Overview
In Claude Code its highest evaluation score is 100, on GeoAgentBench. It costs $0.54 per task, more than the median entry ($0.081) and takes 5.6 min per task, slower than the median entry (2.2 min).
Technical specification
- Developer
- Anthropic
- Released
- 28 September 2026
- Weights
- Not published
- Harness
- Claude Code 2.1.286
- Reasoning setting
- max
- Context window
- 1,000,000 tokens
- Maximum output tokens
- 128,000
- Image input
- Yes
- Temperature
- Provider default
- Input price per 1M tokens
- $2.00
- Cached input price per 1M tokens
- $0.10
- Output price per 1M tokens
- $10.00
- Map and trace judge
- Claude Sonnet 5.5
- Reward hacking
- 0.5% of reviewed passing attempts flaggedSafety page
By evaluation
All
Earth-Bench V2
higher is better
Data table: Earth-Bench V2
GeoBenchX
higher is better
Data table: GeoBenchX
GS-QA2
higher is better
Data table: GS-QA2
GeoAgentBench
higher is better
Data table: GeoAgentBench
GeoAnalystBench
higher is better
Data table: GeoAnalystBench
ThinkGeo
higher is better
Data table: ThinkGeo
OpenEarthAgent
higher is better
Data table: OpenEarthAgent
OGD4All
higher is better
Data table: OGD4All
GeoNatureAgent
higher is better
Data table: GeoNatureAgent
MultiGlobeQA
higher is better
Data table: MultiGlobeQA
GeoAgentEval
higher is better
Data table: GeoAgentEval
Open weights
Earth-Bench V2
higher is better
Data table: Earth-Bench V2
GeoBenchX
higher is better
Data table: GeoBenchX
GS-QA2
higher is better
Data table: GS-QA2
GeoAgentBench
higher is better
Data table: GeoAgentBench
GeoAnalystBench
higher is better
Data table: GeoAnalystBench
ThinkGeo
higher is better
Data table: ThinkGeo
| Model | Developer | ThinkGeo |
|---|---|---|
| Claude Sonnet 5.5 (max), Claude Code | Anthropic | 85 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 74 |
| DeepSeek V4.1 Flash (max), Codex | DeepSeek | 70 |
OpenEarthAgent
higher is better
Data table: OpenEarthAgent
| Model | Developer | OpenEarthAgent |
|---|---|---|
| Claude Sonnet 5.5 (max), Claude Code | Anthropic | 100 |
| DeepSeek V4.1 Flash (max), Codex | DeepSeek | 94 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 88 |
OGD4All
higher is better
Data table: OGD4All
| Model | Developer | OGD4All |
|---|---|---|
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 97 |
| Claude Sonnet 5.5 (max), Claude Code | Anthropic | 97 |
| GLM-5.3 (max), Claude Code | Z.ai | 96 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 96 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 95 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 93 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 93 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 92 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 91 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 90 |
| MiniMax M3.1 Flash (max), Terminus-2 | MiniMax | 88 |
| DeepSeek V4 Flash (high), Claude Code | DeepSeek | 70 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 47 |
GeoNatureAgent
higher is better
Data table: GeoNatureAgent
| Model | Developer | GeoNatureAgent |
|---|---|---|
| MiniMax M3.1 Flash (max), Terminus-2 | MiniMax | 100 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 100 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 100 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 100 |
| GLM-5.3 (max), Claude Code | Z.ai | 100 |
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 100 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 100 |
| Claude Sonnet 5.5 (max), Claude Code | Anthropic | 100 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 92 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 88 |
| DeepSeek V4 Flash (high), Claude Code | DeepSeek | 71 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 64 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 0 |
MultiGlobeQA
higher is better
Data table: MultiGlobeQA
| Model | Developer | MultiGlobeQA |
|---|---|---|
| Claude Sonnet 5.5 (max), Claude Code | Anthropic | 78 |
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 77 |
| GLM-5.3 (max), Claude Code | Z.ai | 74 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 74 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 73 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 73 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 72 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 71 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 70 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 68 |
| MiniMax M3.1 Flash (max), Terminus-2 | MiniMax | 65 |
| DeepSeek V4 Flash (high), Claude Code | DeepSeek | 64 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 62 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 48 |
GeoAgentEval
higher is better
Data table: GeoAgentEval
| Model | Developer | GeoAgentEval |
|---|---|---|
| Claude Sonnet 5.5 (max), Claude Code | Anthropic | 100 |
| GLM-5.3-Flash (max), Claude Code | Z.ai | 96 |
| Kimi K3 (Reasoning), Kimi Code | Moonshot AI | 96 |
| Qwen 3.8 27B (xhigh), Terminus-2 | Alibaba (Qwen) | 92 |
| Nemotron 3 Ultra (high), Terminus-2 | NVIDIA | 92 |
| Step 5 Preview (Reasoning), Terminus-2 | StepFun | 91 |
| GLM-5.3 (max), Claude Code | Z.ai | 88 |
| Qwen 3.8 27B (xhigh), Qwen Code | Alibaba (Qwen) | 88 |
| Nemotron 3.5 Super VL 120B (Reasoning), Terminus-2 | NVIDIA | 80 |
| DeepSeek V4 Flash (high), Claude Code | DeepSeek | 80 |
| MiniMax M3.1 Flash (max), Terminus-2 | MiniMax | 67 |
| Mistral Large 4 (high), Terminus-2 | Mistral AI | 60 |
| Nemotron 3 Nano Omni 30B (Reasoning), Terminus-2 | NVIDIA | 48 |
| Nemotron 3.5 Lightning 30B (Reasoning), Terminus-2 | NVIDIA | 36 |
Cost
All
Cost per task
Average cost per agent task (USD) · Lower is better
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.
Data table: Cost per task
Open weights
Cost per task
Average cost per agent task (USD) · Lower is better
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.
Data table: Cost per task
Time
All
Time per task
Average minutes per agent task, at 100 output tokens/s · Lower is better
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.
Data table: Time per task
Open weights
Time per task
Average minutes per agent task, at 100 output tokens/s · Lower is better
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.
Data table: Time per task
Tokens
All
Output tokens per task
Average output tokens per agent task, reasoning included · Lower is better
How to read this chart
Output tokens per task. The average number of tokens the model wrote per attempt, its reasoning included, as reported by the provider.
Compare with care. Models use different tokenisers, so the same text is a different number of tokens for different models. Use token counts to see how much work each model did, and cost to compare spending.
Data table: Output tokens per task
Open weights
Output tokens per task
Average output tokens per agent task, reasoning included · Lower is better
How to read this chart
Output tokens per task. The average number of tokens the model wrote per attempt, its reasoning included, as reported by the provider.
Compare with care. Models use different tokenisers, so the same text is a different number of tokens for different models. Use token counts to see how much work each model did, and cost to compare spending.
Data table: Output tokens per task
All
Turns per task
Average agent turns per task · Lower is better
How to read this chart
What this metric means. A turn is one model reply plus the commands it ran.
Data table: Turns per task
Open weights
Turns per task
Average agent turns per task · Lower is better
How to read this chart
What this metric means. A turn is one model reply plus the commands it ran.