

OGD4AllScores 0 to 100: Claude Opus 4.8 (high), Claude Code: 97; GLM-5.3 (max), Claude Code: 96; Step 5 Preview (Reasoning), Terminus-2: 94025507510097Claude Opus4.8 (high)Claude Code96GLM-5.3 (max)Claude Code94Step 5Preview(Reasoning)Terminus-2
Data table: OGD4All

GeoNatureAgent

Not in an index: environmental raster analysis over named regions · 0 to 100 · Higher is better

GeoNatureAgentScores 0 to 100: Step 5 Preview (Reasoning), Terminus-2: 100; Qwen 3.8 27B (xhigh), Terminus-2: 100; GLM-5.3-Flash (max), Claude Code: 1000255075100100Step 5Preview(Reasoning)Terminus-2100Qwen 3.8 27B(xhigh)Terminus-2100GLM-5.3-Flash(max)Claude Code
Data table: GeoNatureAgent
GeoNatureAgent
ModelDeveloperGeoNatureAgent
Step 5 Preview (Reasoning), Terminus-2StepFun100
Qwen 3.8 27B (xhigh), Terminus-2Alibaba (Qwen)100
GLM-5.3-Flash (max), Claude CodeZ.ai100

MultiGlobeQA

Not in an index: spatial reasoning over facts about places worldwide · 0 to 100 · Higher is better

MultiGlobeQAScores 0 to 100: Step 5 Preview (Reasoning), Terminus-2: 83; Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2: 82; Claude Sonnet 5.5 (xhigh), Claude Code: 79025507510083Step 5Preview(Reasoning)Terminus-282Nemotron 3.5Super VL 120B(Reasoning)Terminus-279Claude Sonnet5.5 (xhigh)Claude Code
Data table: MultiGlobeQA

GeoAgentEval

Not in an index: building, auditing and repairing a PostGIS database · 0 to 100 · Higher is better

GeoAgentEvalScores 0 to 100: Claude Opus 5.5 (medium), Claude Code: 100; GLM-5.3-Flash (max), Claude Code: 96; Step 5 Preview (Reasoning), Terminus-2: 950255075100100Claude Opus5.5 (medium)Claude Code96GLM-5.3-Flash(max)Claude Code95Step 5Preview(Reasoning)Terminus-2
Data table: GeoAgentEval

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; Step 5 Preview (Reasoning): 92025507510098Claude Sonnet4.6 (high)94Gemini 3.8Flash (low)92Step 5Preview(Reasoning)
Data table: Geospatial knowledge
Geospatial knowledge
ModelDeveloperGeospatial knowledge
Claude Sonnet 4.6 (high)Anthropic98
Gemini 3.8 Flash (low)Google94
Step 5 Preview (Reasoning)StepFun92

Cost

Cost per task

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

Cost per taskCost per task in US dollars: GPT-6.1 Sol (xhigh), Codex: $0.085; Step 5 Preview (Reasoning), Terminus-2: $0.087; Claude Opus 5.5 (xhigh), Claude Code: $0.25$0$0.10$0.20$0.30$0.40$0.085GPT-6.1 Sol(xhigh)Codex$0.087Step 5Preview(Reasoning)Terminus-2$0.25Claude Opus5.5 (xhigh)Claude Code
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 StepFun 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
Cost per task
ModelDeveloperCost per task
GPT-6.1 Sol (xhigh), CodexOpenAI$0.085
Step 5 Preview (Reasoning), Terminus-2StepFun$0.087
Claude Opus 5.5 (xhigh), Claude CodeAnthropic$0.25

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: Step 5 Preview (Reasoning), Terminus-2: 40.02, $0.087; 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 quadrant30405060708090$0.05$0.10$0.20$0.50$1.00Cost per task (USD, log scale)Geospatial Agent IndexStep 5 Preview (Reasoning), Terminus-2Claude 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
Step 5 Preview (Reasoning), Terminus-240$0.087No
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

Time per taskTime per task: GPT-6.1 Sol (xhigh), Codex: 0.8 min; Claude Opus 5.5 (xhigh), Claude Code: 1.4 min; Step 5 Preview (Reasoning), Terminus-2: 9.2 min02.557.5100.8 minGPT-6.1 Sol(xhigh)Codex1.4 minClaude Opus5.5 (xhigh)Claude Code9.2 minStep 5Preview(Reasoning)Terminus-2
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 StepFun 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
Step 5 Preview (Reasoning), Terminus-2StepFun9.2 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: Step 5 Preview (Reasoning), Terminus-2: 40.02, 9.2 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 quadrant304050607080900.5 min1 min2 min5 min10 min20 minTime per task (minutes, log scale)Geospatial Agent IndexStep 5 Preview (Reasoning), Terminus-2Claude 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
Step 5 Preview (Reasoning), Terminus-2409.2 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-QA2GeoAgentBenchGeoAnalystBenchOGD4AllGeoNatureAgentMultiGlobeQAGeoAgentEvalGeospatial knowledge
Step 5 Preview (Reasoning), Terminus-2Ranked411861848994100839592

Frequently asked questions

What StepFun models do you benchmark?
We benchmark 1 StepFun model: Step 5 Preview.
Which is the most intelligent StepFun model for geospatial work?
Step 5 Preview (Reasoning), with 56.4 on the Geospatial Intelligence Index (Text only), #51 of 61.
What is the cheapest StepFun model per task?
Step 5 Preview (Reasoning), Terminus-2, at $0.087 per agent task at list prices, #40 of 74 on the Geospatial Agent Index.
What is the fastest StepFun model?
Step 5 Preview (Reasoning), Terminus-2, at 9.2 min (estimated) per agent task at 100 output tokens per second, #74 of 74.
Does StepFun offer open-weights models?
Yes: Step 5 Preview. Their weights are published, so you can run them on your own hardware.
Which StepFun models can read satellite imagery?
Step 5 Preview takes images as input.
How do StepFun models do on Earth observation, GIS and geospatial knowledge?
Which StepFun model is best for agent work, and in which harness?
Step 5 Preview (Reasoning), Terminus-2, with 40.0 on the Geospatial Agent Index, #66 of 74, in the Terminus-2 harness.
How does StepFun's best model compare with the overall leader?
Step 5 Preview (Reasoning) scores 56.4 on the Geospatial Intelligence Index (Text only), 32.3 points behind the leader, Anthropic's Claude Opus 5.5 (xhigh), with 88.7.
Are any StepFun models still being benchmarked?
No. Every StepFun model listed has finished its benchmark and is ranked.
How should I choose between StepFun models for geospatial work?
Start from the job. For the strongest answers, Step 5 Preview (Reasoning); for agent work with data and code, Step 5 Preview (Reasoning), Terminus-2; for the lowest cost, Step 5 Preview (Reasoning), Terminus-2; to run it yourself, Step 5 Preview. Each model page shows its scores on every evaluation.