152 models ranked for agriculture and farming. Scored with bonuses for vision (crop/pest analysis), reasoning (yield prediction), JSON mode (structured data), function calling (IoT integration), and web search (weather data).
| # | Model | Score |
|---|---|---|
| 1 | GPT-5.4 ProOpenAI | 92 |
| 2 | GPT-5.4OpenAI | 92 |
| 3 | GPT-5.2 ProOpenAI | 91 |
| 4 | Claude Opus 4.6 (Fast)Anthropic | 90 |
| 5 | Claude Opus 4.6Anthropic | 90 |
| 6 | Grok 4.20xAI | 89 |
| 7 | GPT-5.3-CodexOpenAI | 89 |
| 8 | GPT-5 ProOpenAI | 89 |
| 9 | Gemini 3 Flash PreviewGoogle | 88 |
| 10 | Grok 4xAI | 88 |
| 11 | GPT-5.1-Codex-MaxOpenAI | 88 |
| 12 | GPT-5.2-CodexOpenAI | 90 |
| 13 | GPT-5.2OpenAI | 90 |
| 14 | o3 Deep ResearchOpenAI | 87 |
| 15 | o3 ProOpenAI | 87 |
| 16 | o3OpenAI | 87 |
| 17 | GPT-5 CodexOpenAI | 88 |
| 18 | GPT-5OpenAI | 88 |
| 19 | Claude Sonnet 4.6Anthropic | 85 |
| 20 | Claude Opus 4.5Anthropic | 85 |
| 21 | GPT-5.1OpenAI | 87 |
| 22 | GPT-5.1-CodexOpenAI | 87 |
| 23 | GPT-5.1-Codex-MiniOpenAI | 87 |
| 24 | Grok 4.20 Multi-AgentxAI | 88 |
| 25 | Gemini 2.5 ProGoogle | 84 |
| 26 | Gemini 2.5 Pro Preview 06-05Google | 84 |
| 27 | Gemini 2.5 Pro Preview 05-06Google | 84 |
| 28 | GPT-5.3 ChatOpenAI | 87 |
| 29 | Claude Sonnet 4.5Anthropic | 82 |
| 30 | GPT-5.1 ChatOpenAI | 87 |
Vision models identify crop diseases, nutrient deficiencies, and growth stages from field images. Upload drone or satellite imagery for automated field analysis.
Identify insects, weeds, and diseases from photos. Models recommend treatment options with dosage, timing, and organic alternatives.
Analyze weather, soil data, and historical yields to forecast harvests. JSON mode produces structured predictions for farm management systems.
Optimize irrigation schedules, fertilizer application, and planting density. Function calling integrates with IoT sensors and weather APIs for real-time decisions.
Vision-capable models analyze satellite imagery, drone photos, and soil maps to identify crop stress, pest damage, and irrigation issues. Models with web search can pull real-time weather data and market prices for yield optimization.
Self-hostable open-source models can run on local hardware without internet. Smaller models (7B-13B parameters) work on edge devices, though they trade capability for offline access. Consider models ranked high for open-source flexibility.
Models with reasoning capabilities can analyze historical yield data, weather patterns, and market trends to provide forecasts. However, these are estimates - combine AI insights with agronomist expertise and local knowledge for best results.
For analyzing multi-page soil reports, seasonal data, or regulatory documents, 128K+ context windows are essential. Smaller context models may miss connections between early-season conditions and harvest projections.